scholarly journals The Application of Leader Following Method and Cubic Polynomial Path Planning Algorithm with Formation Control on Multi-Robot Systems

2021 ◽  
Author(s):  
Nuri Efe TATLI ◽  
Pınar OĞUZ EKİM ◽  
Dilara FİDAN ◽  
Beyzanur KALAYCI ◽  
Cem ÇEBER
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Robot Systems ◽  
Leader Following ◽  
Planning Algorithm ◽  
Cubic Polynomial ◽  
Path Planning Algorithm ◽  
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 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.

scholarly journals Multi-Robot Path Planning Method Using Reinforcement Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3057 ◽  
Author(s):  
Hyansu Bae ◽  
Gidong Kim ◽  
Jonguk Kim ◽  
Dianwei Qian ◽  
Sukgyu Lee
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Robot Path Planning ◽  
Q Learning ◽  
Efficient Performance ◽  
Planning Algorithm ◽  
Neural Network Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path ◽  
Multi Robot

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

Multi-Robot Systems

2012 ◽  
pp. 259-281
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Map Building ◽  
Multiple Robots ◽  
Robot Path Planning ◽  
Robotic Swarms ◽  
Robot Systems ◽  
Robot Task ◽  
Robot Path ◽  
Multi Robot

While the concepts of robotics and planning may be easily understood by the taking a single robot, it is not necessary that the problems we solve have a single robot in the planning scenario. In this chapter, the authors present systems with multiple robots, each robot attempts to coordinate and cooperate with the other robots for problem solving. The authors first look at the specific problems where multiple robots would be a boon for the system. This includes problems of maze solving, complete coverage, map building, and pursuit evasion. The inclusion of multiple robots in the scenario takes all the concepts of single robotic systems. It also introduces some new concepts and issues as well. They look into all these issues in the chapter which include optimality in terms of computational time and solution generated, completeness of planning, reaching a consensus, cooperation amongst multiple robots, and means of communication between robots for effective cooperation. These issues are highlighted by specific problems. The problems include multi-robot task allocation, robotic swarms, formation control with multiple robots, RoboCup, multi-robot path planning, and multi-robot area coverage and mapping. The authors specifically take the problem of multi-robot path planning, which is broadly classified under centralized and decentralized approaches. They discuss means by which algorithms for single robot path planning may be extended to the use of multiple robots. This is specifically done for the graph search, evolutionary, and behavioral approaches discussed in the earlier chapters of the book.

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