No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Real-time obstacle avoidance for manipulators and mobile robots
... Nowadays, quadrotor UAVS are used widely in agriculture to ensure accurate agricultural production [12]. Several control strategies have been employed to stabilize and control UAVs such as classical and implicit PID controllers [13], [14], linear quadratic regulator (LQR) [15], feedback linearization and adaptive feedback linearization control [16], [17], back stepping [18], sliding mode [19], adaptive fuzzy [20], neural network based MPC [21], in addition to other control approaches [22]. ...
... Hence, the terms d , d y , d z , d ϕ , d θ and d φ in equations (6)-(11) satisfy (13) and represent unspecified disturbance terms with real positive limits η x , η y , η z , η ϕ , η θ and η φ . ...
... where σ z = ν z + η z and η z denotes the upper bound of the disturbance signal as in assumption (13). ...
Fractional order calculus (FOC) uses arbitrary order operations in differentiation and integration. Previously, the absence of methods to solve fractional differential equations limited the application of fractional order calculus. However, with the development of new methods, FOC is now applicable in various fields and has been utilized to enhance control system performance in traditional techniques. FOC is used in this study to develop a new fractional structure for the sliding mode control approach that offers improved performance and more robustness to external disturbances. The fractional order sliding mode control (FOSMC) strategy is designed using a Lyapunov-based sliding condition to ensure system stability. To enhance performance, genetic algorithms are used to adjust the fractional orders and controller parameters. The proposed fractional sliding mode control system is applied on an unmanned aerial vehicle system affected by external disturbances and then it is compared with a conventional integer order sliding mode control (IOSMC) system. Simulation results have proved the efficiency of the proposed fractional order controller where it outperforms the conventional sliding mode controller in terms of better transient dynamics and more robustness to external disturbances.
... Autonomous navigation with obstacle avoidance is a significant challenge in robotics, particularly in dynamic and unknown environments. Various techniques have emerged to address this problem, including the widely recognized artificial potential fields (APF) method [1]. While APF is celebrated for its simplicity and efficiency compared to sampling-based methods like A* or Dijkstra, it is prone to local minima [2]. ...
... Safety Velocity Cones (SVCs) have been proposed in [17], [18] as a simple and lightweight reactive feedback-based approach while more sophisticated hybrid feedback control methods in [19] enable safe and global navigation in unknown environments. Most of these methods, except for [1], target single-integrator systems, raising safety concerns when applied to robots with complex dynamics. An extension to higher-order models via reference governors is proposed in [20], though it limits performance in less aggressive scenarios. ...
... By using distance functions, we avoid the need to explicitly describe obstacles with parametric functions, simplifying the representation of free space. This not only reduces the computational burden but also offers more flexibility in handling environments with numerous and unknown shaped obstacles, making it a more scalable and adaptable solution for navigation tasks; this approach has been used e.g., [1], [17], [18]. Consider the distance function d X ∁ to the obstacle set X ∁ . ...
This paper introduces DAF (Dissipative Avoidance Feedback), a novel approach for autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics. Unlike traditional APF (Artificial Potential Field) methods, which rely on repulsive forces based solely on position, DAF employs a dissipative feedback mechanism that adjusts the robot's motion in response to both its position and velocity, ensuring smoother, more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by considering the robot's state and local obstacle distance information. We show that the controller guarantees safe navigation in generic n-dimensional environments and that all undesired -limit points are unstable under certain \textit{controlled} curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces.
... Motion planning for robot manipulators in the presence of dynamic obstacles is a key capability that robots should have to ensure their safe operation in various applications ranging from manufacturing to home robotics [1]- [3]. A multitude of reactive motion planning approaches have been proposed in the literature that create velocity (or force) fields to avoid obstacles and also ensure progress towards the goal [4]- [8]. These velocity (vector) fields are primarily defined over the real Euclidean space; hence, they are inherently designed for point robots with point obstacles [9]. ...
... Email: hallj@bu.edu. 4 Author is with University of Nottingham. Email: luis.figueredo@ieee.org. ...
This paper is about generating motion plans for high degree-of-freedom systems that account for collisions along the entire body. A particular class of mathematical programs with complementarity constraints become useful in this regard. Optimization-based planners can tackle confined-space trajectory planning while being cognizant of robot constraints. However, introducing obstacles in this setting transforms the formulation into a non-convex problem (oftentimes with ill-posed bilinear constraints), which is non-trivial in a real-time setting. To this end, we present the FLIQC (Fast LInear Quadratic Complementarity based) motion planner. Our planner employs a novel motion model that captures the entire rigid robot as well as the obstacle geometry and ensures non-penetration between the surfaces due to the imposed constraint. We perform thorough comparative studies with the state-of-the-art, which demonstrate improved performance. Extensive simulation and hardware experiments validate our claim of generating continuous and reactive motion plans at 1 kHz for modern collaborative robots with constant minimal parameters.
