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Published in: Cognitive Computation 4/2012

01-12-2012

Multi-Robot Exploration in Wireless Environments

Authors: Anshika Pal, Ritu Tiwari, Anupam Shukla

Published in: Cognitive Computation | Issue 4/2012

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Abstract

This paper presents a multi-robot exploration approach for application in wireless environments. The challenges generally faced by a robot team are to maintain network connectivity among themselves, in order to have an accurate map of the environment at each instant and have an efficient navigation plan for moving toward the unexplored area. To address these issues, we focus on the integration of such connectivity constraints and take navigation plan problems into account. A modified A* based algorithm is proposed for planning the navigation of the robots. A communication protocol based on the concept of leader-follower is developed for maintaining network connectivity. Mobile robots typically use a wireless connection to communicate with the other team members and establishes a Mobile Ad Hoc NETwork among themselves. A communication route is established between each robot pair for exchanging local map data, in order to achieve consistent global map of the environment at each instant. If the routes have multiple hops, this raises the problem of message delaying because time delay accumulates per hop traveled. The purpose of the proposed Leader Follower Interaction Protocol is to reduce the total number of hop counts required for all transmissions between robot pairs. This is different from the centralized approach where the leader is a fixed base station. The role of leader in the proposed approach switches from one robot to others as network’s wireless topology changes as robots move. Simulation results show the effectiveness of communication protocol, as well as the navigation mechanism.

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Literature
1.
go back to reference Bender MA, Fekete SP, Kroller E, Mitchell JSB. Polishchuk V. The lawnmower problem. In: Proceedings of the 5th Canadian conference on computational geometry; 1993. p. 461–466. Bender MA, Fekete SP, Kroller E, Mitchell JSB. Polishchuk V. The lawnmower problem. In: Proceedings of the 5th Canadian conference on computational geometry; 1993. p. 461–466.
2.
go back to reference Colegrave J, Branch AA. case study of autonomous household vacuum cleaner. In: AIAA/NASA CIRFFSS. Houston; 1994. Colegrave J, Branch AA. case study of autonomous household vacuum cleaner. In: AIAA/NASA CIRFFSS. Houston; 1994.
3.
go back to reference Gage DW. Randomized search strategies with imperfect sensors. In: Chun WH, Wolfe WJ, editors. Presented at the society of photo-optical instrumentation engineers (SPIE) conference, mobile robots VIII, vol. 2058. Bellingham: Society of Photo-Optical Instrumentation Engineers; 1994. p. 270–279. Gage DW. Randomized search strategies with imperfect sensors. In: Chun WH, Wolfe WJ, editors. Presented at the society of photo-optical instrumentation engineers (SPIE) conference, mobile robots VIII, vol. 2058. Bellingham: Society of Photo-Optical Instrumentation Engineers; 1994. p. 270–279.
4.
go back to reference Pearce AL, Rybski PE, Stoeter SA, Papanikolopoulos N. Dispersion behaviors for a team of multiple miniature robots. International conference on robotics and automation. Taipei; 2003. p. 1158–1163. Pearce AL, Rybski PE, Stoeter SA, Papanikolopoulos N. Dispersion behaviors for a team of multiple miniature robots. International conference on robotics and automation. Taipei; 2003. p. 1158–1163.
5.
go back to reference Yamauchi B. A frontier-based approach for autonomous exploration. In: IEEE international symposium on computational intelligence in robotics and automation. 1997; p. 146–151. Yamauchi B. A frontier-based approach for autonomous exploration. In: IEEE international symposium on computational intelligence in robotics and automation. 1997; p. 146–151.
6.
go back to reference Guzzoni D, Cheyer A, Julia L, Konolige K. Many robots make short work. AI Magazine. 1997;18(1):55–64. Guzzoni D, Cheyer A, Julia L, Konolige K. Many robots make short work. AI Magazine. 1997;18(1):55–64.
