No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
COLREG-RRT: A RRT-based COLREGS-Compliant Motion Planner for Surface Vehicle Navigation
Abstract
Motion planning for Autonomous Surface Vehicles (ASVs) is challenging since surface vessels are nonlinear under-actuated kinodynamic systems with often large inertia. Thus, ASV planners must identify long-term trajectories in order to avoid guiding the ASV into inevitable collision states. Furthermore, maritime vessels are required to follow COLlision REGulationS (COLREGS), which dictates collision avoidance patterns. Current state of the art methods are based on Model Predictive Control (MPC) and assume other vessels move at constant velocities without consideration of COLREGS. In this paper, we propose COLREG-RRT, a RRT-based planner capable of identifying long-term, COLREGS-compliant trajectories with a high navigation success rate. This is achieved by conducting joint forward simulations of both the ASV and the other vessels during RRT growth in order to anticipate future collisions. The COLREGS-compliance is enforced by constructing virtual obstacles that inhibit tree growth. We demonstrate COLREGS-compliance in single-ship encounters and compare against two state of the art methods in multi-ship encounters with up to 20 other vessels. Experiments indicate that COLREG-RRT has a 32% higher success rate and is real-time capable in the most difficult environment tested. Additionally, COLREG-RRT identifies longer trajectories, as compared to MPC. This property aids with collision avoidance with other ships.
... It generates a graph with random nodes and searches for a path across it. Hao et al. introduced an approach for autonomous surface vehicles with COLlision REGulationS and RRT (COLREG-RRT) to produce trajectory in dynamic environment [22]. Due to large area search, COLREG-RRT becomes computationally expensive to search the feasible path for autonomous ship due to dynamically changing environment. ...
... It computes collision risk with a random tree search. However, the path obtained with RRT-COLREG contains a lack of smoothness, and it prefers the short length path over the safety-ensured path, as shown in Fig. 9 [22]. However, these three problems are all interconnected in the realtime path planning of unmanned ships. ...
... MPAPF and APF require the modeling of obstacles as special geometric objects (i.e., spheres) and, therefore, were unable to provide a global path. The path obtained through RRT-COLREG [22] is colored green, and a reddotted line denotes the global path achieved with EGA [46]. Although RRT and EGA work efficiently in finding a global path, their paths cross between islands that are separated by a narrow shallow water line, which is not safe for autonomous ships due to the presence of unexpected underwater threats as shown in Fig. 9. ...
... 2) General ship autonomy: A larger body of work considers the broader task of autonomous ship operation in non-ice environments [34]- [42]. This includes urban waterways [40], harbors [39], [43], and shorelines [34]. ...
... 2) General ship autonomy: A larger body of work considers the broader task of autonomous ship operation in non-ice environments [34]- [42]. This includes urban waterways [40], harbors [39], [43], and shorelines [34]. In [40], a recedinghorizon path planner is proposed for an autonomous ship operating in constrained urban environments. ...
Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach.
... Existing works on ASV path planning primarily focused on finding a collision-free path [4], [5], [6], and are not easily generalizable to environments with high ice concentrations, where collision-free paths are typically non-existent, as illustrated in Fig. 1. While the works in [3] and [7] address this challenge, they do not consider ice motion during planning. ...
... While these works demonstrated good performance, local planners are required to achieve full ship autonomy. In ASV local planning, however, most work aims to compute a collision-free path [4], [5], [6], [11]. ...
Autonomous navigation in ice-covered waters poses significant challenges due to the frequent lack of viable collision-free trajectories. When complete obstacle avoidance is infeasible, it becomes imperative for the navigation strategy to minimize collisions. Additionally, the dynamic nature of ice, which moves in response to ship maneuvers, complicates the path planning process. To address these challenges, we propose a novel deep learning model to estimate the coarse dynamics of ice movements triggered by ship actions through occupancy estimation. To ensure real-time applicability, we propose a novel approach that caches intermediate prediction results and seamlessly integrates the predictive model into a graph search planner. We evaluate the proposed planner both in simulation and in a physical testbed against existing approaches and show that our planner significantly reduces collisions with ice when compared to the state-of-the-art. Codes and demos of this work are available at https://github.com/IvanIZ/predictive-asv-planner.
... In crowd waters, where the USV may successively encounter approaching vessels, the USV should frequently take the COLAV maneuvers after the USV encounters the approaching vessels, which increases the USV's actual sailing distance. This problem can be alleviated by making COLAV decisions considering all the vessels in crowded waters by path replanning [22], [23], [24], [25], [26], [27], [28], [29], [30], [31] such that the USV could take COLAV maneuvers before the USV encounters the approaching vessels. The path replanning methods consist of the A * method [22], [23], the rapid random tree method [24], [25], the probabilistic roadmap method [26], [27], the evolutionary method [28], [29], and the numerical optimization method [30], [31]. ...
... This problem can be alleviated by making COLAV decisions considering all the vessels in crowded waters by path replanning [22], [23], [24], [25], [26], [27], [28], [29], [30], [31] such that the USV could take COLAV maneuvers before the USV encounters the approaching vessels. The path replanning methods consist of the A * method [22], [23], the rapid random tree method [24], [25], the probabilistic roadmap method [26], [27], the evolutionary method [28], [29], and the numerical optimization method [30], [31]. In crowded waters, the approaching vessels may take COLAV maneuvers frequently, which the above existing path replanning methods cannot predict. ...
