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 System for Manipulators and Mobile Robots
Abstract
This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an upper speed limit. The artificial potential field ap proach has been extended to collision avoidance for all ma nipulator links. In addition, a joint space artificial potential field is used to satisfy the manipulator internal joint con straints. This method has been implemented in the COSMOS system for a PUMA 560 robot. Real-time collision avoidance demonstrations on moving obstacles have been performed by using visual sensing.
... Wang et al. [10] proposed an improved RRT* algorithm, including dynamic step size, steering angle constraints, and optimal tree reconnection. The artificial potential field method [11] is a common algorithm for local path planning. The algorithm assumes that the subject is moving under a virtual force field, the target point exerts gravity on the subject to guide the object to move towards it, and the obstacle exerts repulsive force on the subject to avoid collision with it. ...
... In short, given the initial constraints q 0 at t 0 and end constraints q f at t f , fixed a 6 and b 6 give a path, satisfying the constraints mentioned above, which is described in Equation (11). The following algorithm optimizes a 6 and b 6 to obtain a path. ...
In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which makes it difficult to realize the plan, thus affecting the performance of the path tracking controller. In this paper, a real-time path planning algorithm based on particle swarm optimization for an agricultural machinery parametric kinematic model is proposed. The algorithm considers the agricultural machinery kinematic model, defines the path satisfying the kinematic model through a parametric equation, and solves the initial path through the analytic method. Then, considering the constraints of obstacles, acceleration, and turning angle, two objective functions are proposed. The particle swarm optimization algorithm is used to search the path near the initial path which satisfies the obstacle avoidance condition and has a better objective function value. In addition, the influence of the algorithm parameters on the running time is analyzed, and the method of compensating the radius of the obstacle is proposed to compensate the influence of the discrete time on the obstacle collision detection. Finally, experimental results show that the algorithm can plan a path in real time that avoids any moving obstacles and has a better objective function value.
... It performs path planning in real-time, which belongs to the dynamic planning algorithm [8]. In 1986, Khatib [9] proposed the Artificial Potential Field (APF) method as a solution for obstacle avoidance issues. This method refers to constructing a repulsive potential field near the obstacle to deter the robot from getting too close and a gravitational potential field near the target point to give the robot an attraction force, and the robot moves towards the target point under the joint action of the two forces. ...
In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.
... Khatib [33] introduced a pioneering methodology known as the artificial potential field (APF) algorithm in 1986. The methodology involves manually simulating an electric potential field within the operational environment. ...
As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates remote sensing information to overcome traditional APF challenges and combines the IAPF with the traveling salesman problem for optimal path cruising. Through a combination of DRL and multi-source data optimization, the approach ensures minimal energy consumption across all target points. Inertial sensors further refine trajectory, ensuring smooth navigation and precise positioning. The comparative experiments confirm the method’s energy efficiency, trajectory refinement, and safety excellence.
... This article presents a further development of infotaxis-based GSL for multi-robot systems. We extend here our previous work [17], where we proposed a potential-field-based method [18][19][20] for navigating a swarm of robots toward locations with high expected information gain. The model used in [17] is a simplified description of the gas propagation based on Poisson's equation. ...
Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources using concentration measurements, robots need to seek out informative sampling locations. For this, domain knowledge needs to be incorporated into their exploration strategy. We achieve this by means of partial differential equations incorporated into a probabilistic gas dispersion model that is used to generate a spatial uncertainty map of process parameters. Previously, we presented a potential-field-control approach for navigation based on this map. We build upon this work by considering a more realistic gas dispersion model, now taking into account the mechanism of advection, and dynamics of the gas concentration field. The proposed extension is evaluated through extensive simulations. We find that introducing fluctuations in the wind direction makes source localization a fundamentally harder problem to solve. Nevertheless, the proposed approach can recover the gas source distribution and compete with a systematic sampling strategy. The estimator we present in this work is able to robustly recover source candidates within only a few seconds. Larger swarms are able to reduce total uncertainty faster. Our findings emphasize the applicability and robustness of robotic swarm exploration in dynamic and challenging environments for tasks such as gas source localization.
