No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Proactive Collision Avoidance for ASVs using A Dynamic Reciprocal Velocity Obstacles Method
... "A Guide to the Collision Avoidance Rules" [5] comprehensively examines the core COLREGs Rules through various precedents and expert discussions. In keeping with this guide, a collision avoidance process on the basis of COLREGs Rules 5,7,8,[13][14][15][16][17] proposed, as shown in Figure A1. First, in accordance with Rule 5, a vessel shall perform appropriate observations to ensure that the present situation and collision risk are fully assessed using all available means appropriate to the given circumstances and conditions. ...
... First, if the stand-on vessel is the cause of constant collision risk, a collision-avoidance action in accordance with Rule 17 can be taken; however, the time point for this collision avoidance was not defined. Second, assuming the lengths of the MASS and TS to be the diameter, the velocity obstacle (VO) [6][7][8][9][10][11] combines the radii of the MASS and TS to formulate a circle for creating a cone-shaped danger zone. However, because this circle is smaller than the ship domain (SD), where no TS exists for preventing collision, the collision-avoidance action is taken with a collision risk always existing with no minimum safe distance being secured. ...
... The simulation results showed that SDVO + FIS-NC could avoid the TS according to COLREGs Rules 5,7,8,[13][14][15][16][17], via Algorithms 1 and 2. In this section, the quality of the local route created by SDVO + FIS-NC is discussed in terms of safety and effectiveness during navigation. ...
A maritime autonomous surface ship (MASS) ensures safety and effectiveness during navigation using its ability to prevent collisions with a nearby target ship (TS). This avoids the loss of human life and property. Therefore, collision avoidance of MASSs has been actively researched recently. However, previous studies did not consider all factors crucial to collision avoidance in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) Rules 5, 7, 8, and 13–17. In this study, a local route-planning algorithm that takes collision-avoidance actions in compliance with COLREGs Rules using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO) is proposed. FIS-NC is used to infer the collision risk index (CRI) and determine the time point for collision avoidance. Following this, I extended the VO using the SD to secure the minimum safe distance between the MASS and the TS when they pass each other. Unlike previous methods, the proposed algorithm can be used to perform safe and efficient navigation in terms of near-collision accidents, inferred CRI, and deviation from the course angle route by taking collision-avoidance actions in compliance with COLREGs Rules 5, 7, 8, and 13–17.
... P6. Environmental disturbances: Table 3 shows that when it comes to environmental disturbances, more than half (27, or 60%) of the papers do not take any environmental disturbances into consideration. Several papers [e.g., 25,26,27] are focusing only on the effect of current on the vessel (7, or 16%), some consider both current and wind (4, or 9%) [47,49,53,54], and only two papers consider both current, wind, and waves [29,35]. None of the papers consider waves as the only environmental disturbance affecting the ship's movement; however, waves are included in two papers together with wind and current. ...
... CRA parameters are not limited only to TCPA and DCPA, although these are the most commonly used ones. Other papers also consider parameters such as the distance of the last-minute avoidance, distance to the target vessel, ratio of speed, relative bearing, safe passage circle, and distance of adopt avoidance action [15,47,62]. ...
... 10 papers use none of the four objective function components, and no paper uses all four. In most of these cases, the papers are dealing with collision avoidance [9,15,47,51,52,53,55]; therefore, authors do not prioritize optimization of the path's length, energy efficiency, or other parameters but instead focus on safety of the collision-free path. Other components included in objective functions by some authors are tractability [31]; cost on deviating from the relative nominal trajectory, and on control input [41]; and navigation restoration time and angle during collision avoidance manoeuvre as well as optimal safe avoidance turning angle [18]. ...
Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.
... Reactive collision avoidance, i.e. the ability, based on a sensor-based perception of the local environment, to perform evasive manoeuvres that mitigate collision risk, remains a very challenging undertaking (e.g., see [12]- [15]). This is not to say, however, that the topic is not well-researched; a wide variety of approaches have been proposed, including especially (but not exhaustively) artificial potential field methods [16]- [18], dynamic window methods [19]- [21], velocity obstacle methods [22], [23] and optimal control-based methods [24]- [28]. However, it appears from a literature review that, when applied to autonomous vehicles with nonholonomic and real-time constraints, the approaches suggested so far suffer from one or more of the following drawbacks [29]- [32]: ...
... Raw closeness penalty (23) where α x is in the order of magnitude of the sensor range, such that sufficiently high negative rewards are given as objects get closer to the own-ship. ...
... Furthermore, the reward must reflect the variable risk associated with the direction of a target ship's velocity; an approaching target ship gives rise to a much higher risk than a receding one. In addition, the relatively steep function used as weighting term in Equation 23 was exchanged for a flatter function of the form 1/(1 + exp(x)), so as to give dynamic obstacles detected around the own-ship sufficient priority. Making adjustments to the static obstacle penalty to adhere to these requirements, the penalty for a single dynamic obstacle was chosen as ...