In crowded waters, multiple vessel encounter situations increase the collision risks (CRs) of unmanned surface vehicles (USVs) and hence the frequent collision avoidance (COLAV) maneuvers of USVs increase their actual sailing distances. This paper innovatively proposes a risk-prediction-based deep reinforcement learning (RPDRL) approach for the integrated intelligent guidance and motion control of USVs with anticipatory COLAV decision-making. The data sizes of detected vessels’ motion states are different due to the uncertainties in the number of vessels detected by the navigation systems of a USV. To address this problem, these data are, for the first time, converted into the corresponding same-sized raster data as the states in the RPDRL approach. A new CR assessment model of the USV collisions with all the detected vessels is built to calculate the rewards in the RPDRL approach. Furthermore, actor and critic deep convolutional neural networks are created to make the anticipatory COLAV decisions which are the engine command and rudder command. Simulations and simulation comparison results on a USV demonstrate that the USV sails along a shorter path with a lower CR under the anticipatory COLAV decisions from our proposed RPDRL approach compared with a velocity obstacle method and a model predictive control method, and hence the economy and safety of USVs’ autonomous navigation are enhanced.
... The most relevant maritime traffic rules for collision avoidance are specified in the COLREGS [17]. Often, these traffic rules are indirectly integrated in the motion planning approach, e.g., through geometric thresholds [18]- [23], virtual obstacles [24], or cost functions [6], [25]- [29]. However, these approaches usually do not capture the temporal properties of collision avoidance rules, and the implemented interpretation of the COLREGS is often intransparent. ...
... for a acc ∈ A acc do 20: [24][25]. This has the effect that the vessel does not switch between different accelerations during the maneuver. ...
For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to formalize such traffic rules. Still, temporal logic rules often result in constraints that are hard to solve using optimization-based motion planners. Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles. However, vanilla RL algorithms are based on random exploration and do not automatically comply with traffic rules. Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL. Specifically, we consider the application of vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). To efficiently synthesize rule-compliant actions, we combine predicates based on set-based prediction with a statechart representing our formalized rules and their priorities. Action masking then restricts the RL agent to this set of verified rule-compliant actions. In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides while achieving a high goal-reaching rate during training and deployment. In contrast, vanilla and traffic rule-informed RL agents frequently violate traffic rules and collide even after training.
... This technology aims to forecast the future paths of ships, helping detect potential collision risks at an early stage and enabling timely evasive actions. It also assists captains or automated navigation systems in making optimal navigational decisions, such as course adjustments and speed regulation, to respond swiftly and minimize collision risks [3]. Moreover, accurate and prompt trajectory prediction can reduce economic losses from collisions, enhance navigational efficiency, and save fuel. ...
... Y t n+1 = F(P t 1 :t n ) (3) Within this framework, lon represents longitude, lat denotes latitude, v signifies the ship's speed over ground, and w encapsulates the vessel's course over ground. The schematic illustration of ship trajectory prediction, as depicted in Figure 1, entails designating the vessel's state information from time 1 t to n t as the input for the prediction model. ...
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.
... For example, collision avoidance strategies have been applied to methods such as D* [58], RRT [59], [60], optimal path based on Genetic Algorithm (GA) [61], and international regulations for preventing collisions at sea (COLREGs) [62], [63]. Additionally, combination of these methods with other traditional PP techniques have been explored in various studies [64]- [66]. ...
Obstacle avoidance (OA) is necessary for any path planning in outdoor environment to prevent any collision with the obstacles in natural environment. In this paper, a quadrotor navigates using Active Simultaneously Localization and Mapping (ASLAM) in GNSS-denied outdoor environment. In ASLAM, the quadrotor path is defined using real-time Observability Based Path Planning (OBPP) method, autonomously. To prepare using of the OBPP in outdoor environment, it is necessary to add the ability of OA to it. So, the OA-OBPP method is introduced which defines the path based on terrestrial landmarks while preventing any collision with the obstacles. This approach is developed by redefining a dataset of in range landmarks while all of the landmark in the vicinity of the obstacles are removed from the in-range landmarks dataset. To evaluate the performance of the proposed method, simulations of the OA-OBPP algorithm are conducted for a simplified 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations evaluate the efficiency, accuracy and robustness of the proposed method. Results across various scenarios show that the method effectively avoids collisions with obstacles while simultaneously determining a path to the goal. Additionally, a comparison of the position estimation RMSE with Monte Carlo PP highlights the accuracy of the OA-OBPP. The robustness of the method, tested with varying initial positions, demonstrates its success in real-time path planning (PP) from any starting point to the destination without collisions. The results confirm that the OA-OBPP enhances the robot's capability to perform real-time, autonomous, and robust path planning in outdoor environments, even in the absence of GNSS signals, through visual navigation.
... Besides, APF may also have unfavourable situations, such as local optimisation, force equilibrium, and repulsive force greater than attractive force. An improved RRT algorithm incorporating COLREGs is proposed by Chiang and Tapia (2018). The algorithm predicts future collisions by co-simulating other vessels during the growth of the RRT. ...