... The artificial potential field algorithm proposed in [41] assumes the presence of a repulsive field around the obstacle and an attractive field around the target, which influences the moving autonomous underwater vehicle. As a result of the overlap of these virtual fields, the resultant force is determined, defining the vehicle's direction of movement. ...
The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.
... and the repulsive potential of the obstacle in the map. The path could be determined by the change in the gradient of the potential field [15]. However, there might be regions in the map where the potential field is in equilibrium, causing the UAVs to fall into local minima [16]. ...
This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs.
... The Dynamic A* (D*) algorithm is a dynamic planning algorithm proposed by Stentz et al., based on the A* algorithm, which can quickly replan part of the local path when the environment changes without replanning the whole path, thus reducing the computational effort and meeting real-time requirements. Khatib was the first to propose the artificial potential field (APF) method [18] and to apply it to path planning. The artificial potential field method is easy to calculate, more effective in real time, safer, and does not collide with obstacles [19]. ...
Due to the increased employment of robots in modern society, path planning methods based on human-robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function's sub-functions and enhance the algorithm's performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique's ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.
... Traditional obstacle avoidance planning methods APF (artificial potential field method) (Khatib, 1986), A * (Hart et al., 1968) The principle is simple and easy to implement ...
Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.
... Path planning aims to find a feasible trajectory connecting the starting point and the target point while avoiding collisions with obstacles [9], and has become one of the key technologies for the autonomous operation of inspection robots. Currently, the common path planning algorithms mainly include the A* algorithm, the artificial potential field (APF) method, the rapidly exploring random tree algorithm [10][11][12], and so on. The A* algorithm introduces heuristic functions to search for paths, the advantages of which include path optimality and high searching efficiency. ...
In order to improve the safety and efficiency of inspection robots for solar power plants, the Rapidly Exploring Random Tree Star (RRT*) algorithm is studied and an improved method based on an adaptive target bias and heuristic circular sampling is proposed. Firstly, in response to the problem of slow search speed caused by random samplings in the traditional RRT* algorithm, an adaptive target bias function is applied to adjust the generation of sampling points in real-time, which continuously expands the random tree towards the target point. Secondly, to solve the problem that the RRT* algorithm has a low search efficiency and stability in narrow and long channels of solar power plants, the strategy of heuristic circular sampling combined with directional deviation is designed to resample nodes located on obstacles to generate more valid nodes. Then, considering the turning range of the inspection robot, our method will prune nodes on the paths that fail to meet constraint of the minimum turning radius. Finally, the B-spline curve is used to fit and smooth the path. A simulation experiment based on the environment of solar power plant is conducted and the result demonstrates that, compared with the RRT*, the improved RRT* algorithm reduces the search time, iterations, and path cost by 62.06%, 45.17%, and 1.6%, respectively, which provides a theoretical basis for improving the operational efficiency of inspection robots for solar power plants.
... The potential field method (PFM), the velocity obstacle method (VOM), and the dynamic window approach (DWA) are three reaction-based algorithms that are widely used in engineering and manufacturing. The PFM [13] uses the gradient of a potential field to move the robot from an initial to a target point. The VOM [14] relies on current positions and velocities of robots and obstacles to calculate a reachable avoidance velocity (RAV) space. ...
An end-to-end approach to autonomous navigation that is based on deep reinforcement learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic (AC) frameworks, namely, deep deterministic policy gradient (DDPG) and twin-delayed DDPG (TD3), are employed to enable a nonholonomic wheeled mobile robot (WMR) to perform navigation in dynamic environments containing obstacles and for which no maps are available. A comprehensive reward based on the survival penalty function is introduced; this approach effectively solves the sparse reward problem and enables the WMR to move toward its target. Consecutive episodes are connected to increase the cumulative penalty for scenarios involving obstacles; this method prevents training failure and enables the WMR to plan a collision-free path. Simulations are conducted for four scenarios—movement in an obstacle-free space, in a parking lot, at an intersection without and with a central obstacle, and in a multiple obstacle space—to demonstrate the efficiency and operational safety of our method. For the same navigation environment, compared with the DDPG algorithm, the TD3 algorithm exhibits faster numerical convergence and higher stability in the training phase, as well as a higher task execution success rate in the evaluation phase.