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters.
... Current state-of-the-art methods for ASVs operate following COLREGs on several single-to-single encounters with sequential actions (e.g., [6], [7]) and reciprocal cooperative actions by the obstacles (e.g., [8], [9]). These sequential and myopic methods may produce conflicting actions in realworld scenarios. ...
... As the majority of the encountered obstacles in aquatic environments are unknown or dynamic -detected by the ASV on-board sensors -many studies (e.g., [7], [11]) have addressed the collision avoidance problem based on the local domain. Local-based methods include Artificial Potential Field (APF) [6], [12], Velocity Obstacle (VO) [7], [13] and its variant [8], Dynamic Window [14], model-predictive [15], evolutionary algorithm [16], learning-based model [17]. Other examples include fuzzy adaptive control model [18] and Differential Evolution algorithm [19]. ...
... This work was supported by EU ERC grant SAHR. 1 Conversely, the velocity obstacle (VO) approach is designed for dynamic environments. VO takes into account the motion of the obstacles and their potential future positions to calculate collision-free motion [5], [6]. VO has been successfully used in shared control on wheelchairs [7]. ...
... control loop). 6 Comparing FOA to MuMo, this is an effective speed up of data reception to control output of over 200. In the experiment, where a person suddenly appeared in front of the robot at a distance of approximately 0.4 m, the algorithm is required to update the motion in a fraction of a second to ensure the continuous movement of the robot at a speed of approximately 0.5 m/s. ...
Humans excel at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance which is adept at partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance (FOA). The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. Thus, we can ensure that the agent does not become stuck when a feasible path exists between obstacles. Our algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in shared-control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller took 1 millisecond to evaluate over 30000 data points.
... Despite these challenges, research community have developed very promising MASS applications focusing on autonomous path planning. Some of these MASS applications base on artificial potential fields [2], [3], velocity obstacles [4], [5], model predictive control [6], [7], multi objective optimisation [8], artificial neural networks [9], ant colony opti-misation [10] and genetic algorithm [11]. The main feature of all path planning algorithms in MASS for collision avoidance is the distance from other ship that indicates the time and scale of evasive manoeuvre. ...
... In addition, [30] have stated that the goal of MASS may be to track another ship from the distance of radius R o , which can be deemed as the safe passing distance. [5] assume minimum safe passing distance as the sum of half distance of own and target ship length. ...
... Zhao et al. (2016) modify the ORCA algorithm and consider the reactive behavior between USVs. Kufoalor et al. (2018) assign the responsibility for collision avoidance between unmanned ships based on the principle of RVO. It is worth noting that the research group of Gelder, P.H.A.J.M is a typical representative of VO method used in ship collision avoidance applications: In , the non-linear velocity obstacle region is constructed which is based on the assumption that the predicted trajectories of other ships can be obtained, it can be used to support OOW making the collision avoidance decisions; In , the collision avoidance system named GVO-CAS is developed which is based on the principle of GRVO; In addition to that, VO is also used for collision risk detection and analysis Chen et al., 2018Chen et al., , 2019. ...
... These algorithms cannot calculate the optimal collision avoidance decisions. Although some VO algorithms find out the optimal solution by designing objective function (Zhao et al., 2016;Kufoalor et al., 2018) or giving constraint rules , few methods can fully consider the real application scene and be used to design a complete CAS. The modified VO method proposed in this paper fully considers various limiting factors in the real marine environment, including ship maneuverability, multi-ship encounter situation, COLREGS, course maintenance, good seamanship and situations in which other vessels take actions. ...
This paper proposed a new collision avoidance decision-making system designed for autonomous ship. The system outputs collision avoidance decisions based on the latest information at a certain frequency, which is suitable for real ship application. Front end and back end are two main components of this system. Front end provides preliminary information while back end generates collision avoidance decisions. Based on modified velocity obstacle method, the multistage optimization decision model is introduced and various constrains are considered including ship maneuverability, multi-ship, COLREGS, off-course and seamanship. Then the interactive actions taken by other ships during collision avoidance process are further analyzed. The modified velocity obstacle method incorporates a Finite State Machine (FSM) which can be used to handle the dynamic behavior of other ships. Finally, the case study is completed on the Electronic Chart System (ECS) to show that the proposed collision avoidance decision-making system is robust and effective under various marine scenarios. Therefore, the system has great potential to be equipped on board in the future.
... Reliable collision avoidance is crucial to the deployment of Autonomous Surface Vehicles (ASV), which is still a challenging problem [1], especially in congested environments. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) [2] defines rules for collision avoidance between vessels, which have been used to develop obstacle avoidance solutions in multi-ASV navigation scenarios [3], [4], [5], [6]. However, following COLREGs may cause conflicting actions when the roles of each ego vehicle with respect to different neighboring vehicles are in conflict, especially in congested waters [7]. ...
Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.
... Navigation and obstacle avoidance in aquatic environments for ASVs in high-traffic scenarios is still an open challenge, as COLREGs is not defined for multi-encounter situations. Current state-of-the-art methods for ASVs operate following COL-REGs on several single-to-single encounters with sequential actions (e.g., [18,17]) and reciprocal cooperative actions by the obstacles (e.g., [16,2]). However, these myopic methods may produce conflicting actions ('give-way' vs. 'stand-on') in real-world scenarios [7]. ...
Research on coastal regions traditionally involves methods like manual sampling, monitoring buoys, and remote sensing, but these methods face challenges in spatially and temporally diverse regions of interest. Autonomous surface vehicles (ASVs) with artificial intelligence (AI) are being explored, and recognized by the International Maritime Organization (IMO) as vital for future ecosystem understanding. However, there is not yet a mature technology for autonomous environmental monitoring due to typically complex coastal situations: (1) many static (e.g., buoy, dock) and dynamic (e.g., boats) obstacles not compliant with the rules of the road (COLREGs); (2) uncharted or uncertain information (e.g., non-updated nautical chart); and (3) high-cost ASVs not accessible to the community and citizen science while resulting in technology illiteracy. To address the above challenges, my research involves both system and algorithmic development: (1) a robotic boat system for stable and reliable in-water monitoring, (2) maritime perception to detect and track obstacles (such as buoys, and boats), and (3) navigational decision-making with multiple-obstacle avoidance and multi-objective optimization.
... Their system offers a comprehensive and adaptable solution for impact avoidance, uniquely considering a range of real-world maritime factors and dynamically adapting to the evolving conditions of marine navigation. This represents a significant enhancement over other VO algorithms [119,123,124], which may not fully account for the complexities and dynamic nature of maritime environments. ...
The collisions between bridges and ships might cause severe damage to both of them, which is impossible to avoid completely, although several specifications or requirements need to be followed in the design of bridges and during the navigation of ships passing bridge. Many researches on protective technology had been conducted to reduce the potentially disastrous consequences. These technologies can be broadly categorized into two main types: the technologies of collision avoidance, which try to reduce the collision possibility by warning the passing ship that might impact the bridge; and passive collision protections, which use protective structures to minimize the damage of bridge and ship due to impact. The purpose of the present paper is to systematically summarize both classifications and then provide insights into their characteristics, advantages, disadvantages, and suitable conditions for application. Additionally, the related approaches originally designed for other applications but with potential relevance are also discussed, such as ship-ship collision avoidance. This review can serve as meaningful guidance and reference for future research and realistic engineering applications.
... Different types of Velocity Obstacle (VO) based methods have been proposed for collision avoidance in (Kuwata et al., 2014;Kluge and Prassler, 2003;Kufoalor et al., 2018;Cho et al., 2020;Cho et al., 2021), where the core idea is to compute a set of reachable velocities for the own-ship which do not cause collision with nearby dynamic obstacles. COLREGS adherence and dynamic obstacle kinematic uncertainties have been considered by specifying additional constraints when computing the VO, in probabilistic versions of the algorithm (Kluge and Prassler, 2003;Cho et al., 2021). ...
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.
... Deliberate methods, such as well-known graphsearch algorithms like Voronoi graphs and A* [15] and, as well as randomized approaches like probabilistic roadmaps [19] and rapidly-exploring random trees [18], build an optimal path ahead of time that is to be followed using a lowlevel steering controller. Reactive approaches, dynamic window techniques, optimal control-based techniques, and, most significantly, artificial potential field and velocity obstacle techniques [8], [9] use sensor readings from the surrounding area to inform their guidance decisions. ...
In this paper, we examine the viability of solving the dual-objective problem of controlling an under-actuated autonomous surface vehicle to follow a predefined path and avoid colliding with stationary objects using proximal policy optimization, a cutting-edge deep reinforcement learning algorithm for continuous control tasks. This research introduces a novel approach to enhancing the navigation capabilities of ASVs by employing Deep Reinforcement Learning (DRL) techniques for collision avoidance and path following. For obstacle detection, Equipped with multiple rangefinder sensors, the Open AI Gym Python toolkit-based simulation environment presents a difficult training and evaluation environment for the AI agent. Depending on the approach it takes-which can vary from radical obstacle avoidance to radical path adherence-the trained agent succeeds almost 100% of the time in episodic tasks. INDEX TERMS: machine learning controller, collision avoidance, autonomous surface vehicle, Deep reinforcement learning.