7.
go back to reference Fox D, Ko J, Konolige K, Limketkai B, Stewart B. Distributed multi-robot exploration and mapping. In: Proceedings of the IEEE special issue on multi-robot systems; 2006. p. 1325–1339. Fox D, Ko J, Konolige K, Limketkai B, Stewart B. Distributed multi-robot exploration and mapping. In: Proceedings of the IEEE special issue on multi-robot systems; 2006. p. 1325–1339.
8.
go back to reference Shatkay H, Kaelbling LP. Learning topo-logical maps with weak local odometric information. In: Proceedings of the international joint conference on artificial intelligence; 1997. p. 920–929. Shatkay H, Kaelbling LP. Learning topo-logical maps with weak local odometric information. In: Proceedings of the international joint conference on artificial intelligence; 1997. p. 920–929.
9.
go back to reference Yamauchi B. Frontier-based exploration using multiple robots. In: Proceedings of the 2nd international conference on autonomous agents; 1998. p. 47–53. Yamauchi B. Frontier-based exploration using multiple robots. In: Proceedings of the 2nd international conference on autonomous agents; 1998. p. 47–53.
10.
go back to reference Simmons R, Apfelbaum D, Burgard W, Fox D, Moors M, Thrun S et al. Coordination for multi-robot exploration and mapping. In: Proceedings of the national conference on artificial intelligence (AAAI); 2000. p. 851–858. Simmons R, Apfelbaum D, Burgard W, Fox D, Moors M, Thrun S et al. Coordination for multi-robot exploration and mapping. In: Proceedings of the national conference on artificial intelligence (AAAI); 2000. p. 851–858.
11.
go back to reference Rooker MN, Birk A. Multi-robot exploration under the constraints of wireless networking. Control Eng Pract. 2007;15(4):435–445.CrossRef Rooker MN, Birk A. Multi-robot exploration under the constraints of wireless networking. Control Eng Pract. 2007;15(4):435–445.CrossRef
12.
go back to reference Vazquez, Malcolm C. Distributed multirobot exploration maintaining a mobile network. In: Proceedings of the 2nd international IEEE conference on intelligent systems; 2004. p. 113–118. Vazquez, Malcolm C. Distributed multirobot exploration maintaining a mobile network. In: Proceedings of the 2nd international IEEE conference on intelligent systems; 2004. p. 113–118.
13.
go back to reference Sheng W, Yang Q, Tan J, Xi N. Distributed multirobot coordination in area exploration. Robot Auton Syst. 2006;54:945–955.CrossRef Sheng W, Yang Q, Tan J, Xi N. Distributed multirobot coordination in area exploration. Robot Auton Syst. 2006;54:945–955.CrossRef
14.
go back to reference Ricardo AL, Leandro SC, Gustavo HCO. K-Bug, A new bug approach for mobile robot’s path planning. In: 16th IEEE international conference on control applications part of IEEE multi-conference on systems and control. Singapore; 2007. p. 403–408. Ricardo AL, Leandro SC, Gustavo HCO. K-Bug, A new bug approach for mobile robot’s path planning. In: 16th IEEE international conference on control applications part of IEEE multi-conference on systems and control. Singapore; 2007. p. 403–408.
15.
go back to reference Djekoune AO, Achour K, Toumi R. A sensor based navigation algorithm for a mobile robot using the DVFF approach. Int J Adv Rob Syst. 2009;6(2):97–108. Djekoune AO, Achour K, Toumi R. A sensor based navigation algorithm for a mobile robot using the DVFF approach. Int J Adv Rob Syst. 2009;6(2):97–108.
16.
go back to reference Nooraliei A, Nooraliei H. Path planning using wave front’s improvement methods. In: International conference on computer technology and development; 2009. p. 259–264. Nooraliei A, Nooraliei H. Path planning using wave front’s improvement methods. In: International conference on computer technology and development; 2009. p. 259–264.