... There are two typical local search methods: DWA (Dynamic Window Approach) and APF(Artificial Potential Field) [7]. The DWA considers robot motion performance, samples multiple sets of velocities in the velocity space and uses trajectory evaluation function to get the optimal solution. ...
... In the algorithm, we introduce the attractive potential field [7] for sampling. New sample points are generated randomly. ...
... However, it cannot guarantee the optimal path, has high computational demands, and low sampling efficiency. Khatib et al. proposed the Artificial Potential Field (APF) method [9], which is simple in structure, requires less computation, and generates smooth trajectories. However, it is prone to getting stuck in local minima and performs poorly in highly complex environments. ...
In this study, to address the issues faced by mobile robots in complex environments, such as sparse rewards caused by limited effective experience, slow learning efficiency in the early stages of training, as well as poor obstacle avoidance performance in environments with dynamic obstacles, the authors proposed a new path planning algorithm for mobile robots by introducing Intrinsic Curiosity Module (ICM) and Long Short-Term Memory (LSTM) into the Proximal Policy Optimization (PPO) algorithm. ICM provided intrinsic rewards in addition to external rewards, accelerating the initial convergence speed. And the Actor-Critic network was optimized by employing a LSTM-based neural network to enhance the performance of avoiding dynamic obstacles. Then, various simulation experiments were conducted in Gazebo with scenarios featuring both static and dynamic obstacles, and the TurtleBot3 mobile robot was used for experimental verification. The experiments demonstrate that the proposed algorithm significantly accelerates convergence in environments with sparse rewards compared to traditional algorithms, and the robot can find more target points within a single episode, indicating more effective path planning ability. Additionally, it can avoid obstacles in various states. Finally, the effectiveness of the algorithm was validated using the TurtleBot3 physical robot in real-world scenarios. Results show that compared to Deep Deterministic Policy Gradient (DDPG), PPO, LSTM-PPO and ICM-PPO, the success rate of the proposed algorithm in path planning increases by 9%, 7.2%, 4% and 4.8% in the most complex simulation environment and by 20%, 16%, 10% and 12% in the physical environment, respectively.
... The traditional APF method constructs a gravitational potential field from the target point and a repulsive field from obstacles [44]. The robot moves toward the target while avoiding obstacles, guided by the combined potential field. ...
In response to the growing demands for faster production and cost-efficiency, collaborative and autonomous robots are playing increasingly important roles in various industries. However, ensuring safe interactions between robots and humans in shared workspaces continues to pose significant challenges. This paper provides a detailed review of motion planning algorithms designed for robotic manipulators working in dynamic environments, with a focus on efficiently adapting to changing conditions while ensuring human operator safety. The algorithms covered in this survey—identified through a comprehensive review of the literature and validated experimentally—include sampling-based methods, artificial potential field (APF) methods, optimization-based approaches, and learning-based techniques. Experimental evaluations offer a practical assessment of these algorithms’ real-time performance. By comparing and analysing the experimental results, this article highlights the relative efficiency of the algorithms and suggests strategies for improving motion planning in dynamic environments.
... These methods often use potential fields where attractive forces guide the robot toward the goal and repulsive forces push it away from obstacles. The Artificial Potential Field (APF) approach introduced in [7] is a seminal work in this domain, enabling real-time obstacle avoidance through the computation of artificial forces. Despite their simplicity and computational efficiency, APF methods are prone to issues such as local minima and oscillations in dynamic environments. ...
This work uses density functions for safe navigation in dynamic environments. The dynamic environment consists of time-varying obstacles as well as time-varying target sets. We propose an analytical construction of time-varying density functions to solve these navigation problems. The proposed approach leads to a time-varying feedback controller obtained as a positive gradient of the density function. This paper's main contribution is providing convergence proof using the analytically constructed density function for safe navigation in the presence of a dynamic obstacle set and time-varying target set. The results are the first of this kind developed for a system with integrator dynamics and open up the possibility for application to systems with more complex dynamics using methods based on control density function and inverse kinematic-based control design. We present the application of the developed approach for collision avoidance in multi-agent systems and robotic systems. While the theoretical results are produced for first-order integrator systems, we demonstrate how the framework can be applied for systems with non-trivial dynamics, such as Dubin's car model and fully actuated Euler-Lagrange system with robotics applications.