... To incorporate dynamic obstacle collision avoidance, one can employ a joint simulator as in [37] in the steering together with adding virtual obstacles for striving towards COLREG compliance, or utilize biased sampling methods as in e.g. [38]. ...
Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch’s t-test at a significance level of , indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.
... Heuristic search methods like Dijkstra [3,4] and A* [5] can efficiently find optimal paths, but they struggle with heading adjustments and dynamic obstacles, making it difficult to apply COLREGs for accurate collision avoidance [6][7][8]. The RRT algorithm [9] is adaptable but faces challenges with randomness and handling dynamic obstacles [10,11]. In contrast, the artificial potential field (APF) method is efficient, flexible, and integrates well with COLREGs, making it more effective for real-time collision avoidance in complex environments [12]. ...
In recent years, unmanned surface vehicles (USVs) have gained increasing attention in industry due to their efficiency and versatility in marine operations. Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. Not to mention that actual navigations may involve other dynamic traffic participants. In this work, an improved APF algorithm integrating the boundary potential field method and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. By incorporating the boundary potential field method, this novel approach effectively reduces the computational burden caused by clusters of land obstacles in complex environments, significantly improving computational efficiency. Furthermore, the APF method is refined to ensure the algorithm strictly adheres to COLREGs in head-on, overtaking, and crossing encounters, generating smooth and safe collision avoidance paths. The proposed method was tested in numerous complex scenarios derived from electronic navigational charts. The simulation results demonstrated the robustness and efficiency of the proposed algorithm for collision avoidance within complex maritime environments, providing reliable technical support for autonomous obstacle avoidance in dynamic ocean conditions.
... II. RELATED WORKS Multi-vessel collision avoidance problems have been widely studied with the consideration of COLREGs. Naeem et al. [16] and Chiang et al. [17] developed COLREGs-compliant algorithms based on Artificial Potential Field (APF) and rapidlyexploring random tree (RRT) respectively by generating virtual obstacles around other vessels to prevent COLREGs-violating actions; Cho et al. [3] developed a rule-based system that specifies the roles of vessels in multi-vessel encounters and utilizes the probabilistic velocity obstacle method for collision avoidance. However, system performance of these works is only illustrated in highly simplified simulations without modeling of environmental disturbances and perceptual error. ...
With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. However, it is still challenging for ASVs to perform sensor-based autonomous navigation in obstacle-filled and congested waterways, where perception errors, closely gathered vehicles and limited maneuvering space near buoys may cause difficulties in following the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). To address these issues, we propose a novel Distributional Reinforcement Learning based navigation system that can work with onboard LiDAR and odometry sensors to generate arbitrary thrust commands in continuous action space. Comprehensive evaluations of the proposed system in high-fidelity Gazebo simulations show its ability to decide whether to follow COLREGs or take other beneficial actions based on the scenarios encountered, offering superior performance in navigation safety and efficiency compared to systems using state-of-the-art Distributional RL, non-Distributional RL and classical methods.
... (weather routing), and the local or reactive level, in which subparts of the path are re-planned online in reaction to changes in the environment detected by sensors, such as moving or unexpected obstacles. The scientific literature proposed various approaches to reactive collision avoidance of marine vessels, including A* (Seo et al., 2023), Dijkstra's algorithm (Singh et al., 2017(Singh et al., , 2018, visibility graphs (D'Amato et al., 2021), rapidly-exploring random trees (Chiang and Tapia, 2018;Zaccone and Martelli, 2020;Enevoldsen et al., 2021), Artificial Potential Field methods (Zhu et al., 2022;Li et al., 2021), Randomly-Exploring Random Trees (Zaccone and Martelli, 2020), Dynamic Programming (Zaccone, 2024), and various population-based heuristics (Ito et al., 1999;Kang et al., 2018;Ning et al., 2020;Gao et al., 2023). ...
... Sampling-based algorithms are suitable for path planning in high-dimensional space, which has the characteristics of convenience and efficiency, easy to constrain and probabilistic completeness. Sampling-based algorithms are mainly categorized into probabilistic roadmap (PRM) [12], and rapid search random tree (RRT) [13]. Among them, the RRT algorithm is favored for its fast search speed, powerful exploration ability, and probabilistic completeness. ...
An improved RRT* algorithm, referred to as the AGP-RRT* algorithm, is proposed to address the problems of poor directionality, long generated paths, and slow convergence speed in multi-axis robotic arm path planning. First, an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency. Second, a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm. Finally, the planning path is processed by pruning, removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm. Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform, the results show that the AGP-RRT* algorithm reduces 87.34% in terms of the average running time and 40.39% in terms of the average path cost; Meanwhile, under two sets of complex environments A and B, the average running time of the AGP-RRT* algorithm is shortened by 94.56% vs. 95.37%, and the average path cost is reduced by 55.28% vs. 47.82%, which proves the effectiveness of the AGP-RRT* algorithm in improving the efficiency of multi-axis robotic arm path planning.