... Artificial potential field, abbreviated as APF was first proposed by Khatib et al. [9]. It is a wellrecognized grid-based motion planning approach that is used nowadays as a reactive planner for obstacle avoidance. ...
Autonomous Vehicle Motion Planning Artificial Potential Field Path Planning Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of traditional APF approach have given answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve, the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analysed and evaluated. At the end, the results of the review are discussed and future works are proposed.
Path planning is a crucial aspect of agent (robot) automation, as its efficacy directly affects the quality of tasks performed. This paper proposes an innovative and efficient path planning method by merging the advantages of the A-STAR and the artificial potential field (APF) technique. The proposed method of calculation aims to enhance the traditional A-STAR approach by incorporating an artificial potential field system. Specifically, the estimated cost in the conventional A-STAR approach is ameliorated through the integration of a precisely designed estimated cost gain. The gain is determined by the direction of the force in the artificial potential field., thus enabling the algorithm to focus more accurately on the direction of the target location while avoiding obstacles during path exploration. Through simulation results, the improved algorithm proves its feasibility by maintaining the same path length while reducing the running time by decreasing multiple exploration directions. The improved algorithm, which is more efficient, surpasses the conventional A-STAR and can be utilized for agent trajectory planning in static and complex environments, showcasing its superior performance.
Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.
Close human-robot interaction enables the combination of complementary abilities of humans and robots, thereby promoting efficient manufacturing. Human safety is an important aspect of human-robot interaction. To this end, the robot executes an evasive motion for collision avoidance when the human approaches. However, the evasive motion may be inconsistent with the robot's task resulting in unresumable task failure. In this letter, a control framework is proposed to achieve guaranteed human safety and hierarchical task consistency. First, the high-order time-varying control barrier function (HO-TV-CBF) is proposed to keep a safe distance between human and robot, thereby guaranteeing human safety. Next, to achieve hierarchical task consistency, a hard constraint and a soft constraint are defined systematically. The hard constraint ensures primary task consistency that keeps the task resumable, while the soft constraint together with the hard constraint ensures full task consistency. Finally, two quadratic programs (QPs) are employed to coordinate different control objectives, i.e., human safety and primary task consistency are always guaranteed while full task consistency is ensured whenever possible. Experiments are conducted to validate the proposed control framework with comparisons to existing methods.
In recent years, the robot industry has developed rapidly, and researchers and enterprises have begun to pay more attention to this industry. People are barely familiar with climbing robots, a kind of special robot. However, from their practical value and scientific research value, climbing robots should studied further. This paper analyzes and summarizes the key technologies of climbing robots, introduces various kinds of climbing robots, and examines their advantages and disadvantages to provide a reference for future researchers. Many countries have studied climbing robots and made some achievements. However, due to the complexity of climbing robots, their climbing efficiency and accuracy need to be further improved. The new structure can improve the climbing efficiency. This paper analyzes climbing robots such as mechanical arms, magnetic attraction, and claws. Optimization methods and path planning can improve the accuracy of work. This paper involves some control methods, including complex intelligent control methods such as behavior-based robot control. This paper also investigates various kinematic planning methods and expounds and summarizes various path planning algorithms, including machine learning and reinforcement learning of artificial intelligence, ant colony algorithm, and other algorithms. Therefore, by analyzing the research status of climbing robots at home and abroad, this paper summarizes three important aspects of climbing robots, namely, structural design, control methods, and climbing strategies, elaborates on the achievements and existing problems of these key technologies, and looks forward to the future development trend and research direction of climbing robots.
