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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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.
Colegrave J, Branch AA. case study of autonomous household vacuum cleaner. In: AIAA/NASA CIRFFSS. Houston; 1994.
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.
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.
Yamauchi B. A frontier-based approach for autonomous exploration. In: IEEE international symposium on computational intelligence in robotics and automation. 1997; p. 146–151.
Guzzoni D, Cheyer A, Julia L, Konolige K. Many robots make short work. AI Magazine. 1997;18(1):55–64.
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.
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.
Yamauchi B. Frontier-based exploration using multiple robots. In: Proceedings of the 2nd international conference on autonomous agents; 1998. p. 47–53.
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.
Rooker MN, Birk A. Multi-robot exploration under the constraints of wireless networking. Control Eng Pract. 2007;15(4):435–445.
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.
Sheng W, Yang Q, Tan J, Xi N. Distributed multirobot coordination in area exploration. Robot Auton Syst. 2006;54:945–955.
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.
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.
Nooraliei A, Nooraliei H. Path planning using wave front’s improvement methods. In: International conference on computer technology and development; 2009. p. 259–264.
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.
Manikas TW, Ashenayi K, Wainwright RL. Genetic algorithms for autonomous robot navigation. In: IEEE instrumentation and measurement magazine; 2007. p. 26–31.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Kuhn HW. The hungarian method for the assignment problem. Naval research logistics quarterly; 1995. p. 83–97.
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.
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.
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.
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.
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.
Shi Z, Wang W. Artificial intelligence. Beijing: National Defence Industry Press; 2004. p. 63–106.
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.
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.
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.
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.
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.
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.
Bouraqadi N, Doniec A. Flocking-based multi-robot exploration. In: 4th National conference on control architectures of robots; 2009.
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.
About this article
Cite this article
Pal, A., Tiwari, R. & Shukla, A. Multi-Robot Exploration in Wireless Environments. Cogn Comput 4, 526–542 (2012). https://doi.org/10.1007/s12559-012-9142-7
- Multi-robot system
- Area exploration
- A* algorithm
- Communication protocol