... In addition to the widely used Dijkstra and A* algorithms, a range of motion planning strategies has been developed to address various challenges and specific applications. For instance, the wavefront algorithm [Kha86] employs a distinctive method by propagating wavefronts outward from the starting point, creating a gradient map that helps in efficiently identifying the shortest path. On the other hand, the Potential Gradient Descent algorithm [C + 47], inspired by physical principles, treats the environment as a potential field. ...
Autonomous systems, including robots and drones, face significant challenges when navigating through dynamic environments, particularly within urban settings where obstacles, fluctuating traffic, and pedestrian activity are constantly shifting. Although, traditional motion planning algorithms like the wavefront planner and gradient descent planner, which use potential functions, work well in static environments, they fall short in situations where the environment is continuously changing. This work proposes a dynamic, real-time path planning approach specifically designed for autonomous systems, allowing them to effectively avoid static and dynamic obstacles, thereby enhancing their overall adaptability. The approach integrates the efficiency of conventional planners with the ability to make rapid adjustments in response to moving obstacles and environmental changes. The simulation results discussed in this article demonstrate the effectiveness of the proposed method, demonstrating its suitability for robotic path planning in both known and unknown environments, including those involving mobile objects, agents, or potential threats.
... The former requires a priori information of environments (e.g., position and shape of obstacles), whereas the latter utilizes only local knowledge of obstacles obtained from onboard sensors. Among the global methods, a computationally efficient solution is the artificial potential field (APF)-based approach [5], which uses a field of potential forces to push the robot toward a goal position and pull it away from obstacles [6], [7], [8]. Control barrier function (CBF) combined with quadratic programming (CBF-QP) also provides an effective strategy for developing controllers that can both stabilize a system and ensure safety, making it particularly useful for autonomous vehicles operating in obstacle-cluttered environments [9], [10], [11]. ...
In this article, we propose a novel approach, called integrated planning and tube-following Control (InPTC), for prescribed-time collision-free navigation of wheeled mobile robots in a compact convex workspace cluttered with static, sufficiently separated, and convex obstacles. A path planner with prescribed-time convergence is presented based upon Bouligand's tangent cones and time scale transformation (TST) techniques, yielding a continuous vector field that can guide the robot from almost all initial positions in the free space to the designated goal at a prescribed time, while avoiding entering the obstacle regions augmented with a safety margin. By leveraging barrier functions and TST, we further derive a tube-following controller to achieve robot trajectory tracking within a prescribed time less than the planner's settling time. This controller ensures the robot moves inside a predefined “safe tube” around the reference trajectory, where the tube radius is set to be less than the safety margin. Consequently, the robot will reach the goal location within a prescribed time while avoiding collision with any obstacles along the way. The proposed InPTC is implemented on a Mona robot operating in an arena cluttered with obstacles of various shapes. Experimental results demonstrate that InPTC not only generates smooth collision-free reference trajectories that converge to the goal location at the preassigned time of
(i.e., the required task completion time), but also achieves tube-following trajectory tracking with tracking accuracy higher than
after the preassigned time of
. This enables the robot to accomplish the navigation task within the required time of
.
... However, as the number of people increases, accurate and fast path planning becomes more difficult, increasing the computational time required to find the optimal path. More computationally efficient reactive methods are artificial potential fields [7], [8], the velocity obstacle (VO) [9], and the social force model (SFM) [10], [11]. SFM describes pedestrian movement as influenced by social forces that guide acceleration towards desired velocities while maintaining interpersonal distances. ...
When humans move in a shared space, they choose navigation strategies that preserve their mutual safety. At the same time, each human seeks to minimise the number of modifications to her/his path. In order to achieve this result, humans use unwritten rules and reach a consensus on their decisions about the motion direction by exchanging non-verbal messages. They then implement their choice in a mutually acceptable way. Socially-aware navigation denotes a research effort aimed at replicating this logic inside robots. Existing results focus either on how robots can participate in negotiations with humans, or on how they can move in a socially acceptable way. We propose a holistic approach in which the two aspects are jointly considered. Specifically, we show that by combining opinion dynamics (to reach a consensus) with vortex fields (to generate socially acceptable trajectories), the result outperforms the application of the two techniques in isolation.
... However, as the dimensionality of the search space increases, these algorithms become computationally expensive and are often unsuitable for real-time planning in dynamic scenarios. Potential field methods, such as Artificial Potential Field (APF) [5], which generate motion by treating the robot as a particle influenced by attractive forces towards the goal and repulsive forces from obstacles, offer faster computation but often suffer from local minima, causing the robot to get stuck before reaching the target. Optimization-based methods, such as trajectory smoothness [6] and trajectory generation [7], are effective in producing smooth, optimal trajectories but are highly sensitive to parameter tuning and environment changes. ...
