Environment Recognition and Path Planning by Multiple Mobile Robots

1997 ◽  
Vol 9 (5) ◽  
pp. 380-386
Author(s):  
Toshiyuki Kumaki ◽  
◽  
Masahito Nakajima ◽  
Masayoshi Kakikura ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Simulation Experiment ◽  
Distributed Coordination ◽  
Multiple Robots ◽  
Multiple Mobile Robots ◽  
Unknown Environments ◽  
Environment Recognition ◽  
Coordination Work ◽  
Observation Method

This article, concerned with a part of the research on distributed coordination work by multiple robots, discusses an algorithm for creating maps of unknown environments which are searched for and observed by multiple mobile robots, and on the results of a simulation experiment using this algorithm. This algorithm comprises a moving method, an observation method, and a task planning method which are intended to help the multiple mobile robots carry out an efficient search of unknown environments.

Simulation Language for Multiple Mobile Robots

1997 ◽  
Vol 9 (5) ◽  
pp. 373-379
Author(s):  
Yoshinobu Adachi ◽  
◽  
Masayoshi Kakikura ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Graph Structure ◽  
Multiple Robots ◽  
Robot System ◽  
Multiple Mobile Robots ◽  
Simulation Language ◽  
Optimum Path ◽  
The Given ◽  
Planning Problems

The purpose of this paper is to propose a design concept for a simulation language to be used in path planning in a multiple mobile robots system. The paper also includes some examples of programming of path planning for multiple mobile robots. In some path planning problems with mobile robots, graph theory can be a powerful tool. In our system, the environment information of the robots is represented in a graph structure. By handling this graph with a programming language, path planning tasks can be executed in a systematic way. Each task of the multiple mobile robots can be described using a Petrinet expression, and several features of the given robot system, for example, the optimum path problem, the deadlock problem of multiple robots, etc., can be deduced using the network model.

scholarly journals Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments

10.5772/5787 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 21
Author(s):  
Kristo Heero ◽  
Alvo Aabloo ◽  
Maarja Kruusmaa
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Shortest Path ◽  
Dynamic Environments ◽  
Distance Transform ◽  
Test Results ◽  
Approach To Learning ◽  
Planning Strategy ◽  
Unknown Environments ◽  
Planning Strategies

This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot's behaviour becomes more predictable. The test results also suggest that the robot's behaviour depends on knowledge about the environemnt but not about the path planning strategy used.

Cooperative Towing With Multiple Robots

2008 ◽  
Author(s):  
Jonathan Fink ◽  
Peng Cheng ◽  
Vijay Kumar
Keyword(s):  
Mobile Robots ◽  
Dry Friction ◽  
Equations Of Motion ◽  
Static Model ◽  
Multiple Robots ◽  
Coordinated Motion ◽  
Multiple Mobile Robots ◽  
Basic Concepts ◽  
Simulation Results

In this paper, we address the cooperative towing of payloads by multiple mobile robots in the plane. Robots are attached via cables to a planar object or a pallet carrying a payload. Coordinated motion by the robots allow the payload to be manipulated through a planar, warehouse-like environment. We formulate a quasi-static model for manipulation and derive equations of motion that yield the motion of the payload for a prescribed motion of the robots in the presence of dry friction and tension constraints. We present experimental and simulation results that demonstrate the basic concepts.

Development of an Environment Recognition System for Multiple Mobile Robots Controlled by a Human

2001 ◽  
Vol 34 (19) ◽  
pp. 321-326
Author(s):  
Akio Nakamura ◽  
Jun Ota ◽  
Shinichiro Kaneko ◽  
Takashi Matsumoto ◽  
Tamio Arai
Keyword(s):  
Mobile Robots ◽  
Recognition System ◽  
Multiple Mobile Robots ◽  
Environment Recognition
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