17.
go back to reference Pal A, Tiwari R, Shukla A. A focused wave front approach for mobile robot path planning. In: 6th International conference on hybrid artificial intelligence systems. Wroclaw, Poland: Part I, LNAI 6678; 2011. p. 190–197. Pal A, Tiwari R, Shukla A. A focused wave front approach for mobile robot path planning. In: 6th International conference on hybrid artificial intelligence systems. Wroclaw, Poland: Part I, LNAI 6678; 2011. p. 190–197.
18.
go back to reference Manikas TW, Ashenayi K, Wainwright RL. Genetic algorithms for autonomous robot navigation. In: IEEE instrumentation and measurement magazine; 2007. p. 26–31. Manikas TW, Ashenayi K, Wainwright RL. Genetic algorithms for autonomous robot navigation. In: IEEE instrumentation and measurement magazine; 2007. p. 26–31.
19.
go back to reference Mahmoudi SE, Bitaghsir AA, Forouzandeh B, Marandi AR. A new genetic method for mobile robot navigation. In: 10th IEEE international conference on methods and models in automation and robotics. Poland: Miedzyzdroje; 2004. Mahmoudi SE, Bitaghsir AA, Forouzandeh B, Marandi AR. A new genetic method for mobile robot navigation. In: 10th IEEE international conference on methods and models in automation and robotics. Poland: Miedzyzdroje; 2004.
20.
go back to reference Liang Y, Xu L. Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation; 2009. p. 303–308. Liang Y, Xu L. Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation; 2009. p. 303–308.
21.
go back to reference Dai S, Huang H, Wu F, Xiao S, Zhang T. Path planning for mobile robot based on rough set genetic algorithm. In: 2nd International conference on intelligent networks and intelligent systems; 2009. p. 278–281. Dai S, Huang H, Wu F, Xiao S, Zhang T. Path planning for mobile robot based on rough set genetic algorithm. In: 2nd International conference on intelligent networks and intelligent systems; 2009. p. 278–281.
22.
go back to reference Mei Y, Lu Y, George LCS, Hu YC. Energy-efficient mobile robot exploration. In: IEEE international conference on robotics and automation; 2006. p. 505–511. Mei Y, Lu Y, George LCS, Hu YC. Energy-efficient mobile robot exploration. In: IEEE international conference on robotics and automation; 2006. p. 505–511.
23.
go back to reference Stachniss C, Mozos OM, Burgard W. Efficient exploration of unknown indoor environments using a team of mobile robots. Ann Math Artif Intell. 2008;52:205–227.CrossRef Stachniss C, Mozos OM, Burgard W. Efficient exploration of unknown indoor environments using a team of mobile robots. Ann Math Artif Intell. 2008;52:205–227.CrossRef
24.
go back to reference Agmon N, Hazon N, Kaminka G. The giving tree: constructing trees for efficient offline and online multi robot coverage. Ann Math Artif Intell. 2008;52:143–168.CrossRef Agmon N, Hazon N, Kaminka G. The giving tree: constructing trees for efficient offline and online multi robot coverage. Ann Math Artif Intell. 2008;52:143–168.CrossRef
25.
go back to reference Visser A, Slamet BA. Balancing the information gain against the movement cost for multi robot frontier exploration. In: European robotics symposium; 2008. p. 43–52. Visser A, Slamet BA. Balancing the information gain against the movement cost for multi robot frontier exploration. In: European robotics symposium; 2008. p. 43–52.
26.
go back to reference Wurm KM, Stachniss C, Burgard W. Coordinated multi-robot exploration using a segmentation of the environment. In: International conference on intelligent robots and systems; 2008. p. 1160–1165. Wurm KM, Stachniss C, Burgard W. Coordinated multi-robot exploration using a segmentation of the environment. In: International conference on intelligent robots and systems; 2008. p. 1160–1165.