... The shortest computational latency is highly desirable for real-time implementation. [11] integrated the dynamic model with a planning method, rapidly exploring random tree, to compute the collision-free trajectory, where comparisons with the MPC method provide further insight into the performance and capabilities of the approach. [12] proposed a hybrid method, fast marching square and velocity obstacle (VO), for global path planning to generate the optimal trajectory. ...
In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.
... International regulations for preventing collisions at sea (COLREGs) serve as the international standards for avoiding collisions in maritime environments. Several papers are utilized these rules independently [24], [25] and some in combination with other traditional MP methods [26], [27], [28]. ...
This paper introduces a collision avoidance (CA) method integrated into the Observability Based Motion Planning (OBMP) framework. OBMP facilitates autonomous robot navigation in GNSS-denied outdoor environments, leveraging the concept of observability degree. The CA-OBMP method proposed here is an extension of the OBMP algorithm, which constructs paths based on terrestrial landmarks considering the obstacles. The CA approach is developed by redefining a dataset of in range landmarks while all of the landmark in vicinity of the obstacles are removed from the dataset. To evaluate the performance of the proposed method, simulations of the CA-OBMP algorithm are conducted for a 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations encompass various arrangements of obstacles and diverse initial positions to assess the efficacy of CA method in different scenarios.
... This requirement is essential in scenarios where autonomous systems interact with human-controlled systems [13]. The scientific literature proposed various approaches to collision avoidance of marine vessels, including A* [14,15], Dijkstra's algorithm [16,17], visibility graphs [18], rapidly-exploring random trees [19][20][21], and various population-based heuristics [22][23][24][25]. ...
The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, Dynamic Programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. Through case studies, we demonstrate the effectiveness of the proposed approach in navigating complex scenarios, contributing to the future of autonomous maritime navigation. Through case studies, we show the efficacy of our approach in navigating complex scenarios.
... In the robotics field, path planning is commonly divided into two levels [14,15], the off-line or global level, in which the path is determined based on a priori known information, such as information about fixed obstacles or weather forecasts (weather routing), and the local or reactive level, in which subparts of the path are re-planned online in reaction to changes in the environment detected by sensors, such as moving or unexpected obstacles. The scientific literature has proposed various approaches to the reactive collision avoidance of marine vessels, including A* [16], Dijkstra's algorithm [17,18], visibility graphs [19], rapidly exploring random trees [20][21][22], Artificial Potential Field methods [23,24], and various population-based heuristics [25][26][27][28]. ...
The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, dynamic programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for the collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. We put the proposed algorithms to the test in realistic navigation scenarios and also develop an extensive test on a large set of randomly generated scenarios, comparing them with the RRT* algorithm using performance metrics proposed in the literature. The results show the potential benefits of an autonomous navigation or decision support framework.
... To incorporate dynamic obstacle collision avoidance, one can employ a joint simulator as in Chiang and Tapia (2018) in the steering together with adding virtual obstacles for striving towards COLREG compliance, or utilize biased sampling methods as in e.g. (Enevoldsen, Reinartz and Galeazzi, 2021). ...
Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query non-holonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators.
Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch's t-test at a significance level of 𝛼 = 0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.
... Main Features [48] Two-level dynamic obstacle avoidance algorithm • Combination of VO algorithm and improved APF • Multi-obstacles [119] Generalized velocity obstacle (GVO) algorithm • Works properly in various maritime environments • More trustworthy and appropriate for preventing ship collisions at close range • Offer ships a minimal number of required evasive actions that comply with the rules [120] Fast marching square algorithm • Multiple USVs • Practical safe navigation on genuine nautical chart [55] COLREG-RRT • Identifies longer trajectories • COLREGS-compliant trajectories [121] Optimized particle swarm (PSO) algorithm in a modified form • USV route control that also takes currents into account • Categorized as global route planning • Dynamic crowding distance [122] Genetic algorithm • Optimal route with the least amount of trip time • Considers environmental loads • Random initialization ...
This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.
... Rapidly exploring Random Trees (RRTs) was used in a sampling-based planning algorithm for COLREGS-compliant dynamic and static collision avoidance in (Chiang and Tapia, 2018). The planner used a joint simulator for predicting both the own-ship and dynamic obstacle motion, with potential fields being used in the joint prediction to ensure collision-free trajectories. ...
In this article, full-scale experiments with a dynamic obstacle intention-aware Collision Avoidance System (CAS) are presented. The CAS consists of the Probabilistic Scenario-Based Model Predictive Control (PSB-MPC) for trajectory planning, dynamic obstacle avoidance, and antigrounding, with a Dynamic Bayesian Network (DBN) used for inferring obstacle intentions online. The novelty of this article lies in the utilization of intention information in deliberate collision-free planning. By inferring multiple different intention states on how and if nearby obstacles adhere to the COLREGS, the PSB-MPC can plan COLREGS-compliant avoidance maneuvers when possible, taking into account its awareness of the situation. The experiments put emphasis on hazardous situations where this intention information is both useful and necessary in order to avoid high collision risk. To the authors’ knowledge, the work is the first field experimental validation of such a probabilistic intention-aware CAS with consideration of multiple intention states. The experimental results demonstrate the validity of the proposed CAS scheme, with adherence to the traffic rules (COLREGS) 7, 8 and 13–17 in a diverse set of situations. The strengths and weaknesses of the proposed CAS are also discussed, giving insights that can be useful for researchers and practitioners in the field. Here, challenges related to detecting obstacle maneuvers and making the intention inference more robust to noise should be addressed as future work to make the scheme better suited for general usage on ships engaged in real traffic.
