A Coordinated Path Planning Algorithm for Multi-Robot in Intelligent Warehouse*

2019 ◽  
Author(s):  
Xin Chen ◽  
Yanjie Li ◽  
Lintao Liu
Keyword(s):  
Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Coordinated Path Planning ◽  
Multi Robot

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.

scholarly journals Optimizing Coverage Performance of Multiple Random Path-planning Robots

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Md Ahsan Habib ◽  
M.S. Alam ◽  
N.H. Siddique
Keyword(s):  
Path Planning ◽  
Planning Problem ◽  
Coverage Path Planning ◽  
Simulated Environment ◽  
Planning Algorithm ◽  
Multi Agent ◽  
And Performance ◽  
Unknown Area ◽  
Path Planning Algorithm ◽  
Multi Robot

AbstractThis paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

A complete multi-robot path-planning algorithm

2018 ◽  
Vol 32 (5) ◽  
pp. 693-740 ◽  
Author(s):  
Ebtehal Turki Saho Alotaibi ◽  
Hisham Al-Rawi
Keyword(s):  
Path Planning ◽  
Robot Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path ◽  
Multi Robot

scholarly journals Decentralized Path Planning for Multi-Objective Robot Swarm System

2021 ◽  
Vol 2113 (1) ◽  
pp. 012002
Author(s):  
Zhuokai Wu
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Planning Problem ◽  
Robot Swarm ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
Multi Robot

Abstract The multi-robot path planning aims to explore a set of non-colliding paths with the shortest sum of lengths for multiple robots. The most popular approach is to artificially decompose the map into discrete small grids before applying heuristic algorithms. To solve the path planning in continuous environments, we propose a decentralized two-stage algorithm to solve the path-planning problem, where the obstacle and inter-robot collisions are both considered. In the first stage, an obstacle- avoidance path-planning problem is mathematically developed by minimizing the travel length of each robot. Specifically, the obstacle-avoidance trajectories are generated by approximating the obstacles as convex-concave constraints. In the second stage, with the given trajectories, we formulate a quadratic programming (QP) problem for velocity control using the control barrier and Lyapunov function (CBF-CLF). In this way, the multi-robot collision avoidance as well as time efficiency are satisfied by adapting the velocities of robots. In sharp contrast to the conventional heuristic methods, path length, smoothness and safety are fully considered by mathematically formulating the optimization problems in continuous environments. Extensive experiments as well as computer simulations are conducted to validate the effectiveness of the proposed path-planning algorithm.

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