... However, USVs should follow the International Regulations for Preventing Collisions at Sea (COLREGS) [3], which has become the consensus of relevant research. Common collision avoidance algorithms include the artificial potential field (APF) method [4][5][6][7][8], velocity obstacle (VO) algorithm [8][9][10][11][12][13] and some intelligent algorithms [13][14][15][16][17][18]. ...
To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less.
... In [8], the authors propose a proactive, instead of a reactive, collision avoidance method for ASVs, for the Velocity Obstacles Framework, which employs relative motion arguments in order to proceed collision avoidance operations. ...
... Conversely, the Velocity Obstacle (VO) approach is designed for dynamic environments. VO takes into account the motion of the obstacles and their potential future positions to calculate a collision-free motion [6]- [8]. VO has been successfully used in shared control on wheelchairs [9]. ...
Humans are remarkable at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance robust to partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance. The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. We can ensure that the agent does not get stuck when a feasible path exists between obstacles. The algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in a shared control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller on the onboard computer took 1 millisecond to evaluate over 30000 data points.
... As can be seen above, VO algorithm has a convenient framework that can adapt maritime environment. From this intuition, Kufoalor et al. (2018) propose reciprocal VO method in which the responsibility of collision avoidance has been shared between own and target (obstacle) ship. It means that MASS actions depend on weather the target ship take action or not. ...
Unmanned maritime systems are evolving in a rapidly changing environment. Although the regulation and international law processes are still in progress, there are numerous research attempts regarding autonomous maritime vehicle path planning particularly. In contrast to ground and air autonomous path planning, ship path planning has numerous pitfalls such as safety, complexity and environmental dynamics that hinder the emergence of reliable autonomous systems. This review explores the path planning algorithms of autonomous maritime vehicles and their collision regulation relevance in order to reveal how the research community handles this issue. Our relevant findings point out that there are still many traffic rules to be dealt by path planning algorithms. Algorithms that can be calibrated in terms of safe distance, safe speed and etc. may be deemed more compliant after regulation amendments.
... Several methods for collision avoidance at sea are available in the literature. There are local, short-term methods such as dynamic window (DW), velocity obstacles (VOs), and the branching-course model-predictive controller (BC-MPC) [5,6,7,8], and global methods, often employing graph-search or optimization-based techniques [9,10]. Some This work was supported in part by the Research Council of Norway through project number 269116, as well as through the Centres of Excellence funding scheme with project number 223254. ...
... According to the clustering scheme used in , all approaches relied at least on classified object, meaning that the object status as ship was known and principle position and speed data were available, even though specifically Rate of Turn values were not commonly considered. However, only Burmeister et al. Kufoalor et al. (2018) considered handling of identified objects, that included respecting a navigational status provided from a situational awareness system. Kufoalor et al. (2019) is also the only approach identified, that considered sensor uncertainties in obstacle detection by partly working with interval values. ...
In this survey, results from an investigation on collision avoidance and path planning methods developed in recent research are provided. In particular, existing methods based on Artificial Intelligence, data-driven methods based on Machine Learning, and other Data Science approaches are investigated to provide a comprehensive overview of maritime collision avoidance techniques applicable to Maritime Autonomous Surface Ships. Relevant aspects of those methods and approaches are summarized and put into suitable perspectives. As autonomous systems are expected to operate alongside or in place of conventionally manned vessels, they must comply with the COLREGs for robust decision-support/-making. Thus, the survey specifically covers how COLREGs are addressed by the investigated methods and approaches. A conclusion regarding their utilization in industrial implementations is drawn.
... Ship trajectory prediction plays an important role in ship collision detection and avoidance . Some methods are basic and simple, such as predicting the trajectories of other ships are straight lines (Johansen et al., 2016;Kufoalor et al., 2018); The method of trajectory forecasting based on historical ship position data from AIS (Tu et al., 2018) receives attention in recent years, a few models and methods including extended Kalman filter (Fossen, 2018), Hidden Markov Model (Peel and Good, 2011) are used in this area. Moreover, some researchers assume that ships can broadcast and share their navigation intentions (Kim et al., 2017) and planned route Chen et al., 2019;Du et al., 2020) with each other through communication devices, so as to realize the trajectory prediction of target ships. ...
The multi-ship encounter situation at sea is characterized by high complexity and uncertainty, which is a big challenge for both traditional ships and the new autonomous ships. In order to make reasonable navigation decisions and perform well under multi-ship encounter situation, it is necessary to grasp the current scenario correctly and intelligently. Therefore, in this paper, an adaptive understanding model for multi-ship encounter situation is proposed. The core function of this model is to infer the navigation intention of other target ships under the same situation. This model is mainly composed of two sub-models. One is the ship encounter situation analysis model, which realizes the cognition of the whole encounter scenario from the global perspective by maintaining the “double matrix”. The second is the ship navigation intention inference model, the key part of the model is a set of well-designed fuzzy inference system. The output of the encounter situation analysis model is the input of the intention inference model, and these two models are closely linked to form a unified whole. This model is verified by both simulation-based and real scenario-based experiments, the results show that this model can perform well under the complex multi-ship encounter situation. Moreover, some necessary discussion and analysis for this inference model are also stated at the end of this paper, in the future, we expect that this model can be applied in real situations.