Robots are increasingly deployed in dynamic and crowded environments, such as urban areas and shopping malls, where efficient and robust navigation is crucial. Traditional risk-based motion planning algorithms face challenges in such scenarios due to the lack of a well-defined search region, leading to inefficient exploration in irrelevant areas. While bi-directional and multi-directional search strategies can improve efficiency, they still result in significant unnecessary exploration. This article introduces the Neural Adaptive Multi-directional Risk-based Rapidly-exploring Random Tree (NAMR-RRT) to address these limitations. NAMR-RRT integrates neural network-generated heuristic regions to dynamically guide the exploration process, continuously refining the heuristic region and sampling rates during the planning process. This adaptive feature significantly enhances performance compared to neural-based methods with fixed heuristic regions and sampling rates. NAMR-RRT improves planning efficiency, reduces trajectory length, and ensures higher success by focusing the search on promising areas and continuously adjusting to environments. The experiment results from both simulations and real-world applications demonstrate the robustness and effectiveness of our proposed method in navigating dynamic environments. A website about this work is available at https://sites.google.com/view/namr-rrt.
... The Artificial Potential Field (APF) method [48] generates virtual force fields to facilitate smooth navigational adjustments for agents but is significantly limited by its tendency to become trapped in local minima. Emerging learning-based solutions, including data-driven approaches that computationally enhance traditional methodologies [51]- [53], have been introduced. ...
Path planning in high-dimensional spaces poses significant challenges, particularly in achieving both time efficiency and a fair success rate. To address these issues, we introduce a novel path-planning algorithm, Zonal RL-RRT, that leverages kd-tree partitioning to segment the map into zones while addressing zone connectivity, ensuring seamless transitions between zones. By breaking down the complex environment into multiple zones and using Q-learning as the high-level decision-maker, our algorithm achieves a 3x improvement in time efficiency compared to basic sampling methods such as RRT and RRT* in forest-like maps. Our approach outperforms heuristic-guided methods like BIT* and Informed RRT* by 1.5x in terms of runtime while maintaining robust and reliable success rates across 2D to 6D environments. Compared to learning-based methods like NeuralRRT* and MPNetSMP, as well as the heuristic RRT*J, our algorithm demonstrates, on average, 1.5x better performance in the same environments. We also evaluate the effectiveness of our approach through simulations of the UR10e arm manipulator in the MuJoCo environment. A key observation of our approach lies in its use of zone partitioning and Reinforcement Learning (RL) for adaptive high-level planning allowing the algorithm to accommodate flexible policies across diverse environments, making it a versatile tool for advanced path planning.
... Whether walking on a busy street, playing team sports or moving around an office space, such behaviour involves navigating towards one or more fixed or moving goal location(s) while simultaneously avoiding stationary and moving obstacles. Traditional approaches to understanding navigational behaviours have assumed that these behaviours are solely dependent on neurocognitive path planning or integration processes and require the construction of representational maps of the task environment [1][2][3]. However, a growing body of research (e.g. ...
This study investigated whether dynamical perceptual-motor primitives (DPMPs) could also be used to capture human navigation in a first-person herding task. To achieve this aim, human participants played a first-person herding game, in which they were required to corral virtual cows, called targets, into a specified containment zone. In addition to recording and modelling participants’ movement trajectories during gameplay, participants’ target-selection decisions (i.e. the order in which participants corralled targets) were recorded and modelled. The results revealed that a simple DPMP navigation model could effectively reproduce the movement trajectories of participants and that almost 80% of the participants’ target-selection decisions could be captured by a simple heuristic policy. Importantly, when this policy was coupled to the DPMP navigation model, the resulting system could successfully simulate and predict the behavioural dynamics (movement trajectories and target-selection decisions) of participants in novel multi-target contexts. Implications of the findings for understanding complex human perceptual-motor behaviour and the development of artificial agents for robust human–machine interaction are discussed.
... A general procedure of these methods is to take the environmental changes as input and output a trajectory for the subsequent few time frames. Artificial potential-fields [5] and numerous extended variations [6] [7] achieve real-time collision avoidance by exerting virtual forces on the robot to keep the robot away from obstacles. Further research like [8] outlines a real-time method to handle unexpected virtual and physical contact. ...
