DocumentCode :
668222
Title :
Multiple-Robot motion planning in an unknown and uncertain environment
Author :
Bautista-Montesano, Rolando ; de la Cueva-Hernandez, Victor
Author_Institution :
Robot. & Control Grad. Program, ITESM-CCM, Mexico City, Mexico
fYear :
2013
fDate :
13-15 Nov. 2013
Firstpage :
1
Lastpage :
6
Abstract :
When a Robot finds itself in an unknown and uncertain environment it needs to retrieve information from its surroundings so it can perform a task in the appropriate way. A task may demand far much more capabilities than the ones a robot can perform by itself, a Multiple-Robot System can accomplish lots of tasks, however, working with them implies more uncertainty while executing actions. The desired task in this case is to move a huge object from an initial position to a final one. There are two main solutions to this problem, the first one is to employ a big and expensive robot that is capable of moving the object. The second choice is to employ a Multiple-Robot System composed by several low-cost robots. The second one was chosen because of its economic advantages, although it needs a more complex way of communication between the agents. This paper presents a novel form of deploying a coupled Multiple-Robot System based in a Master-Slave configuration in an Unknown and Uncertain Environment, where initially only the goal is known, no a priori environment information is given. This way, the Multiple-Robot System will need to retrieve data from its sensors so it can spot itself and build a map while moving through the space so Classic Path Planning techniques can be used. The employed algorithms were Approximate Cell Decomposition, A star, Kalman Filter and Particle Filter; they all run in MATLAB 2012a. The used robots were Parallax Boe-Bots.
Keywords :
Kalman filters; mobile robots; multi-robot systems; particle filtering (numerical methods); path planning; uncertain systems; A star; Kalman filter; Parallax Boe-Bots; agent communication; approximate cell decomposition; classic path planning technique; coupled multiple-robot system; economic advantage; environment information; in MATLAB 2012a; information retrieval; low-cost robots; map building; master-slave configuration; multiple-robot motion planning; object moving; particle filter; sensor data retrieval; uncertain environment; unknown environment; Kalman filters; Particle filters; Path planning; Robot kinematics; Robot sensing systems; a star; cell decomposition; kalman; multiple-robot; particle; path planning; uncertainty; unknown;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power, Electronics and Computing (ROPEC), 2013 IEEE International Autumn Meeting on
Conference_Location :
Mexico City
Type :
conf
DOI :
10.1109/ROPEC.2013.6702730
Filename :
6702730
Link To Document :
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