Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

One of the current challenges in the development of swarm robots is making them realize obstacle avoidance while keeping formation. This paper proposes a new obstacle avoidance method for multiple robots based on the aggregation and disaggregation approach. First, the formation-based multi-resolution models of swarm robots are presented and the formation attribute-based consistency mapping functions are designed. Then, obstacle avoidance behavior based on aggregation and disaggregation method is presented and the basic obstacle avoidance behaviors are designed. Finally, the simulation results are presented to show the feasibility of the proposed method.

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Formation Control and Obstacle Avoidance for Multiple Robots Subject to Wheel-Slip

International Journal of Advanced Robotic Systems ◽

10.5772/52768 ◽

2012 ◽

Vol 9 (5) ◽

pp. 188 ◽

Cited By ~ 9

Author(s):

Cai Ze-Su ◽

Zhao Jie ◽

Cao Jian

Keyword(s):

Obstacle Avoidance ◽

Formation Control ◽

Multiple Robots ◽

Wheel Slip

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Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

Journal of Marine Science and Engineering ◽

10.3390/jmse9020161 ◽

2021 ◽

Vol 9 (2) ◽

pp. 161

Author(s):

Xun Yan ◽

Dapeng Jiang ◽

Runlong Miao ◽

Yulong Li

Keyword(s):

Obstacle Avoidance ◽

Potential Field ◽

Formation Control ◽

Shape Change ◽

Artificial Potential Field ◽

Generation Algorithm ◽

Time Performance ◽

Movement Ability ◽

Window Approach ◽

Avoidance Problem

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

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Space robot motion path planning based on fuzzy control algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219094 ◽

2021 ◽

pp. 1-8

Author(s):

Baoyu Shi ◽

Hongtao Wu

Keyword(s):

Path Planning ◽

Fuzzy Control ◽

Obstacle Avoidance ◽

Control Algorithm ◽

Space Robot ◽

Robot Motion ◽

Robot Path Planning ◽

Behavior Based ◽

Fuzzy Control Algorithm ◽

Robot Path

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

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