... One of the first obstacle representations to be used for assessing collision risk is the closest point of approach (CPA) [1], which computes the distance and point in time when two vessels are at their closest, given that the vessels have a known constant velocity. CPA was initially developed to give human readable feedback to navigators on the risk associated with the speed and course of the vessel, but has more recently been incorporated into automated COLAV systems [2], [3]. Based on the same idea as CPA, the velocity obstacle (VO) representation [4], [5], computes the set of velocities which lead to a collision, i.e. giving a distance at the closest point of approach (dCPA) of zero, and a time of closest point of approach (tCPA) greater then zero. ...
When operating autonomous surface vessels in uncertain environments with dynamic obstacles, planning safe trajectories and evaluating collision risk is key to navigating safely. In order to perform these tasks, it is important to have a computationally efficient and adaptable obstacle representation to allow for quick and robust predictions of the obstacle trajectory. This paper presents a novel space-time obstacle representation, which is able to predict the reachable set for a dynamic obstacle under uncertainty. This is done by projecting the area occupied by the obstacle forward in time, using a set of velocities representing the possible maneuvers that the obstacle may take. Under some mild assumptions, we show how the space-time obstacle can be implemented in a computationally efficient way, using both convex polytopes and ellipsoids. Additionally, we show how the space-time obstacle representation can be used for risk assessment, collision avoidance and trajectory planning for autonomous surface vessels.
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.
Collision avoidance (CA) systems are pivotal for enabling vehicles to autonomously complete tasks in environments containing obstacles. With its low computational burden and underlying flexibility, the velocity obstacle (VO) algorithm presents a favorable method to avoid collisions, which is based on representing obstacles in the velocity space. In this study, we use the VO principle to form a reactive strategy for vehicles to avoid collisions with dynamic obstacles, which is applied to two different classes of systems, specifically nonholonomic vehicles and underactuated surface vessels. Instead of producing velocity references, the algorithm outputs collision-free directions, thus circumventing the need for explicitly controlling the vehicle speed. Moreover, the majority of existing VO approaches are only supported by simulations and experiments of specific CA scenarios, and the few studies that include some theoretical assurance are based on assumptions that cannot be guaranteed in the general case. In this article, we consider factors such as vehicle dynamics and constraints in a rigorous analysis of the algorithm. We analytically derive conditions ensuring feasibility of the avoidance maneuvers and overall safety of the vehicle, which provide intuitive requirements on the parameters of the algorithm. The theoretical results are supported through simulations and experiments of the strategy applied to a nonholonomic vehicle and an underactuated marine vessel.
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.
A growing interest in developing autonomous surface vehicles (ASVs) has been witnessed during the past two decades, including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways. This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches. It features a holistic viewpoint of ASV safe navigation, namely from collision detection to decision making and then to path replanning. The existing methods in all these three stages are classified according to various criteria. An in-time overview of the recently-developed learning-based methods in motion prediction and path replanning is provided, with a discussion on ASV navigation scenarios and tasks where learning-based methods may be needed. Finally, more general challenges and future directions of ASV navigation are highlighted.
A velocity obstacle (VO) based collision avoidance method for autonomous surface vessels operating in restricted and unstructured environments with traffic is considered. We propose a novel VO for enforcing maneuvering compliance with rules 13-15 and 17 in the International Regulations for Preventing Collisions at Sea (COLREGs), where the vessel-to-vessel encounter is first classified w.r.t. the COLREGs, then an encounter-specific domain is assigned to the opposing vessel, and finally a VO for that domain is formulated. The maneuvers of the opposing vessel throughout the encounter is considered when evaluating which side is appropriate to pass the vessel on. This increases robustness to non-compliant behaviour by the opposing vessel. The domain size is determined based on a measure for the available space to maneuver, which ensures an appropriate separation of the vessels in both confined spaces and open waters. Furthermore, collision avoidance with static obstacles from electronic charts is included, where a convex set that is free of static obstacles is constructed to simplify complex geometries, and the boundary of the set is enforced by VOs. The performance of the proposed collision avoidance method is demonstrated through numerical simulation, where it show compliance with COLREGs rules 13-15 and 17. Furthermore, the novel VO shows improved COLREGs compliance compared to another popular COLREGs-specific VO.
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—known as COLREGs—relevant for motion planning. We use these traffic rules to derive a trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions, that is, in traffic environments with human operated vessels. The choice of an 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 receding horizon. The ability to plan considering different types of constraints over a long horizon in a unified manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior. The efficacy of the derived algorithm is validated in different simulation scenarios.
