Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

Cui, Peng; Yan, Weisheng; Cui, Rongxin; Yu, Jiahui

doi:10.1155/2018/4359036

Cited by 11 publications

(4 citation statements)

References 42 publications

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“…e increase of ω appropriately can improve the global search ability of the algorithm, and the decrease of ω improves the local search ability. erefore, a reinforcement learning mechanism [30,31] is integrated with PSO to overcome the shortcomings of the PSO algorithm and plan the optimal path rapidly.…”

Section: Pso Weight Updating Functionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Huang

Jin

2021

Complexity

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In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

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Section: Pso Weight Updating Functionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Huang

Jin

2021

Complexity

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“…Mobile manipulation robots combine lightweight industrial robotic arms with multiple degrees of freedom and intelligent mobile platforms, combining the mobility of mobile platforms and the manipulation ability of robotic arms [9][10]. Compared with traditional robots with a single function, mobile manipulator robots greatly expand the workspace and application scope [11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of automatic driving task serialisation monitoring for operating robots

Bai,

Ruan,

Peng

et al. 2024

Applied Mathematics and Nonlinear Sciences

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With the increasing application of industrial robots, more and more simple and repetitive operations in industrial automated production lines are replaced by industrial robots. In this study, a path planning algorithm is proposed for the autopilot task of an operating robot to find the optimal path from the start point to the goal point under the constraints of safe obstacle avoidance, shortest distance, and fastest time. Forward kinematics is used to detect whether it will collide with environmental obstacles until the first feasible result is detected as the end position for navigation. Serialized monitoring is used to obtain the activity information of each computational node, the hardware state information of the system, and the state of the autopilot task processing. Finally, obstacle avoidance navigation experiments on a mobile operating robot are conducted to verify the effectiveness and feasibility of the obstacle avoidance navigation algorithm. The results show that the maximum error of the autopilot task of the operating robot is 2.91cm, and the average error is 0.13 2cm, which is in line with the trajectory tracking error requirements of the operating machine control method and verifies the validity and practicability of this study.

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“…Therefore, how to build a mobile robot with an ideal path planner in a partially known or completely unknown environment has become a research hotspot (Bak et al, 2019;Mu et al, 2018). In general, the problem of path planning can be divided into two parts: offline global planning with known information, and online local path planning with unknown information (Cui et al, 2018;Zafar and Mohanta, 2018). For global path planning, the artificial potential field (APF) method provides a simple and feasible idea (Khatib, 1986), but the goal nonreachable with obstacle nearby (GNRON) problem and the local minimum defects limit its effect (Faridi et al, 2018;Li et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles

Lin

Yue

Chen

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper proposes a two-layer path-planning method, where an optimized artificial potential field (APF) method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively. This method enables the robot to plan a better path under a multi-obstacle environment while avoiding the moving obstacles effectively. For the part of global path planning, a new repulsive field is proposed based on the APF method. The length and smoothness of the global path are taken as fitness functions of particle swarm optimization (PSO) to obtain the obstacle influence range, the coefficients of gravitation and repulsion in APF. At the level of local path planning, on the basis of DWA, a fuzzy control scheme is adopted to evaluate the danger level of moving obstacles via collision risk index and relative distance. In brief, compared with existing methods, the robot can reasonably plan a shorter and smoother path with the aid of PSO-based APF, meanwhile quickly react to the moving obstacles and avoid them by fuzzy-based DWA. Finally, a static multi-obstacle environment and two dynamic scenarios with moving obstacles are simulated to verify the effectiveness of the proposed path-planning method.

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Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

Cited by 11 publications

References 42 publications

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Application of automatic driving task serialisation monitoring for operating robots

Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles

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