... Co-citation links are illustrated in Figure 7. Additionally, the articles introduced in Table 2 are further described. Three concept papers Johansen et al. 2016;Chiang and Tapia 2018) are related to collision avoidance strategies that comply with COLREGs. A generalised velocity obstacle algorithm is proposed for collision prevention of both unmanned and manned ships ). ...
The maritime industry is following the trend of increased autonomy and digitalisation applied in aviation, automotive, military, and chemical industries. Maritime autonomous and unmanned vehicles have received significant attention recently, both from academia and industry. This paper investigates research into the progress of the development of autonomous and unmanned shipping by employing bibliometric analysis tools VOSviewer and CiteSpace, to present a comprehensive picture of this emerging field of research. Bibliometrics is applied to investigate the collected data sample from Scopus related to predefined keywords. Bibliometric tools assist review by network visualisation, clustering, and metrics. Therefore, this paper presents an analysis aiming at (1) increasing the understanding of the structure and contents of the academic field of autonomous and unmanned shipping; (2) determining and mapping scientific networks in this field; (3) analysing and visualising major divisions within the field; (4) identifying research needs and future research directions in the field. Through clusters generated by bibliometric tools, research divisions are identified and discussed. Furthermore, potential research directions are outlined.
... In recent years, it has already been used in multi-ship collision avoidance (Liu et al., 2018;Sun et al., 2021). The basic principle of the algorithm is to deduce the future output of a system through its current state, future inputs and control quantities, on the basis of the model-based rolling time-domain optimisation control method, which aims to achieve control by solving constrained optimisation problems (Chiang and Tapia, 2018). This rolling optimisation strategy ensures that the input at each step is the optimum value calculated based on the current state, so the algorithm can effectively overcome the effects of model inaccuracies and time-varying factors, and has strong robustness. ...
Ship autonomous collision avoidance has attracted increasing attention in recent years. However, more attention is paid to the scenario in which the target ship keeps its course and speed. Less attention is paid to the development of a collision avoidance decision-making system under the uncertainty of the target ship's movement in a complex multi-ship encounter situation. Based on the idea of model predictive control (MPC), this paper proposes an autonomous ship collision avoidance decision-making system (CADMS) suitable for the uncertainty of ship motion. The CADMS includes four modules: collision risk analysis module, control and execution module, ship trajectory prediction module and collision avoidance decision-making model. The proposed model can be implemented in the collision avoidance decision-making system for safe navigation or it can be included in the ship autonomous navigation process. The decision-making model is achieved from a risk identification-motion prediction-ship control-scheme implementation perspective, and the dynamic and uncertainty features of the ship action (i.e., alter course or change speed) are considered in the modelling process. The real-time rolling update of ship collision avoidance decisions is realised based on the time series rolling method. Four scenarios are designed to demonstrate the collision avoidance decision-making system's performance. The results show that the proposed collision avoidance decision-making system is a reasonable and effective system for collision avoidance, particularly in multi-ship encounter situations of target ships suddenly altering course or change speed.
... Based on the COLREGs, this paper classifies four encountering situations of two vessels: the head-on situation, the left crossing situation, the right crossing situation, and the overtaking situation are considered [32,33]. More details of the encountering situations can be found in rules 13, 14, and 15 of the COLREGs [34,35], which are depicted in Figure 3. Based on the DCCR analysis, a switch control strategy is employed. A collision-avoidance controller (CAC) and a prescribed performance controller are designed, and the stability of the control system is proven. ...
This manuscript investigates an output feedback-tracking control problem of a dynamically positioned vessel with an input constraint. The vessel is exposed to model uncertainty and external disturbances. Compared with the existing results, the primary contribution is to develop a switch-control strategy for achieving collision avoidance and performance constraints by using an extended state observer (ESO), a collision-avoidance controller (CAC), a prescribed performance controller, and an auxiliary dynamic system (ADS). The switch control strategy combined two different controllers, and an extended state observer (ESO) is designed. The ESO is employed to recover velocity information as well as unknown model uncertainty and external disturbances. A collision-risk-analysis module is introduced to evaluate whether there exists a risk of collision avoidance. Based on the analysis, the CASC can choose between a CAC and a PPC. An ADS is constructed to handle the input constraints. The CAC is employed by using an artificial potential function, the ADS, and the ESO. The PPC is designed based on an error constraint function, the ADS, and the ESO. The stability of the closed-loop control system is analyzed based on the Lyapunov direct method. Simulations prove the effectiveness of the presented control strategy.
... In recent years, there has been an increase in the amount of studies discussing the integration of the COLREGs, decision-making functions, and guidance systems. These researchers demonstrated the concepts of combining static optimized collision avoidance paths with PI or PID linear controllers [7,8]. However, ship-ship dynamics were not considered in the design procedures; hence, they are only workable at certain operating conditions and are not applicable in real applications. ...