This paper presents Ex-RRT*, a modification of the rapidly-exploring random tree star (RRT*) algorithm that allows the robot to avoid obstacles with a margin. RRT* generates the shortest path to a destination while avoiding obstacles. However, if the robot’s embedded trajectory generation algorithm interpolates the waypoints generated by the RRT* to make a motion, collisions may occur with the edges or overhang of obstacles. This algorithm adds a cost function for the distance from each node to the nearest obstacle to ensure that the waypoints generated by the path planner have an appropriate margin for obstacles. It is designed to provide safer control from collisions when each robot’s embedded trajectory generation algorithm operates by interpolating waypoints derived by path planning. Through simulation, we compare the proposed Ex-RRT* and conventional RRT* with performance indices such as total distance traveled and collision avoidance norm. Experiments are conducted on the task of moving an object inside a box with a commercial robot to validate the proposed algorithm. The proposed algorithm generates paths with improved safety and can be applied to various robotic arms and mobile platforms.
This paper utilizes the distributed event-triggered impulsive control (ETIC) scheme to solve the consensus formation problem of leader-follower multi-agent systems (MASs) subject to obstacles. In order to solve the problem of the high cost of continuous control, an impulsive control strategy combined with the event triggering function is applied. By Lyapunov theory, the sufficient conditions for the strategy to achieve global exponential consensus are obtained, which implies that the formation of the system can be completed successfully. This research proposes a novel event-triggered condition which reduces the system’s energy loss when performing formation tasks. The proposed scheme not only realizes the system formation but also avoids the occurrence of Zeno behavior. Furthermore, the influence of the external environment with obstacles to consensus formation is considered. An improved artificial potential field (APF) function is proposed, which enables the MASs to avoid obstacles during the formation process. Finally, the effectiveness of the proposed method is verified by simulations.
This paper proposes a hybrid algorithm to complete path planning and dynamic obstacle avoidance in complicated maps for mobile robot. The hybrid algorithm (A*-DWA-B) combines the advantages of A* algorithm and Dynamic Window Approach (DWA). Firstly, methods of environmental modeling and collision detection are set. The improvement of A* algorithm lies in the establishment of a new calculation method for the evaluation function. After adding the risky cost, the parent node information is introduced into the calculation of the estimated cost, and the influence of the robot starting and braking modes is added to the calculation of the actual cost. Secondly, after removing superfluous nodes, the path obtained by the improved A* algorithm is divided into several linear segment paths. Then the endpoints of each line segment path are taken as the start node and target node of DWA for path planning. Adaptive initial attitude is set and two dynamic obstacle avoidance strategies are added for DWA. After integrating the paths planned by DWA, the B-spline smoothing method is used to optimize the integrated path, and finally obtained a smooth path. Compared with other similar algorithms, the proposed algorithm has advantages in path cost and turning angle. Experimental results show that the hybrid algorithm not only has strong ability of safe and smooth path planning, but also can avoid dynamic obstacles in time and effectively.
Nonlinear control-affine systems described by ordinary differential equations with time-varying vector fields are considered in the paper. We propose a unified control design scheme with oscillating inputs for solving the trajectory tracking and stabilization problems under the bracket-generating condition. This methodology is based on the approximation of a gradient-like dynamics by trajectories of the designed closed-loop system. As an intermediate outcome, we characterize the asymptotic behavior of solutions of the considered class of nonlinear control systems with oscillating inputs under rather general assumptions on the generating potential function. These results are applied to examples of nonholonomic trajectory tracking and obstacle avoidance.
The Adaptive Collision Avoidance Algorithm Based on the Estimated Collision Time (ACACT) is proposed in this paper, representing a novel approach designed for effective and efficient collision avoidance and path planning in highly dynamic environments, notably those with swarm Unmanned Aerial Vehicles (UAVs). The fundamental challenge in swarm UAV operations revolves around dynamic collision avoidance and nimble path planning. Addressing this, the ACACT algorithm exhibits the capability of predicting imminent collisions by estimating their likely occurrence times and then adeptly adjusting the UAVs’ trajectories in real-time. A significant facet of the algorithm is the employment of adaptive target velocity, updated in accordance with the predicted collision timelines. This ensures not only that UAVs can sidestep potential collisions but also that they can pursue more direct and efficient routes in comparison to conventional methodologies. Highlighting its superiority over existing techniques, the ACACT algorithm successfully resolves some long-standing issues linked with the Artificial Potential Field (APF) method, especially concerning unreachability and oscillation. This is accomplished by integrating a strategic contingency plan coupled with enhanced obstacle navigation, particularly in proximity to target locations. For a comprehensive evaluation of the algorithm’s prowess in collision avoidance and path planning, a novel metric named the Path Traveling Time Ratio (PTTR) is introduced. PTTR assesses both the traveling time taken for a vehicle to reach its target position and the duration it spends within collision-prone zones. This metric offers a more advanced evaluation method than merely comparing path lengths, collision counts, or traveling times. Through rigorous experimentation, it is observed that the ACACT algorithm enhances collision avoidance and path planning by an impressive margin of up to 20% compared to its traditional counterparts. Furthermore, a distinct advantage of the ACACT is its ability to uniformly tackle obstacles, irrespective of their speeds and independent of PID gain variations. It not only boosts the safety parameters but also amplifies operational efficiency, setting new benchmarks for UAVs to reach their target points with swiftness and security.
Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.
This chapter addresses the layered formation-containment (LFC) problem for multi-spacecraft in the constrained space with a directed communication topology. The formation-containment problem is first defined using a layered framework, and a layered distributed finite-time estimator is proposed to acquire the target states for spacecraft in each layer. Based on the proposed framework, the formation configuration and the mechanism of the information flow can be explored and designed naturally. To avoid collisions with borders, obstacles, as well as the other spacecraft in the constrained space, an artificial potential function is designed based on the Dirac delta function. Further, a disturbance observer and adaptive neural networks (NNs) are applied to respectively tackle the external disturbance and the model uncertainties. The desired formation of eachs layer can be achieved while no collision occurs in the constrained space. The semi-global uniform ultimate boundedness of closed-loop errors is guaranteed by Lyapunov stability theory. Simulation results are given to show the effectiveness of the proposed approaches.
Snake robot is a robot that can move freely in a small and complex space with super-redundant DOF and motion flexibility, and it has been proven to have a wide range of application prospects. RGB-D camera is a new type of visual sensor that can directly obtain color images and depth information. To enable the snake robot to work in some special task environments, we developed an orthogonal modular snake robot using RGB-D cameras, and conducted research on snake robot map building and path planning. An improved Batch Informed Tree (BIT*) algorithm is proposed. Finally, to verify the effectiveness of the proposed method, a corresponding experimental program is designed and related experimental analysis is carried out. The results show that our designed snake robot can complete the task of map building and path planning.
This paper contributes with a multi-level, hierarchical control system for a fleet of mobile robots sharing a common 2D motion space. The system consists of three levels, with the top level being a supervisor based on a discrete representation of the Multiple Mobile Robot System (MMRS), in which robot motion processes are seen as sequences of stages. The supervisor controls centrally the changes of their stages by robots, ensuring their collision-, and deadlock-free concurrent movement. The intermediate control level supervises locally the execution of robot motion on individual stages in a manner consistent with the decisions of the top level. The lowest level, robot control, is responsible for motion execution as determined by the local supervisor. We capitalize on some earlier results concerning the supervisory control of MMRS and propose a common framework for three supervisory control models. Then we propose relevant solutions for the local supervisors, in particular, a DES-based robot-motion-mode control and application of the Artificial Potential Field model for ensuring collision-free motion of two robots sharing a space sector. Next we assume simple robot control and subject the system to simulation experiments aimed at comparing the impact of the different solutions on the performance of MMRS.
Soft robot deformations are typically estimated using strain sensors to infer change from a nominal shape while taking a robot‐specific mechanical model into account. This approach performs poorly during buckling and when material properties change with time, and is untenable for shape‐changing robots that don't have a well‐defined resting (unactuated) shape. Herein, these limitations are overcome using stretchable shape sensing (S3) sheets that fuse orientation measurements to estimate 3D surface contours without making assumptions about the underlying robot geometry or material properties. The S3 sheets can estimate the shape of target objects to an accuracy of ≈3 mm for an 80 mm long sheet. The authors show the S3 sheets estimating their shape while being deformed in 3D space and also attached to the surface of a silicone three‐chamber pneumatic bladder, highlighting the potential for shape‐sensing sheets to be applied, removed, and reapplied to soft robots for shape estimation. Finally, the S3 sheets detecting their own stretch up to 30% strain is demonstrated. The approach introduced herein provides a generalized method for measuring the shape of objects without making strong assumptions about the objects, thus achieving a modular, mechanics model‐free approach to proprioception for wearable electronics and soft robotics.