A potential field-based Extended Dynamic Window Approach (EDWA) is proposed to fulfill the real-time trajectory planning for automatic berthing. To avoid falling into a local minimum, an nonuniform Theta* (NT*) is introduced to search the global path considering the risk of obstacles. Three potential fields are established: the attractive potential field guides the unmanned surface vehicle (USV) along its global path; the grid-based repulsive potential field is designed to keep the USV away from shores; and the COLREGs-compliant repulsive potential field is to avoid collision from other USVs. Taking the dynamic constraints of the USV into account, EDWA generates predicted trajectories of both constant force and deceleration phases. The objective function is subsequently produced to select them optimally according to the established potential fields. Finally, a linear MPC is presented to track the designed berthing trajectory considering the real-time requirement. The whole automatic berthing scheme is simulated on the Dalian port. The results indicate that the USVs avoid the potential collisions in accordance with COLREGs, and successfully reach their desired berthing spots with reasonable small deviations.
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-known as COLREGs-relevant for motion planning. We use these traffic rules to derive a trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions, that is, in traffic environments with human operated vessels. The choice of an 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 receding horizon. The ability to plan considering different types of constraints over a long horizon in a unified manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior. The efficacy of the derived algorithm is validated in different simulation scenarios.
Collision avoidance (COLAV) is a prerequisite for the navigation safety of unmanned surface vehicles (USVs). Since USVs have to avoid obstacles clearly and timely, i.e. the COLAV should be agile, the COLAV algorithm should have low computation complexity and make efficient COLAV decisions. However, balancing between the computation complexity and the COLAV decision optimality is still intractable at present. This paper innovatively proposes a COLAV algorithm for USVs by combining the velocity obstacle method with the predictive model method, named the collision shielded model predictive control (CS-MPC) algorithm, such that the agility of USVs COLAV is improved. The runtime of the proposed COLAV algorithm is shortened by shielding the dangerous parts of the search space of the COLAV decisions, and the COLAV decision is efficient with the aid of the accurately predicted motion trajectory by the motion mathematical model of USVs. As such, the USV can safely navigate in complex water areas where multiple vessels and obstacles exist. A series of simulations on a yacht in different kinds of encounter situations were carried out to verify the effectiveness and the agility of the proposed CS-MPC COLAV algorithm.
This paper proposes a proactive velocity obstacle (PVO) method through pre-judging whether there are collision risks between an unmanned surface vessel (USV) and its obstacle vessels according to the predicted motion states of the USV by its motion mathematical model to optimize the collision avoidance decision-making. Then integrating the proposed PVO method and the line of sight (LOS) algorithm into the set-based guidance (SBG) framework, we create a dynamic collision avoidance (DCA) solution scheme of USVs and carry out simulations in the cases of single vessel and multiple vessels encounter, respectively. The USV-DCA solution scheme can make the USV successfully avoid obstacle vessels while complying with the international regulations for preventing collisions at sea (COLREGs) and follow the desired path without collisions. The simulation results show that the USV-DCA solution scheme based on PVO and SBG can make USVs avert a series of small velocity changes and make a safer collision avoidance decision than the original SBG framework scheme in the multiple vessels encounter case, which verifies the effectiveness and superiority of our proposed USV-DCA solution scheme.
All vessels operating in a marine environment are required to comply with the international regulations for preventing collisions at sea (COLREGs), which provide the guidelines and evasive procedures required to resolve potential conflicts between vessels. However, not all vessels strictly abide by COLREGs, often leading to dangerous situations. This paper presents a novel approach for robust collision avoidance in encounter situations involving COLREG-violating vessels. A probabilistic velocity obstacle algorithm based on intent inference is designed and implemented with consideration of the tradeoff between the adherence to traffic rules and the proactive evasive actions for safety. One-to-one and multi-ship encounter situations in the presence of rule-violating vessels are examined through Monte-Carlo simulations, and the results are discussed to demonstrate the feasibility and performance of the proposed approach.
Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this paper, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method.
This thesis presents topics related to optimization and control of ASVs. This includes low-level motion control, mid-level local trajectory planning and collision avoidance (COLAV), and high-level global trajectory planning. The main part of the thesis, is a collection of peer-reviewed articles, six journal articles and three conference papers. In addition to the article collection, the initial part of the thesis contains an introduction to the main topics of low-level motion control, mid-level local trajectory planning and COLAV and high-level
global trajectory planning.
