Path planning for manipulators based on an improved probabilistic roadmap method

Chen, Gang; Luo, Ning; Liú, Dan; Zhao, Zhihui; Liang, Changchun

doi:10.1016/j.rcim.2021.102196

Cited by 60 publications

(13 citation statements)

References 18 publications

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“…The algorithm adds visual constraints to the probabilistic roadmap, which avoids collision with obstacles while ensuring that the target is within the field of view of visual sensors. Chen et al proposed a PRM sampling strategy based on virtual force field in 2021 [62], which improved the performance of PRM algorithm in narrow passage scenarios, enabling end-effector to effectively avoid obstacles in chaotic environments.…”

Section: Random Sampling-based Grasping Path Planning Algorithmsmentioning

confidence: 99%

Survey of the key technologies of manipulator target grasping

Huang¹,

Yang²,

Xie³

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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At present, the object grasping system of robot based on vision is one of the research hotspots, and it has a broad application prospect in industry, especially in the part of sorting. The key technologies of this system include robot hand-eye calibration, grasping target detection, grasping path planning, etc. This paper provides an overview about the current status of these key technologies by collating relevant literature, including the four kinds of hand-eye calibration methods, deep learning based grasping target detection algorithms, and the advantages and disadvantages of three kinds of grasping path planning methods. In this paper, the key technologies of vision-based robot object grasping system are summarized and prospected, which can be helpful to researchers in related fields.

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Section: Random Sampling-based Grasping Path Planning Algorithmsmentioning

confidence: 99%

Survey of the key technologies of manipulator target grasping

Huang¹,

Yang²,

Xie³

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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“…The experiments in this paper are based on this equipment. In the experiment section, we compare Q-learning with the A* algorithm [34], PRM [35], RRT [36], and BRRT(bidirectional rapid exploring random trees) [37]. The parameters of the comparison algorithm are shown in Table 2.…”

Section: Experiments a Test Environment And Parameters Settingsmentioning

confidence: 99%

Improved Q-Learning Applied to Dynamic Obstacle Avoidance and Path Planning

2022

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Due to the complexity of interactive environments, dynamic obstacle avoidance path planning poses a significant challenge to agent mobility. Dynamic path planning is a complex multi-constraint combinatorial optimization problem. Some existing algorithms easily fall into local optimization when solving such problems, leading to defects in convergence speed and accuracy. Reinforcement learning has certain advantages in solving decision sequence problems in complex environments. A Q-learning algorithm is a reinforcement learning method. In order to improve the value evaluation of the algorithm in solving practical problems, this paper introduces the priority weight into the Q-learning algorithm. The improved algorithm is compared with existing algorithms and applied to dynamic obstacle avoidance path planning. Experiments show that the improved algorithm dramatically improves the convergence speed and accuracy and increases the value evaluation. The improved algorithm finds the shortest path of 16 units in 27 seconds.

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“…However, they did not differentiate between static and dynamic obstacles, resulting in higher path curvature and increased overall path length. Chen et al preserved key waypoints in global planning to overcome the issue of the dynamic window approach becoming trapped in local optima 9 . They achieved optimization in terms of path length and smoothness.…”

Section: Introductionmentioning

confidence: 99%

Research on path planning algorithm of inspection robot based on improved A* and dynamic window approach

Zhang,

Meng,

Ouyang

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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To address the current issues of path planning and limited applicability in patrol robots, an enhancement has been made to the existing A* algorithm, coupled with the utilization of the dynamic window approach, to design a global dynamic path planning method suitable for patrol robots. This approach involves introducing dynamic weights to enhance search efficiency, identifying collinearity among global path nodes to eliminate redundant points, and conducting obstacle detection between essential nodes to enhance path smoothness. An autonomous navigation algorithm is proposed, wherein the path generated by the A* algorithm is adopted as the global path. The essential nodes within this path are treated as local target points. By adjusting the initial pose angle and optimizing the weight coefficients of the evaluation function, the autonomous navigation algorithm’s optimization efficiency and obstacle avoidance capability against random obstacles are improved. Comparative simulation experiments are conducted to validate its robust performance in terms of planning efficiency, path length, and random obstacle avoidance. By establishing a ROS robot operating platform and conducting experiments in both static and dynamic environments, the autonomous navigation algorithm is shown to successfully plan superior paths and achieve real-time obstacle avoidance to reach the target point.

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Path planning for manipulators based on an improved probabilistic roadmap method

Cited by 60 publications

References 18 publications

Survey of the key technologies of manipulator target grasping

Survey of the key technologies of manipulator target grasping

Improved Q-Learning Applied to Dynamic Obstacle Avoidance and Path Planning

Research on path planning algorithm of inspection robot based on improved A* and dynamic window approach

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