Autonomous robotic path planning in partially known environments, such as warehouse robotics, deals with static and dynamic constraints. Static constraints include stationary obstacles, robotic and environmental limitations. Dynamic constraints include humans, robots and dis/appearance of anticipated dangers, such as spills. Path planning consists of two steps: First, a path between the source and target is generated. Second, path segments are evaluated for constraint violation. Sampling algorithms trade memory for maximal map representation. Optimization algorithms stagnate at non-optimal solutions. Alternatively, detailed grid-maps view terrain/structure as expensive memory costs. The open problem is thus to represent only constraint-free, navigable regions and generating anticipatory/reactive paths to combat new constraints. To solve this problem, a Constraint-Free Discretized Manifolds-based Path Planner (CFDMPP) is proposed in this paper. The algorithm’s first step focuses on maximizing map knowledge using manifolds. The second uses homology and homotopy classes to compute paths. The former constructs a representation of the navigable space as a manifold, which is free of apriori known constraints. Paths on this manifold are constraint-free and do not have to be explicitly evaluated for constraint violation. The latter handles new constraint knowledge that invalidate the original path. Using homology and homotopy, path classes can be recognized and avoided by tuning a design parameter, resulting in an alternative constraint-free path. Path classes on the discretized constraint-free manifold characterize numerical uniqueness of paths around constraints. This designation is what allows path class characterization, avoidance, and querying of a new path class (multiple classes with tuning), even when constraints are simply anticipatory.
As a classical path planning algorithm for mobile robots, artificial potential field method still has some flaws in some application scenarios. In this paper, we improve the artificial potential field method from two aspects to deal with these problems. The hyperbolic tangent function of the distance from the target to the robot is used to improve the repulsive force field to tackle the problem of the unreachable target point near obstacles. A behavior-based strategy based on collision cone is proposed to cope with the local minima problem. Simulation results prove that the improved method is effective.
The interest in using reinforcement learning (RL) controllers in safety-critical applications such as robot navigation around pedestrians motivates the development of additional safety mechanisms. Running RL-enabled systems among uncertain dynamic agents may result in high counts of collisions and failures to reach the goal. The system could be safer if the pre-trained RL policy was uncertainty-informed. For that reason, we propose
conformal predictive safety filters
that: 1) predict the other agents' trajectories, 2) use statistical techniques to provide uncertainty intervals around these predictions, and 3) learn an additional safety filter that closely follows the RL controller but avoids the uncertainty intervals. We use conformal prediction to learn uncertainty-informed predictive safety filters, which make no assumptions about the agents' distribution. The framework is modular and outperforms the existing controllers in simulation. We demonstrate our approach with multiple experiments in a collision avoidance gym environment and show that our approach minimizes the number of collisions without making overly conservative predictions.
Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.
Free space is represented as a union of (possibly overlapping) generalized cones. An algorithm is presented which efficiently finds good collision-free paths for convex polygonal bodies through space littered with obstacle polygons. The paths are good in the sense that the distance of closest approach to an obstacle over the path is usually far from minimal over the class of topologically equivalent collision-free paths. The algorithm is based on characterizing the volume swept by a body as it is translated and rotated as a generalized cone, and determining under what conditions one generalized cone is a subset of another. 8 references.
Cover title: The application of continuum methods to path planning, generation, manipulators, obstacle avoidance, intersection. Thesis (Ph. D.)--Stanford University, 1985. Includes bibliographical references (p. 220-226). Photocopy.
This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.
Fonction decision-commande d’un robot manipulateur. Rapport No
- O Khatib
- M Llibre
- R Mampey
Robotics in three acts-Part II (Film)
- O Khatib
- J Burdick
Système de navigation pour un robot mobile autonome: modélisation et processus décisionnels
- R Chatila
Methods for collision avoidance in computer controlled industrial robots
- H B Kuntze
- W Schill