Maritime Autonomous Surface Ships (MASS) receive enormous attention in recent years. Compared to an individual MASS, greater efficiency and operational capability can be realized by cooperative MASS. An increasing number of researches propose different methods for cooperative multiple ships, such as cooperative collision avoidance, formation control, etc. However, in existing research, hydrodynamic effects, such as the ship-ship and confined water effects, are ignored when designing motion controllers for ships. In fact, in the cooperative scenarios, MASS are close to each other, where hydrodynamic effects significantly impact ship motions. Neglecting hydrodynamic interactions may lead to control failures. Thus, this paper aims at connecting the research on cooperative control of multi-MASS and hydrodynamic effects. Systematic reviews on existing literature on cooperative MASS are carried out in this work. Subsequently, mentioned cooperative scenarios are analyzed, and the parameters involving hydrodynamic interactions are collected. Then, existing research on hydrodynamic effects related to the cooperative scenarios is reviewed to summarize the impacts of hydrodynamics on cooperative motion control of MASS.
About Frontiers Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals.
An enabling technology for future sea transports is safe and energy-efficient autonomous maritime navigation in narrow environments with other marine vessels present. This requires that the algorithm controlling the ship is able to account for the vessel's dynamics, and obeys the rules specified in the international regulations for preventing collision at sea (COLREGs). To account for these properties, this work proposes a two-step optimization-based and COLREGs-compliant motion planner tailored for large vessels. In the first step, a lattice-based motion planner is used to compute a dynamically feasible but suboptimal trajectory based on a library of precomputed motion primitives. To comply with the rules specified in COLREGs, the lattice-based planner is augmented with discrete states representing what type of COLREGs situations that are active with respect to other nearby vessels. The trajectory from the lattice-based planner is then used as warm-start for the second receding-horizon improvement step based on direct optimal control techniques. The aim of the second step is to solve the problem to local optimality with the discrete COLREGs states remained fixed. The applicability of the proposed motion planner is demonstrated in a simulation study, where it computes energy-efficient and COLREGs-compliant trajectories. Moreover, it is shown that the motion planner is able to prevent complex collision situations from occurring.
Ship collisions are major types of maritime accidents which may involve the loss of life and significant damage to property and the environment. Although many automatic ship collision avoidance algorithms have been suggested, most of them are only applicable to a single ship-to-ship encounter situation. Also, although there exist some studies on collision avoidance for multiple agent systems, maritime traffic rules have not been systematically incorporated in the algorithms which limit their practical applicability to real maritime traffic situations. In this study, we propose a rule-compliant automatic ship collision avoidance method that can be applied not only to single ship-to ship situations, but also to multiple-ship encounter situations with consideration of prediction uncertainty. In order to select appropriate evasive actions, a symmetric role-classification criterion is proposed by refining the current maritime traffic rules, and an efficient collision avoidance algorithm based on the probabilistic velocity obstacle method is applied. To verify and demonstrate the performance and practical utility of the proposed algorithm, Monte-Carlo simulations were conducted and the results are presented in this article.
In this paper, we present a mid-level collision avoidance algorithm for autonomous surface vehicles (ASVs) based on model predictive control (MPC) using nonlinear programming. The algorithm enables avoidance of both static and moving obstacles, and following of a desired nominal trajectory if there is no danger of collision. We compare two alternative objective functions, where one is a quadratic function and the other is a nonlinear function designed to produce maneuvers observable for other vessels in compliance with rule 8 of the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is implemented in the CASADI framework and uses the IPOPT solver. The performance of the algorithm is evaluated through simulations which show promising results. Furthermore, the algorithm is considered computationally feasible to run in real time. This algorithm serves as a base algorithm for further development in order to ensure full COLREGS compliance.
This paper presents ICS-AVOID, a collision avoidance scheme based upon the concept of Inevitable Collision State (ICS), ie a state for which, no matter what the future trajectory of the robotic system is, a collision eventually occurs. By design, ICS-AVOID can handle dynamic environments since ICS do take into account the future behaviour of moving objects. ICS-AVOID is designed to keep the system away from ICS. By doing so, motion safety is guaranteed (by definition a robotic system in a non-ICS state has at least one collision-free trajectory that it can use). To demonstrate the efficiency of ICS-AVOID, it has been extensively compared with two state-of-the-art collision avoidance schemes: the first one is built upon the Dynamic Window approach and the second one on the Velocity Obstacle concept. The results obtained show that, when provided with the same amount of information about the future evolution of the environment, ICS-AVOID outperforms the other two schemes. The first reason for this has to do with the extent to which each collision avoidance scheme reasons about the future. The second reason has to do with the ability of each collision avoidance scheme to find a safe control if one exists. ICS-AVOID is the only one which is complete in this respect thanks to the concept of Safe Control Kernel.
In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.
We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.
We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milli-seconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement.