With the goal of efficiently computing collision-free robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lower bound of the final path cost as informed heuristics for fast path-finding. These heuristics guide a search tree to explore the roadmap during runtime. The search tree examines the edges using a fuzzy collision checking concerning the dynamic environment. Finally, the heuristics tree exploits knowledge fed back from the fuzzy collision checking module and updates the lower bound for the path cost. As we demonstrate in real-world experiments, the closed-loop formed by these three components significantly accelerates the planning procedure. An additional backtracking step ensures the feasibility of the resulting paths. Experiments in simulation and the real world show that HIRO can find collision-free paths considerably faster than baseline methods with and without prior knowledge of the environment.
... Pohan et al. [120] propose a hybridization of RRT* and Ant Colony System (RRT-ACS)to generate optimal parking paths efficiently, outperforming other algorithms in common scenarios. Recently, Cao et al. [121] integrate the advantages of RRT-Connect [77] for global path planning, artificial potential field (APF) [122] for local path planning, and cubic B-spline [123] for curve smoothing to optimize the path of unmanned aerial vehicles (UAVs). ...
Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.
... Conventional reaction-based methods are extensively used for robot navigation and obstacle avoidance. For instance, Artificial Potential Fields (APF) [26] for robot navigation generated repulsive forces to avoid obstacles. Reciprocal Velocity Obstacles (RVO) [27], [28] and Optimal Reciprocal Collision Avoidance (ORCA) [29], [30] improved the performance of velocity collision avoidance approaches [31] in multi-agent navigation problem by applying velocities following reciprocal obstacle avoidance behaviors. ...
In this work, we investigate the problem of Unmanned Surface Vehicle (USV) navigation in a dense marine environment with a high-intensity current flow. The complexities arising from static and dynamic obstacles and the disturbance forces caused by current flow render existing navigation protocols inadequate for ensuring safety and avoiding collisions at sea. To learn a safe and efficient robot policy, we propose a novel methodology that leverages attention mechanisms to capture heterogeneous interactions of the agents with the static and moving obstacles and the flow disturbances from the environment in space and time. In particular, we refine a temporal function with MarineFormer, a Transformer navigation policy for spatially variable Marine environment, trained end-to-end with reinforcement learning (RL). MarineFormer uses foundational spatio-temporal graph attention with transformer architecture to process spatial attention and temporal sequences in an environment that simulates a 2D turbulent marine condition. We propose architectural modifications that improve the stability and learning speed of the recurrent models. The flow velocity estimation, which can be derived from flow simulations or sensors, is incorporated into a model-free RL framework to prevent the robot from entering into high-intensity current flow regions including intense vortices, while potentially leveraging the flow to assist in transportation. The investigated 2D marine environment encompasses flow singularities, including vortices, sinks, and sources, representing fundamental planar flow patterns associated with flood or maritime thunderstorms. Our proposed method is trained with a new reward model to deal with static and dynamic obstacles and disturbances from the current flow.
... Although the above methods have demonstrated high computational efficiency and success rate in navigation tests, the voxel maps they rely on can not adequately describe dynamic obstacles with uncertainties, resulting in limited performance in dynamic and uncertain environments. For dynamic obstacles, early methods based on geometric maps proposed artificial potential field 18 and velocity obstacle 19 to generate simple control commands to prevent collisions. Recently, model predictive control (MPC) approaches 6,17,20 have emerged as dynamic obstacle avoidance methods. ...
In practical applications, the unpredictable movement of obstacles and the imprecise state observation of robots introduce significant uncertainties for the swarm of robots, especially in cluster environments. However, existing methods are difficult to realize safe navigation, considering uncertainties, complex environmental structures, and robot swarms. This paper introduces an extended state model predictive control planner with a safe probability field to address the multi-robot navigation problem in complex, dynamic, and uncertain environments. Initially, the safe probability field offers an innovative approach to model the uncertainty of external dynamic obstacles, combining it with an unconstrained optimization method to generate safe trajectories for multi-robot online. Subsequently, the extended state model predictive controller can accurately track these generated trajectories while considering the robots' inherent model constraints and state uncertainty, thus ensuring the practical feasibility of the planned trajectories. Simulation experiments show a success rate four times higher than that of state-of-the-art algorithms. Physical experiments demonstrate the method's ability to operate in real-time, enabling safe navigation for multi-robot in uncertain environments.
... Potential Functions and Operational Space Control: Potential functions [36] define attractive fields towards the goal and repulsive ones that push away from obstacles. Moving along the negative gradient of the sum of these fields provides the motion vector. ...
This paper reviews the large spectrum of methods for generating robot motion proposed over the 50 years of robotics research culminating in recent developments. It crosses the boundaries of methodologies, typically not surveyed together, from those that operate over explicit models to those that learn implicit ones. The paper discusses the current state-of-the-art as well as properties of varying methodologies, highlighting opportunities for integration.
