Distributed Consensus Formation Control with Collision and Obstacle Avoidance for Uncertain Networked Omnidirectional Multi-robot Systems Using Fuzzy Wavelet Neural Networks

2016 ◽  
Vol 19 (5) ◽  
pp. 1375-1391 ◽  
Author(s):  
Ching-Chih Tsai ◽  
Hsiao-Lang Wu ◽  
Feng-Chun Tai ◽  
Yen-Shuo Chen
Keyword(s):  
Neural Networks ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Wavelet Neural Networks ◽  
Distributed Consensus ◽  
Consensus Formation ◽  
Robot Systems ◽  
Fuzzy Wavelet Neural Networks ◽  
Multi Robot

Dynamic model based formation control and obstacle avoidance of multi-robot systems

Robotica ◽  
2008 ◽  
Vol 26 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Celso De La Cruz ◽  
Ricardo Carelli
Keyword(s):  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Inverse Dynamics ◽  
Single Equation ◽  
Model Parameters ◽  
Robot System ◽  
System A ◽  
Robot Systems ◽  
Multi Robot

SUMMARYThis work presents, first, a complete dynamic model of a unicycle-like mobile robot that takes part in a multi-robot formation. A linear parameterization of this model is performed in order to identify the model parameters. Then, the robot model is input-output feedback linearized. On a second stage, for the multi-robot system, a model is obtained by arranging into a single equation all the feedback linearized robot models. This multi-robot model is expressed in terms of formation states by applying a coordinate transformation. The inverse dynamics technique is then applied to design a formation control. The controller can be applied both to positioning and to tracking desired robot formations. The formation control can be centralized or decentralized and scalable to any number of robots. A strategy for rigid formation obstacle avoidance is also proposed. Experimental results validate the control system design.

Multi-Robot Systems Formation Control with Obstacle Avoidance

2014 ◽  
Vol 47 (3) ◽  
pp. 5703-5708 ◽  
Author(s):  
Tiago P. Nascimento ◽  
Andre G.S. Conceicao ◽  
António Paulo Moreira
Keyword(s):  
Obstacle Avoidance ◽  
Formation Control ◽  
Robot Systems ◽  
Multi Robot

Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem

Robotica ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 549-567 ◽  
Author(s):  
Tiago P. Nascimento ◽  
André G. S. Conceição ◽  
António Paulo Moreira
Keyword(s):  
Obstacle Avoidance ◽  
Nonlinear Model ◽  
Formation Control ◽  
Potential Functions ◽  
Robot Systems ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Moving Obstacle ◽  
Avoidance Problem ◽  
Multi Robot

SUMMARYThis paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

scholarly journals Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Minghui Wang ◽  
Bi Zeng ◽  
Qiujie Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Learning Strategy ◽  
Planning Method ◽  
Unknown Environment ◽  
Robot Systems ◽  
Avoidance Control ◽  
Multi Robot

Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

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