An intelligent fuzzy-based control system that consists of several subsystems—a fuzzy collision evaluator, a fuzzy collision avoidance acting timing indicator, a collision-free trajectory generator, and a nonlinear adaptive fuzzy robust control law—is proposed for the collision-free condition and trajectory tracking of unmanned surface vessels (USVs). For the purpose of ensuring that controlled USVs are capable of executing tasks in an actual ocean environment that is full of randomly encountered ships under collision-free conditions, the real-time decision making and the desired trajectory arrangements of this proposed control system were developed by following the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs). From the simulation results, several promising properties were demonstrated: (1) robustness with respect to modeling uncertainties and ocean environmental disturbances, (2) a precise trajectory tracking ability, and (3) sailing collision avoidance was shown by this proposed system for controlled USVs.
With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. However, it is still challenging for ASVs to perform sensor-based autonomous navigation in obstacle-filled and congested waterways, where perception errors, closely gathered vehicles and limited maneuvering space near buoys may cause difficulties in following the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). To address these issues, we propose a novel Distributional Reinforcement Learning based navigation system that can work with onboard LiDAR and odometry sensors to generate arbitrary thrust commands in continuous action space. Comprehensive evaluations of the proposed system in high-fidelity Gazebo simulations show its ability to decide whether to follow COLREGs or take other beneficial actions based on the scenarios encountered, offering superior performance in navigation safety and efficiency compared to systems using state-of-the-art Distributional RL, non-Distributional RL and classical methods.
Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.
Unmanned surface vessels (USVs) are generally subjected to wind force, wave and undercurrent action. Some unidentified objects such as underwater obstacles and submerged reefs should be monitored and evaded with converged communication, autonomous control and network system so as to achieve autonomous and automatic avoidance of danger and real-time control of flight path. Next, based on performing interference mechanics analysis on the USVs under the disturbance of wind force and wave on water surface, the relationship parameters between the bow angle, the deviation angle, safety evaluation function and the external force were obtained, and the comprehensive information of water surface environment was evaluated in combination with the dynamic window approach (DWA) algorithm. Further, the related model was established for simulation by using the simulation software MATLAB, and both reliability and stability of the modified DWA algorithm were designed and examined. Accordingly, the modified DWA algorithm can enhance the safety of the USVs in autonomous path planning and achieve rapid obstacle avoidance and autonomous path planning of the USVs. Our simulation revealed that the modified DWA algorithm can safely and rapidly correct the bow angle and the deviation angle, optimize the path trajectory and reduce the risk of roll-over of the USVs under sudden external force.
The traditional A* algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles (USVs). In addition, the path planned presents numerous redundant inflection waypoints, and the security is low, which is not conducive to the control of USV and also affects navigation safety. In this paper, these problems were addressed through the following improvements. First, the path search angle and security were comprehensively considered, and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed. The A* algorithm search neighborhood was expanded from 3×3 to 5×5, and safe nodes were screened out for extension via the node security expansion strategy. This algorithm can also optimize path search angles while improving path security. Second, the distance from the current node to the target node was introduced into the heuristic function. The efficiency of the A* algorithm was improved, and the path was smoothed using the Floyd algorithm. For the dynamic adjustment of the weight to improve the efficiency of DWA, the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach (DWA) algorithm. Finally, combined with the local target point selection strategy, the optimized DWA algorithm was performed for local path planning. The experimental results show the smooth and safe path planned by the fusion algorithm, which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.
In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.
This article addresses autonomous collision avoidance in restricted waterways in compliance with maritime navigation rules. Since waterways may have diverse shapes, it is not straightforward to design a generic approach that can be applied to all types of waterways. In this article, we propose a shape-invariant coordinate system and a systematic collision avoidance procedure that complies with maritime navigation rules. The waterway space is defined using the coordinates in the along-track and cross-track directions to efficiently represent various types of waterway shapes. An automatic collision avoidance algorithm is designed and applied to the transformed coordinate system, which additionally takes into account the compliance with maritime traffic rules in restricted waterways. The performance of the proposed approach is evaluated in diverse types of waterways by performing Monte Carlo simulations, and the simulation results are presented and discussed.
With growing worldwide interest in commercial, scientific, and military issues associated with both oceans and shallow waters, there has been a corresponding growth in demand for the development of unmanned surface vehicles (USVs) with advanced guidance, navigation and control (GNC) capabilities. This paper presents a comprehensive literature review of recent progress in USVs development. The paper first provides an overview of both historical and recent USVs development, along with some fundamental definitions. Next, existing USVs GNC approaches are outlined and classified according to various criteria, such as their applications, methodologies, and challenges. Finally, more general challenges and future directions of USVs towards more practical GNC capabilities are highlighted.
This paper presents a novel approach for robot navigation in crowded urban environments where people and objects are moving simultaneously while a robot is navigating. Avoiding moving obstacles at their corresponding precise moment motivates the use of a robotic planner satisfying both dynamic and nonholonomic constraints, also referred as kynodynamic constraints. We present a proactive navigation approach with respect its environment, in the sense that the robot calculates the reaction produced by its actions and provides the minimum impact on nearby pedestrians. As a consequence, the proposed planner integrates seamlessly planning and prediction and calculates a complete motion prediction of the scene for each robot propagation. Making use of the Extended Social Force Model (ESFM) allows an enormous simplification for both the prediction model and the planning system under differential constraints. Simulations and real experiments have been carried out to demonstrate the success of the proactive kinodynamic planner.