... In this way, we open the mereogeometry environment to mobile robot navigation. The principal component of our approach is the mereological potential field, the counterpart of potential fields in robotics see [31], [32]. The crucial notion in navigation problem for formations of robots is the r-betweenness relation of Def. ...
Rough sets (RS)proved a thriving realm with successes inn many fields of ML and AI. In this note, we expand RS to RM - rough mereology which provides a measurable degree of uncertainty to those areas.
... To ensure the safety of human-robot cooperation, robots must have the ability of autonomous obstacle avoidance. Among traditional obstacle avoidance methods, the artificial potential field method [19] is a classic approach widely applied in various scenarios. However, this method is prone to getting trapped in local optima, which can limit its effectiveness. ...
... However, for a flexible connection, random motion unavoidably occurs during flight. Therefore, inspired by fuzzy PID algorithms and artificial potential field methods [29], this study establishes the Baseline Predictive Fuzzy PID controller (BPF-PID). It dynamically adjusts the proportional gain coefficient K p of the PID controller by setting fuzzy rules, as shown in Fig. 7. ...
In this paper, we investigate the problem of collaborative localization in search tasks in denied environments, particularly when traditional visual-inertial localization techniques reach their limits. A novel fusion localization method is proposed. It couples dual Payload-Carrying unmanned aerial vehicles (UAVs) using collaborative simultaneous localization and mapping (SLAM) techniques. This method aims to improve the system's search range and payload capacity. The paper utilizes SLAM technology to achieve self-motion estimation, reducing dependence on external devices. It incorporates a collaborative SLAM backend that provides the necessary information for system navigation, path planning, and motion control, ensuring consistent localization coordinates among the UAVs. Then, a joint localization optimization method based on Kalman filtering is introduced. By fusing the localization information from the visual sensors located beneath the UAVs and using the baseline variation between the 2 UAVs as a reference, the method employs a recursive prediction approach to jointly optimize the self-estimated states and the collaborative SLAM state estimates. Experimental validation demonstrates a 31.6% improvement in localization accuracy in complex tasks compared to non-fusion localization method. Furthermore, to address the cooperative trajectory tracking problem of UAVs after system path planning, a baseline-predicting fuzzy Proportional-Integral-Derivative flight controller is designed. Compared to conventional methods, this controller takes into account delays and system oscillations, achieving better tracking performance and dynamic adjustments.
Graphical abstract
... Path planning [8] 提出的一种虚拟力法， 由于这种方法 是为移动机器人提出的，因此它没有考虑障碍物 轮廓的尺寸、道路限制、行驶速度和适用于无人 驾驶车辆的其他限制 [9] 。有研究者通过在传统人工 势场中加入道路边界势场约束 [10] ，但其要求它们 处于静止状态和普通道路上；唐志荣等 [11] ...
Autonomous driving vehicles often encounter and avoid non structured roads within the park due to their small distance, making it difficult for them to meet smoothly. To address this problem, an improved artificial potential field approach is introduced. Firstly, the road boundary repulsive potential field and the lane centerline potential field are introduced to ensure that the vehicle will not drive out of the road boundary and drive back to the original road. Secondly, in order to solve the problem that it is difficult to realize the meeting of vehicles, temporary target points are set outside the collision range of the vehicle's forward direction and obstacles to avoid the inability to plan the path due to zero force in the situation. In addition, the dynamic repulsive potential field of obstacles is introduced, and according to the current position and velocity of the obstacle, the position of the obstacle can be predicted in the future period, and the obstacle velocity repulsion is combined with the traditional static repulsive field to obtain the final repulsive field. The simulation 作者简介：陈翔
... Initially created to improve productivity and safety in industrial settings, their scope has significantly broadened. From initially focusing on path planning for industrial manipulators [1], AMRs now use advanced algorithms to navigate without collisions. This expansion has allowed them to operate in diverse and dynamic environments beyond just industrial settings [2], [3]. ...
This paper introduces a novel classification for Autonomous Mobile Robots (AMRs), into three phases and five steps, focusing on autonomous collision-free navigation. Additionally, it presents the main methods and widely accepted technologies for each phase of the proposed classification. The purpose of this classification is to facilitate understanding and establish connections between the independent input variables of the system (hardware, software) and autonomous navigation. By analyzing well-established technologies in terms of sensors and methods used for autonomous navigation, this paper aims to provide a foundation of knowledge that can be applied in future projects of mobile robots.
... Among the various methods for preventing collisions, artificial potential fields (APFs) are some of the most traditional and were first presented in [14]. APFs have been utilized in multirobot (multiagent) systems to attain cooperative behaviors [15], such as flocking [16], [17], and formation control [18]. ...