27.
go back to reference Doniec A, Bouraqadi N, Defoort M, Le VL, Stinckwich S. Distributed constraint reasoning applied to multi robot exploration. In: 21st IEEE international conference on tools with artificial intelligence; 2009. p. 159–166. Doniec A, Bouraqadi N, Defoort M, Le VL, Stinckwich S. Distributed constraint reasoning applied to multi robot exploration. In: 21st IEEE international conference on tools with artificial intelligence; 2009. p. 159–166.
28.
go back to reference Ferranti E, Trigoni N, Levene M. Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes. Auton Agents Multi Agent Syst. 2009;19(2):210–243.CrossRef Ferranti E, Trigoni N, Levene M. Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes. Auton Agents Multi Agent Syst. 2009;19(2):210–243.CrossRef
29.
go back to reference Pei Y, Mutka MW, Xi N. Coordinated multi-robot real-time exploration with connectivity and bandwidth awareness. In: IEEE Int Conf Robot Autom. 2010; 5460-5465. Pei Y, Mutka MW, Xi N. Coordinated multi-robot real-time exploration with connectivity and bandwidth awareness. In: IEEE Int Conf Robot Autom. 2010; 5460-5465.
30.
go back to reference Al-Khawaldah M, Livatino S, Lee D. Frontier based exploration with two cooperative mobile robots. Int J Circ Syst Signal Proc. 2010;4(2):34–43. Al-Khawaldah M, Livatino S, Lee D. Frontier based exploration with two cooperative mobile robots. Int J Circ Syst Signal Proc. 2010;4(2):34–43.
31.
go back to reference Kuhn HW. The hungarian method for the assignment problem. Naval research logistics quarterly; 1995. p. 83–97. Kuhn HW. The hungarian method for the assignment problem. Naval research logistics quarterly; 1995. p. 83–97.
32.
go back to reference Konar A, Pal S. Modeling cognition with fuzzy neural nets. In: Leondes CT, editor. Fuzzy systems theory: techniques and applications. New York: Academic Press; 1999. p. 1341–1391. Konar A, Pal S. Modeling cognition with fuzzy neural nets. In: Leondes CT, editor. Fuzzy systems theory: techniques and applications. New York: Academic Press; 1999. p. 1341–1391.
33.
go back to reference Xin D, Hua-hua C, Wei-kang G. Neural network and genetic algorithm based global path planning in a static environment. J Zhejiang Univ Sci. 2005;6A(6):549–554. Xin D, Hua-hua C, Wei-kang G. Neural network and genetic algorithm based global path planning in a static environment. J Zhejiang Univ Sci. 2005;6A(6):549–554.
34.
go back to reference Na Y, Oh S. Hybrid control for autonomous mobile robot navigation using neural network based behavior modules and environment classification. Auton Robots. 2003;15(2):193–206.CrossRef Na Y, Oh S. Hybrid control for autonomous mobile robot navigation using neural network based behavior modules and environment classification. Auton Robots. 2003;15(2):193–206.CrossRef
35.
go back to reference Masehian E, Sedighizadeh D. A multi-objective PSO-based algorithm for robot path planning. In: IEEE international conference on industrial technology; 2010. p. 465–470. Masehian E, Sedighizadeh D. A multi-objective PSO-based algorithm for robot path planning. In: IEEE international conference on industrial technology; 2010. p. 465–470.
36.
go back to reference Hart PE, Nilsson NJ, Raphael B. A formal basic for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybernet. 1968;4:100–107.CrossRef Hart PE, Nilsson NJ, Raphael B. A formal basic for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybernet. 1968;4:100–107.CrossRef
37.
go back to reference Shi Z, Wang W. Artificial intelligence. Beijing: National Defence Industry Press; 2004. p. 63–106. Shi Z, Wang W. Artificial intelligence. Beijing: National Defence Industry Press; 2004. p. 63–106.