We present a model-predictive trajectory planning algorithm for following a target boat by an autonomous unmanned surface vehicle (USV) in an environment with static obstacle regions and civilian boats. The planner developed in this work is capable of making a balanced trade-off among the following, possibly conflicting criteria: the risk of losing the target boat, trajectory length, risk of collision with obstacles, violation of the Coast Guard Collision Regulations (COLREGs), also known as “rules of the road”, and execution of avoidance maneuvers against vessels that do not follow the rules. The planner addresses these criteria by combining a search for a dynamically feasible trajectory to a suitable pose behind the target boat in 4D state space, forming a time-extended lattice, and reactive planning that tracks this trajectory using control actions that respect the USV dynamics and are compliant with COLREGs. The reactive part of the planner represents a generalization of the velocity obstacles paradigm by computing obstacles in the control space using a system-identified, dynamic model of the USV as well as worst-case and probabilistic predictive motion models of other vessels. We present simulation and experimental results using an autonomous unmanned surface vehicle platform and a human-driven vessel to demonstrate that the planner is capable of fulfilling the above mentioned criteria.
This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).
An autonomous navigation algorithm for marine vehicles is proposed in this paper us-ing fuzzy logic under COLREG guidelines. The VFF (Virtual Force Field) method, which is widely used in the field of mobile robotics, is modified for application to the autonomous navi-gation of marine vehicles. This Modified Virtual Force Field (MVFF) method can be used in ei-ther track-keeping or collision avoidance modes. Moreover, the operator can select a track-keeping pattern mode in the proposed algorithm. The collision avoidance algorithm has the abil-ity to handle static and/or moving obstacles. The fuzzy expert rules are designed deliberately un-der COLREG guidelines. An extensive simulation study is used to verify the proposed method.
This paper addresses trajectory planning in a dynamic workspace, i.e. motion planning for a robot subject to dynamic constraints and moving in a workspace with moving obstacles. First the novel concept of state-time space is introduced, i.e. the state space of the robot augmented of
the time dimension. Like configuration space which is a tool to formulate path-planning problems, state-time space is a tool to formulate trajectory planning in dynamic workspace problems. It permits us to study the different aspects of dynamic trajectory planning, i.e. moving obstacles and
dynamic constraints, in a unified way. Then this new concept is applied to the case of a car-like robot subject to dynamic constraints and moving along a given path on a dynamic planar workspace. A near-time-optimal approach that searches the solution trajectory over a restricted set of canonical
trajectories is presented. These canonical trajectories are defined as having discrete and piecewise constant acceleration. Under these assumptions, it is possible to transform the problem of finding the time-optimal canonical trajectory to finding the shortest path in a directed graph embedded
in the state-time space.
This work presents a new method for motion planning under differential constraints by incorporating a numerically solved discretized representation of reachable state space for faster state sampling and nearest neighbor searching. The reachable state space is solved for offline and stored into a “reachable map” which can be efficiently applied in online planning. State sampling is performed only over states encompassed by the reachable map to reduce the number of unsuccessful motion validity checking queries. The nearest neighbor distance function is revised such that only reachable states are considered, with states which are unreachable or only reachable beyond a designated time horizon disregarded. This method is generalized for application to any control system, and thus can be used for vehicle models where analytical solutions cannot be found. Greater improvement is expected for more constrained systems where motion checking cost is relatively high. Simulation results are discussed for case studies on a holonomic model and a Dubins car model, both with maximum speed limitation and time included as a dimension in the configuration space, where planning speed (measured by tree growth rate) can be improved through reachability guidance in each system by at least a factor of 3 and 9 respectively.
This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.
The distance metric is a key component in RRT-based motion planning that deeply affects coverage of the state space, path quality and planning time. With the goal to speed up planning time, we introduce a learning approach to approximate the distance metric for RRT-based planners. By exploiting a novel steer function which solves the two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking performance. In an extensive analysis we compare our approach to an Euclidean distance baseline, consider four alternative regression models and study the impact of domain-specific feature expansion. The learning approach is shown to be faster in planning time by several factors at negligible loss of path quality.
The paper proposes a method that uses topological information to guide the path search in any 2D workspace. Our method generates topological paths taking into consideration the constraints of the workspace. Then, a path planner based on the A*, called Homotopic A* (HA*), guides the path search in the workspace using the generated topological paths. Simulated and real results with an Autonomous Underwater Vehicle (AUV) are presented showing the feasibility of the proposal. Comparison with well-known path planning algorithms has also been included.
We present Kinodynamic RRT*, an incremental sampling-based approach for asymptotically optimal motion planning for robots with linear dynamics. Our approach extends RRT*, which was introduced for holonomic robots [10], by using a fixed-final-state-free-final-time controller that optimally connects any pair of states, where the cost function is expressed as a trade-off between the duration of a trajectory and the expended control effort. Our approach generalizes earlier work on RRT* for kinodynamic systems, as it guarantees asymptotic optimality for any system with controllable linear dynamics, in state spaces of any dimension. In addition, we show that for the rich subclass of systems with a nilpotent dynamics matrix, closed-form solutions for optimal trajectories can be derived, which keeps the computational overhead of our algorithm compared to traditional RRT* at a minimum. We demonstrate the potential of our approach by computing asymptotically optimal trajectories in three challenging motion planning scenarios: (i) a planar robot with a 4-D state space and double integrator dynamics, (ii) an aerial vehicle with a 10-D state space and linearized quadrotor dynamics, and (iii) a car-like robot with a 5-D state space and non-linear dynamics.