This article proposes a collision avoidance method for ellipsoidal rigid bodies that utilizes a control barrier function (CBF) designed from a supporting hyperplane. We formulate the problem in the special Euclidean groups
and
, where the kinematics are described as rigid body motion (RBM). We consider the condition for separating two ellipsoidal rigid bodies by employing a signed distance from a supporting hyperplane of a rigid body to the other rigid body. Although a positive value of this signed distance implies that the two rigid bodies are collision-free, a naively designed supporting hyperplane yields a smaller value than the actual distance. To avoid such a conservative evaluation, the supporting hyperplane is rotated so that the signed distance from the supporting hyperplane to the other rigid body is maximized. We prove that the maximum value of this optimization problem is equal to the actual distance between two ellipsoidal rigid bodies, hence eliminating excessive conservativeness. We leverage this signed distance as a CBF to prevent collision while the supporting hyperplane is rotated via a gradient-based input. The designed CBF is integrated into a quadratic programming (QP) problem, where each rigid body calculates its collision-free input in a distributed manner, given communication among rigid bodies. We exemplify that our method can be extended to other kinematic models. The proposed method is demonstrated through simulation results.
... This route planning is commonly applied in scenarios such as ship encounters and obstacle avoidance. The implementation of local route planning typically includes algorithms such as the dynamic window approach (DWA) [6] and artificial potential field (APF) [7]. In addition, some scholars have proposed intelligent algorithms, such as neural networks (NN) [8]. ...
Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.
... This penalty is formulated using a repulsive potential 5 We used multiple shooting for the discretization to solve the boundary value problem with continuity constraints [24]. field approach [26], introducing a strong repulsive force when the UAV gets too close. The repulsive potential U i for obstacle i is defined as, ...
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in real-time. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.
... Moreover, the LSAP does not account for inter-robot collisions, which are crucial in multi-robot systems. Approaches often rely on online replanners or potential field methods [8], which can prevent collisions but often disregard the global optimality of the solution. ...
Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance, aiming to minimize the total distance traveled. The problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its k-nearest robots and k-nearest targets. This scenario combines elements of combinatorial assignment and continuous-space motion planning, posing significant scalability challenges for traditional centralized approaches. To overcome these challenges, we propose a decentralized policy learned via a Graph Neural Network (GNN). The GNN enables robots to determine (1) what information to communicate to neighbors and (2) how to integrate received information with local observations for decision-making. We train the GNN using imitation learning with the centralized Hungarian algorithm as the expert policy, and further fine-tune it using reinforcement learning to avoid collisions and enhance performance. Extensive empirical evaluations demonstrate the scalability and effectiveness of our approach. The GNN policy trained on 100 robots generalizes to scenarios with up to 500 robots, outperforming state-of-the-art solutions by 8.6\% on average and significantly surpassing greedy decentralized methods. This work lays the foundation for solving multi-robot coordination problems in settings where scalability is important.
... The pioneer algorithm in local path planning by Khatib (1990) is the artificial potential field (APF) algorithm. The algorithm drives the vehicle to the goal in an unknown environment by combining continuous attractive fields related to the goal-reaching task and repulsive fields generated by obstacles. ...
This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.
In the realm of marine aquaculture, the netting of cages frequently accumulates marine fouling, which impedes water circulation and poses safety hazards. Traditional manual cleaning methods are marked by inefficiency, high labor demands, substantial costs, and considerable environmental degradation. This paper initially presents the current utilization of net-cleaning robots in the cleaning, underwater inspection, and monitoring of aquaculture cages, highlighting their benefits in enhancing operational efficiency and minimizing costs. Subsequently, it reviews key technologies such as underwater image acquisition, visual recognition, adhesion-based movement, efficient fouling removal, motion control, and positioning navigation. Ultimately, it anticipates the future trajectory of net-cleaning robots, emphasizing their potential for intelligence and sustainability, which could drive the marine aquaculture industry towards a more efficient and eco-friendly era.
With the increasing complexity of ocean missions, using multiple unmanned underwater vehicles to collaborate in executing tasks has become an effective way to improve the overall efficiency of ocean operations. Current research on path planning for multiple unmanned underwater vehicles mainly focuses on the basis of particle models or fully known environmental information, while research directions mainly focus on single indicators such as completion time and energy consumption. This paper first constructs a UUV model and a task scenario with detection success rate as the objective function. Then, a parameterization method based on a spiral search path was proposed for designing variables. A hierarchical control strategy is designed to ensure handle formation constraints. A general optimization framework for task scenarios has been constructed and combined with algorithms to solve optimization problems. Finally, this study compared and analyzed the performance of different optimization algorithms under the optimization framework, evaluated the optimization results of different search strategies, and explored the impact of dynamic objectives on the detection success rate. The results showed that the optimized path had a search success rate that increased by more than 50% compared to the direct path and the cover search path, which verified the effectiveness of the proposed method and strategy.