This paper describes the dynamic window approach to reactive collision avoidance for mobile robots equipped with synchro-drives. The approach is derived directly from the motion dynamics of the robot and is therefore particularly well-suited for robots operating at high speed. It differs from previous approaches in that the search for commands controlling the translational and rotational velocity of the robot is carried out directly in the space of velocities. The advantage of our approach is that it correctly and in an elegant way incorporates the dynamics of the robot. This is done by reducing the search space to the dynamic window, which consists of the velocities reachable within a short time interval. Within the dynamic window the approach only considers admissible velocities yielding a trajectory on which the robot is able to stop safely. Among these velocities the combination of translational and rotational velocity is chosen by maximizing an objective function. The objective fu...
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.
Steering and Sailing Rules deal with conduct of vessels. Every vessel shall at all times maintain a proper lookout by sight and hearing as well as by all available means appropriate in the prevailing circumstances and conditions to make a full appraisal of the situation and of the risk of collision. Any action to avoid collision shall be taken in accordance with the rules, if the circumstances of the case admit, be positive, made in ample time, and with due regard to the observance of good seamanship. Any alteration of course and/or speed to avoid collision shall, if the circumstances of the case admit, be large enough to be readily apparent to another vessel observing visually or by radar; a succession of small alterations of course and/or speed should be avoided. While vessel proceeding along the course of a narrow channel or fairway shall keep as near to the outer limit of the channel or fairway, which lies on its starboard side as is safe and practicable. A vessel shall not cross a narrow channel or fairway if such crossing impedes the passage of a vessel, which can safely navigate only within such channel or fairway.
Reciprocal collision avoidance has become a popular area of research over recent years. Approaches have been developed for a variety of dynamic systems ranging from single integrators to car-like, differential-drive, and arbitrary, linear equations of motion. In this paper, we present two contributions. First, we provide a unification of these previous approaches under a single, generalized representation using control obstacles. In particular, we show how velocity obstacles, acceleration velocity obstacles, continuous control obstacles, and LQR-obstacles are special instances of our generalized framework. Secondly, we present an extension of control obstacles to general reciprocal collision avoidance for non-linear, non-homogeneous systems where the robots may have different state spaces and different non-linear equations of motion from one another. Previous approaches to reciprocal collision avoidance could not be applied to such systems, as they use a relative formulation of the equations of motion and can, therefore, only apply to homogeneous, linear systems where all robots have the same linear equations of motion. Our approach allows for general mobile robots to independently select new control inputs while avoiding collisions with each other. We implemented our approach in simulation for a variety of mobile robots with non-linear equations of motion: differential-drive, differential-drive with a trailer, car-like, and hovercrafts. We also performed physical experiments with a combination of differential-drive, differential-drive with a trailer, and car-like robots. Our results show that our approach is capable of letting a non-homogeneous group of robots with non-linear equations of motion safely avoid collisions at real-time computation rates.
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.
This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time.
The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories.
The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuvers, computed at discrete time intervals. An exhaustive search of the tree yields near-optimal trajectories that either minimize distance or motion time. A heuristic search of the tree is applicable to on-line planning. The method is demonstrated for point and disk robots among static and moving obstacles, and for an automated vehicle in an intelligent vehicle highway system scenario.
Handbook of Marine Craft Hydrodynamics and Motion Control is an extensive study of the latest research in hydrodynamics, guidance, navigation, and control systems for marine craft. The text establishes how the implementation of mathematical models and modern control theory can be used for simulation and verification of control systems, decision-support systems, and situational awareness systems. Coverage includes hydrodynamic models for marine craft, models for wind, waves and ocean currents, dynamics and stability of marine craft, advanced guidance principles, sensor fusion, and inertial navigation.
This important book includes the latest tools for analysis and design of advanced GNC systems and presents new material on unmanned underwater vehicles, surface craft, and autonomous vehicles. References and examples are included to enable engineers to analyze existing projects before making their own designs, as well as MATLAB scripts for hands-on software development and testing. Highlights of this Second Edition include:
- Topical case studies and worked examples demonstrating how you can apply modeling and control design techniques to your own designs
- A Github repository with MATLAB scripts (MSS toolbox) compatible with the latest software releases from Mathworks
- New content on mathematical modeling, including models for ships and underwater vehicles, hydrostatics, and control forces and moments
- New methods for guidance and navigation, including line-of-sight (LOS) guidance laws for path following, sensory systems, model-based navigation systems, and inertial navigation systems
This fully revised Second Edition includes innovative research in hydrodynamics and GNC systems for marine craft, from ships to autonomous vehicles operating on the surface and under water. Handbook of Marine Craft Hydrodynamics and Motion Control is a must-have for students and engineers working with unmanned systems, field robots, autonomous vehicles, and ships.
An approach to motion planning among moving obstacles is presented, whereby obstacles are modeled as intelligent decision-making agents. The decision-making processes of the obstacles are assumed to be similar to that of the mobile robot. A probabilistic extension to the velocity obstacle approach is used as a means for navigation and modeling uncertainty about the moving obstacles' decisions.