38.
go back to reference Couceiro MS, Rocha RP, Ferreira NMF. A novel multi-robot exploration approach based on particle swarm optimization algorithms. In: Proceedings of the IEEE international symposium on safety. Kyoto, Japan: Security and Rescue Robotics; 2011. p. 327–332. Couceiro MS, Rocha RP, Ferreira NMF. A novel multi-robot exploration approach based on particle swarm optimization algorithms. In: Proceedings of the IEEE international symposium on safety. Kyoto, Japan: Security and Rescue Robotics; 2011. p. 327–332.
39.
go back to reference Moreno RA, Espino AL, Miguel AS. Modeling consciousness for autonomous robot exploration. In: Proceedings of the 2nd international work-conference on the interplay between natural and artificial computation, part I: bio-inspired modeling of cognitive tasks; 2007. p. 51–60. Moreno RA, Espino AL, Miguel AS. Modeling consciousness for autonomous robot exploration. In: Proceedings of the 2nd international work-conference on the interplay between natural and artificial computation, part I: bio-inspired modeling of cognitive tasks; 2007. p. 51–60.
40.
go back to reference Pengchong Z, Alei L, Liang L, Ying C, Haibing G, Xinan Y. An exploration algorithm for a swarm of homogeneous robots. In: IEEE international conference on computational intelligence and software engineering; 2009. p. 1–6. Pengchong Z, Alei L, Liang L, Ying C, Haibing G, Xinan Y. An exploration algorithm for a swarm of homogeneous robots. In: IEEE international conference on computational intelligence and software engineering; 2009. p. 1–6.
41.
go back to reference Derr K, Manic M. Multi-robot, multi-target particle swarm optimization search in noisy wireless environments. In: 2nd IEEE international conference on human system interactions; 2009. p. 81–86. Derr K, Manic M. Multi-robot, multi-target particle swarm optimization search in noisy wireless environments. In: 2nd IEEE international conference on human system interactions; 2009. p. 81–86.
42.
go back to reference Ma X, Zhang Q, Li Y. Genetic algorithm-based multi-robot cooperative exploration. In: IEEE international conference on control and automation, Guangzhou. China; 2007. p. 1018–1023. Ma X, Zhang Q, Li Y. Genetic algorithm-based multi-robot cooperative exploration. In: IEEE international conference on control and automation, Guangzhou. China; 2007. p. 1018–1023.
43.
go back to reference Cioarga R, Nalatan I, Tura-Bob S, Micea M, Cretu V, Biriescu M, Groza V. Emergent exploration and resource gathering in collaborative robotic environments. In: IEEE international workshop on robotic and sensors environments. Ottawa-Canada; 2008. p. 13–18. Cioarga R, Nalatan I, Tura-Bob S, Micea M, Cretu V, Biriescu M, Groza V. Emergent exploration and resource gathering in collaborative robotic environments. In: IEEE international workshop on robotic and sensors environments. Ottawa-Canada; 2008. p. 13–18.
44.
go back to reference Bouraqadi N, Doniec A. Flocking-based multi-robot exploration. In: 4th National conference on control architectures of robots; 2009. Bouraqadi N, Doniec A. Flocking-based multi-robot exploration. In: 4th National conference on control architectures of robots; 2009.
45.
go back to reference Macedo L, Cardoso A. The role of surprise, curiosity and hunger on exploration of unknown environments populated with entities. In: IEEE international conference on artificial intelligence; 2005. p. 47–53. Macedo L, Cardoso A. The role of surprise, curiosity and hunger on exploration of unknown environments populated with entities. In: IEEE international conference on artificial intelligence; 2005. p. 47–53.
Metadata
Title
Multi-Robot Exploration in Wireless Environments
Authors
Anshika Pal
Ritu Tiwari
Anupam Shukla
Publication date
01-12-2012
Publisher
Springer-Verlag
Published in
Cognitive Computation / Issue 4/2012
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-012-9142-7

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