Deep reinforcement learning (DRL)-based navigation in an environment with dynamic obstacles is a challenging task due to the partially observable nature of the problem. While DRL algorithms are built around the Markov property (assumption that all the necessary information for making a decision is contained in a single observation of the current state) for structuring the learning process; the partially observable Markov property in the DRL navigation problem is significantly amplified when dealing with dynamic obstacles. A single observation or measurement of the environment is often insufficient for capturing the dynamic behavior of obstacles, thereby hindering the agent’s decision-making. This study addresses this challenge by using an environment-specific heuristic approach to augment the dynamic obstacles’ temporal information in observation to guide the agent’s decision-making. We proposed Multichannel Cost Map Observation for Spatial and Temporal Information (M-COST) to mitigate these limitations. Our results show that the M-COST approach more than doubles the convergence rate in concentrated tunnel situations, where successful navigation is only possible if the agent learns to avoid dynamic obstacles. Additionally, navigation efficiency improved by 35% in tunnel scenarios and by 12% in dense-environment navigation compared to standard methods that rely on raw sensor data or frame stacking.
Unmanned Surface Vehicles (USVs) operating in complex traffic conditions in island reef waters often require different types of algorithms. Therefore, selecting a dynamic path-planning algorithm with strong adaptability has become a new challenge. This paper proposes a dynamic adaptive path planning algorithm for USVs, incorporating an improved Dynamic Window Approach (DWA) with fuzzy logic and the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is designed by integrating three key aspects: evaluation function, fuzzy control, and COLREGS. First, to enable USVs to approach the target point more safely and quickly during navigation, an additional target point attraction sub-function is introduced, extending the original evaluation function. Furthermore, to ensure robust dynamic path planning for USVs across various water environments, such as narrow channels, reef-laden waters, and open seas, fuzzy logic is integrated with the improved DWA algorithm. Since USVs must comply with COLREGS during navigation, the algorithm incorporates these regulations, enhancing the DWA algorithm with fuzzy logic to ensure compliance. Finally, simulation experiments validate the proposed algorithm, demonstrating that the planned paths are safer and more stable, ensuring the safe navigation of USVs in compliance with COLREGS.
To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.
It is essential to efficiently perform collision detection for robotic manipulators obstacle-avoidance planning. Existing methods are excellent when manipulator links are simple and obstacles are convex. But they cannot keep the accuracy and the efficiency at the same time when manipulator links or obstacles are nonconvex. To decrease the computing time and keep a high accuracy, this article presents a collision detection method based on point clouds and stretched primitives (PCSP). In traditional methods, obstacles are often represented either by a convex body or enormous amounts of points. But this needs a trade-off between the accuracy and the computing time when obstacles are concave. In the proposed method, we represent obstacles and complex manipulator links as stretched geometric bodies while simple manipulator links are enclosed by capsules with different sizes. The stretched body is constructed by the original point cloud from sensors but it only requires a small number of points to approximate the original object. We conducted the simulation experiment in our specific scenarios, and the results indicated that PCSP required less computing time while maintaining a high level of accuracy compared to existing methods. We also conducted standard benchmark tests in general scenarios, which showed that PCSP had advantages over libraries based on bounding volume hierarchies when concave objects are close together. Finally, we implemented PCSP for a manipulator obstacle-avoidance motion planning in a real-world environment, which demonstrated that PCSP was effective.
The current chapter presents an overview and state of the art in the field of collaborative robotics (commonly called cobotics), where several issues, challenges and contributions in the field are summarized. Therefore, the chapter addresses different aspects of collaborative robotics and its positioning in the context of the whole robotics research. A particular focus is made on physical Human-Robot Interaction (pHRI) where different aspects are addressed according to the level and frequency of physical interaction. It starts from the space sharing and collision avoidance, through the punctual collision, until the permanent physical interaction between humains and robots. Hence, the specific context of physical Human-Robot Collaboration (pHRC), defined as a key task of human-robot object co-manipulation, is deeply addressed. The importance of collaboration quality evaluation is addressed in this chapter as well, and how this field of pHRC can be considered as a part of the human-centered robotics field thanks to its impact on human beings.
ResearchGate has not been able to resolve any references for this publication.