This paper presents an autonomous motion planning algorithm for unmanned surface vehicles (USVs) to navigate safely in dynamic, cluttered environments. The algorithm not only addresses hazard avoidance (HA) for stationary and moving hazards, but also applies the International Regulations for Preventing Collisions at Sea (known as COLREGS, for COLlision REGulationS). The COLREGS rules specify, for example, which vessel is responsible for giving way to the other and to which side of the “stand-on” vessel to maneuver. Three primary COLREGS rules are considered in this paper: crossing, overtaking, and head-on situations. For autonomous USVs to be safely deployed in environments with other traffic boats, it is imperative that the USV's navigation algorithm obeys COLREGS. Furthermore, when other boats disregard their responsibility under COLREGS, the USV must fall back to its HA algorithms to prevent a collision. The proposed approach is based on velocity obstacles (VO) method, which generates a cone-shaped obstacle in the velocity space. Because VOs also specify on which side of the obstacle the vehicle will pass during the avoidance maneuver, COLREGS are encoded in the velocity space in a natural way. Results from several experiments involving up to four vessels are presented, in what we believe is the first on-water demonstration of autonomous COLREGS maneuvers without explicit intervehicle communication. We also show an application of this motion planner to a target trailing task, where a strategic planner commands USV waypoints based on high-level objectives, and the local motion planner ensures hazard avoidance and compliance with COLREGS during a traverse.
In recent years unmanned vehicles have grown in popularity, with an ever increasing number of applications in industry, the military and research within air, ground and marine domains. In particular, the challenges posed by unmanned marine vehicles in order to increase the level of autonomy include automatic obstacle avoidance and conformance with the Rules of the Road when navigating in the presence of other maritime traffic. The USV Master Plan which has been established for the US Navy outlines a list of objectives for improving autonomy in order to increase mission diversity and reduce the amount of supervisory intervention. This paper addresses the specific development needs based on notable research carried out to date, primarily with regard to navigation, guidance, control and motion planning. The integration of the International Regulations for Avoiding Collisions at Sea within the obstacle avoidance protocols seeks to prevent maritime accidents attributed to human error. The addition of these critical safety measures may be key to a future growth in demand for USVs, as they serve to pave the way for establishing legal policies for unmanned vessels.
This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an upper speed limit. The artificial potential field ap proach has been extended to collision avoidance for all ma nipulator links. In addition, a joint space artificial potential field is used to satisfy the manipulator internal joint con straints. This method has been implemented in the COSMOS system for a PUMA 560 robot. Real-time collision avoidance demonstrations on moving obstacles have been performed by using visual sensing.
This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based dynamical models and avoiding obstacles in the robot's environment. The authors consider generic systems that express the nonlinear dynamics of a robot in terms of the robot's high-dimensional configuration space. Kinodynamic planning is treated as a motion-planning problem in a higher dimensional state space that has both first-order differential constraints and obstacle based global constraints. The state space serves the same role as the configuration space for basic path planning; however, standard randomized path-planning techniques do not directly apply to planning trajectories in the state space. The authors have developed a randomized planning approach that is particularly tailored to trajectory planni ng problems in high-dimensional state spaces. The basis for this approach is the construction of rapidly exploring random trees, which offer benefits that are similar to those obtained by successful randomized holonomic planning methods but apply to a much broader class of problems. Theoretical analysis of the algorithm is given. Experimental results are presented for an implementation that computes trajectories for hovercrafts and satellites in cluttered environments, resulting in state spaces of up to 12 dimensions.
One of the key elements in automatic simulation of ship manoeuvring in confined waterways is route finding and collision avoidance. This paper presents a new practical method of automatic trajectory planning and collision avoidance based on an artificial potential field and speed vector. Collision prevention regulations and international navigational rules have been incorporated into the algorithm. The algorithm is fairly straightforward and simple to implement, but has been shown to be effective in finding safe paths for all ships concerned in complex situations. The method has been applied to some typical test cases and the results are very encouraging.
Planning the path of an autonomous, agile vehicle in a dynamic
environment is a very complex problem, especially when the vehicle is
required to use its full maneuvering capabilities. Recent efforts aimed
at using randomized algorithms for planning the path of kinematic and
dynamic vehicles have demonstrated considerable potential for
implementation on future autonomous platforms. This paper builds upon
these efforts by proposing a randomized motion planning architecture for
dynamical systems in the presence of fixed and moving obstacles. This
architecture addresses the dynamic constraints on the vehicle's motion,
and it provides at the same time a consistent decoupling between
low-level control and motion planning. Simulation examples involving a
ground robot and a small autonomous helicopter, are presented and
discussed
Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.
Inevitable collision states: A step towards safer robots
- T Fraichard
- H Asama