Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System

2019 ◽  
Vol 112 ◽  
pp. 32-48 ◽  
Author(s):  
João Paulo Lima Silva de Almeida ◽  
Renan Taizo Nakashima ◽  
Flávio Neves-Jr ◽  
Lúcia Valéria Ramos de Arruda
Keyword(s):  
Robot System ◽  
Unknown Environment ◽  
On Line ◽  
Path Planner ◽  
Line Path ◽  
Cooperative Exploration ◽  
Multi Robot

Effects of Multi-Robot Team Formations on Distributed Area Coverage

2019 ◽  
pp. 1192-1219
Author(s):  
Prithviraj Dasgupta ◽  
Taylor Whipple ◽  
Ke Cheng
Keyword(s):  
Mobile Robots ◽  
Area Coverage ◽  
Experimental Results ◽  
Wheel Slip ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Unknown Environment ◽  
Robot Teams ◽  
And Performance ◽  
Multi Robot

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

Multi-Robot System for Mapping of the Unknown Environment

2014 ◽  
Vol 511-512 ◽  
pp. 827-833 ◽  
Author(s):  
Martin Vondráček ◽  
Martin Dekan ◽  
František Duchoň ◽  
Stanislav Števo
Keyword(s):  
Robot System ◽  
Unknown Environment ◽  
Multi Robot

The aim of this article is proposal and implementation of the multi-robot system for mapping of the unknown environment. For the localization of each robot, simple odometry was used. Navigation of the robots is based on algorithm similar to bug algorithms. Communication between robots is based on polling. The system was implemented on the platform iRobot Create. Practical experiments have proven that multi-robot system for mapping of the unknown environment is faster and more reliable than single robot system.

Collective navigation of a multi-robot system in an unknown environment

2020 ◽  
Vol 132 ◽  
pp. 103604
Author(s):  
Ertug Olcay ◽  
Fabian Schuhmann ◽  
Boris Lohmann
Keyword(s):  
Robot System ◽  
Unknown Environment ◽  
Collective Navigation ◽  
Multi Robot

CBR Based Collision Avoidance Approach to Reactive Control for Multi-Robot System

2012 ◽  
Vol 232 ◽  
pp. 398-402
Author(s):  
Qiang Liu ◽  
Jia Chen Ma ◽  
Qi Zhang
Keyword(s):  
Collision Avoidance ◽  
Simulation Software ◽  
Case Based Reasoning ◽  
Reactive Control ◽  
Robot System ◽  
Dynamic Selection ◽  
On Line ◽  
High Level ◽  
On Line Learning ◽  
Multi Robot

In this paper we discuss a Case-Based Reasoning (CBR) method which is an on-line learning mechanism for dynamic selection and modification of behavior assemblages for collision avoidance of multi-robot system. The CBR module is designed as an additional reactive control system which provide flexible performance in novel environments without extensive high-level reasoning that would slow the system down. The results by robot simulation software MissionLab show that the CBR are effective for making decisions to avoid the collision with static obstacles as well as moving robots in multi-robot system.

Effects of Multi-Robot Team Formations on Distributed Area Coverage

2011 ◽  
Vol 2 (1) ◽  
pp. 44-69 ◽  
Author(s):  
Prithviraj Dasgupta ◽  
Taylor Whipple ◽  
Ke Cheng
Keyword(s):  
Mobile Robots ◽  
Area Coverage ◽  
Experimental Results ◽  
Wheel Slip ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Unknown Environment ◽  
Robot Teams ◽  
And Performance ◽  
Multi Robot

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.

An efficient on-line path planner for outdoor mobile robots

2000 ◽  
Vol 32 (2-3) ◽  
pp. 129-143 ◽  
Author(s):  
Alex Yahja ◽  
Sanjiv Singh ◽  
Anthony Stentz
Keyword(s):  
Mobile Robots ◽  
On Line ◽  
Path Planner ◽  
Line Path

Homogenous multi-robot system for mapping of unknown environment

2016 ◽  
Author(s):  
Frantisek Duchon ◽  
Martin Vondracek ◽  
Martin Dekan ◽  
Andrej Babinec ◽  
Robert Spielmann ◽  
...  
Keyword(s):  
Robot System ◽  
Unknown Environment ◽  
Multi Robot

Autonomous Role Assignment in a Homogeneous Multi-Robot System

2005 ◽  
Vol 17 (5) ◽  
pp. 596-604 ◽  
Author(s):  
Toshiyuki Yasuda ◽  
◽  
Kazuhiro Ohkura ◽  
Keyword(s):  
Reinforcement Learning ◽  
Time Step ◽  
Robot System ◽  
Continuous Action ◽  
Autonomous Behavior ◽  
Continuous State Space ◽  
Continuous State ◽  
On Line ◽  
Discrimination Method ◽  
Multi Robot

This paper describes an approach for controlling an autonomous homogeneous multi-robot system. An extremely important topic for this type of system is the design of an on-line autonomous behavior acquisition mechanism that is capable of developing cooperative roles as well as assigning them to a robot appropriately in a noisy embedded environment. Our approach applies reinforcement learning that adopts the Bayesian discrimination method for segmenting a continuous state space and a continuous action space simultaneously. In addition, a neural network is provided for predicting the average of the other robots’ postures at the next time step in order to stabilize the reinforcement learning environment. The proposed method is validated through computer simulations as well as our hand-made multi-robot system.

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

SIMULATION ◽  
2020 ◽  
Vol 96 (10) ◽  
pp. 807-824
Author(s):  
Jom J Kandathil ◽  
Robins Mathew ◽  
Somashekhar S Hiremath
Keyword(s):  
Energy Expenditure ◽  
Travel Time ◽  
Obstacle Avoidance ◽  
Heuristic Method ◽  
Robot System ◽  
Unknown Environment ◽  
New Strategy ◽  
Total Travel Time ◽  
Avoidance Strategy ◽  
Multi Robot

This paper addresses the development and implementation of an obstacle avoidance strategy for a multi-robot system operating in an unknown environment. This novel strategy is based on the conventional Bug-1 obstacle avoidance algorithm, which is a non-heuristic method for obstacle avoidance in an unknown environment. In the Bug-1 algorithm, a robot circumnavigates the obstacle to find the coordinates of the point, having minimum distance to the goal. In the case of the new strategy, two robots will circumnavigate the obstacle in such a manner that it will reduce both the total travel time and the distance traveled. Information acquired by the individual robots during the circumnavigation is shared across other robots to accomplish the obstacle avoidance efficiently. A theoretical analysis is carried out to show the improvement in travel time and energy expenditure of the robots in implementing the new strategy. Different test scenarios for comparing the performance of the obstacle avoidance strategies using simulations is also identified. The simulation studies using these scenarios suggest that the new algorithm is a better algorithm with respect to multi-robot obstacle avoidance. The experimental study conducted also shows that robots using this new algorithm have a better travel time and less energy expenditure than the conventional Bug-1 algorithm.

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