scholarly journals Reactive Path Planning Approach for Docking Robots in Unknown Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Cui ◽  
Weisheng Yan ◽  
Yintao Wang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Autonomous Robots ◽  
Planning Problem ◽  
Long Distance ◽  
Unknown Environment ◽  
Work Area ◽  
Planning Approach ◽  
Feasible Path

Autonomous robots need to be recharged and exchange information with the host through docking in the long-distance tasks. Therefore, feasible path is required in the docking process to guide the robot and adjust its pose. However, when there are unknown obstacles in the work area, it becomes difficult to determine the feasible path for docking. This paper presents a reactive path planning approach named Dubins-APF (DAPF) to solve the path planning problem for docking in unknown environment with obstacles. In this proposed approach the Dubins curves are combined with the designed obstacle avoidance potential field to plan the feasible path. Firstly, an initial path is planned and followed according to the configurations of the robot and the docking station. Then when the followed path is evaluated to be infeasible, the intermediate configuration is calculated as well as the replanned path based on the obstacle avoidance potential field. The robot will be navigated to the docking station with proper pose eventually via the DAPF approach. The proposed DAPF approach is efficient and does not require the prior knowledge about the environment. Simulation results are given to validate the effectiveness and feasibility of the proposed approach.

scholarly journals Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2342 ◽  
Author(s):  
Pengwei Wang ◽  
Song Gao ◽  
Liang Li ◽  
Binbin Sun ◽  
Shuo Cheng
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Autonomous Driving ◽  
Field Method ◽  
Driving Safety ◽  
Artificial Potential Field ◽  
Field Range ◽  
Human Driver ◽  
Planning Approach

Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianying Xu ◽  
Haibo Zhou ◽  
Shuaixia Tan ◽  
Zhiqiang Li ◽  
Xia Ju ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Planning Process ◽  
Field Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Content Type ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

scholarly journals Improved Manipulator Obstacle Avoidance Path Planning Based on Potential Field Method

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ming Zhao ◽  
Xiaoqing Lv
Keyword(s):  
Path Planning ◽  
Success Rate ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Working Environment ◽  
Artificial Potential Field ◽  
End Point ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Aiming at the existing artificial potential field method, it still has the defects of easy to fall into local extremum, low success rate and unsatisfactory path when solving the problem of obstacle avoidance path planning of manipulator. An improved method for avoiding obstacle path of manipulator is proposed. First, the manipulator is subjected to invisible obstacle processing to reduce the possibility of its own collision. Second, establish dynamic virtual target points to enhance the predictive ability of the manipulator to the road ahead. Then, the artificial potential field method is used to guide the manipulator movement. When the manipulator is in a local extreme or oscillating, the extreme point jump-out function is used in real time to make the end point of the manipulator produce small displacements and change the action direction to effectively jump out of the dilemma. Finally, the manipulator is controlled to avoid all obstacles and move smoothly to form a spatial optimization path from the start point to the end point. The simulation experiment shows that the proposed method is more suitable for complex working environment and effectively improves the success rate of manipulator path planning, which provides a reference for further developing the application of manipulator in complex environment.

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