Search and Localization of a Weak Source with a Multi-robot Formation

This paper proposes an algorithm to guide a formation of mobile robots, subject to communication constraints, from an arbitrary position to the location of the source of a physical signal in a planar environment. The information on the signal is only based on noisy measurements of its strength collected during the mission and the signal is considered to be weak and indistinguishable from the noise in a large portion of the environment. The goal of the team is thus to search for a reliable signal and finally converge to the source location. An accurate estimation of the signal gradient is obtained by fusing the data gathered by the robots while moving in a circular formation. The algorithm proposed to steer the formation, called Gradient-biased Correlated Random Walk (GCRW), exploits the gradient estimation to bias a correlated random walk, which ensures an efficient non-oriented search motion when far from the source. The resulting strategy is so able to obtain a suitable trade-off between exploration and exploitation. Results obtained in simulated experiments, including comparisons with possible alternatives, are presented to analyze and evaluate the performance of the proposed approach.

[1]  Serge Kernbach,et al.  Multi-robot searching algorithm using Lévy flight and artificial potential field , 2010, 2010 IEEE Safety Security and Rescue Robotics.

[2]  Vijay Kumar,et al.  Robot and sensor networks for first responders , 2004, IEEE Pervasive Computing.

[3]  Carlos Canudas-de-Wit,et al.  Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems , 2014, IEEE Transactions on Automatic Control.

[4]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[5]  C. Patlak Random walk with persistence and external bias , 1953 .

[6]  M. Ani Hsieh,et al.  Multi-agent search for source localization in a turbulent medium , 2016 .

[7]  Jay A. Farrell,et al.  Moth-inspired chemical plume tracing on an autonomous underwater vehicle , 2006, IEEE Transactions on Robotics.

[8]  A. Ijspeert,et al.  Environmental monitoring using autonomous vehicles: a survey of recent searching techniques. , 2017, Current Opinion in Biotechnology.

[9]  Fumin Zhang,et al.  A bio-inspired plume tracking algorithm for mobile sensing swarms in turbulent flow , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Massimo Vergassola,et al.  ‘Infotaxis’ as a strategy for searching without gradients , 2007, Nature.

[11]  G. Viswanathan,et al.  Necessary criterion for distinguishing true superdiffusion from correlated random walk processes. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Frank W. Grasso,et al.  Lévy-taxis: a novel search strategy for finding odor plumes in turbulent flow-dominated environments , 2009 .

[13]  H. Stanley,et al.  Lévy flights in random searches , 2000 .

[14]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[15]  Alexandre Seuret,et al.  Distributed Source Seeking via a Circular Formation of Agents Under Communication Constraints , 2016, IEEE Transactions on Control of Network Systems.

[16]  R. Andrew Russell,et al.  A comparison of reactive robot chemotaxis algorithms , 2003, Robotics Auton. Syst..

[17]  Lino Marques,et al.  Robots for Environmental Monitoring: Significant Advancements and Applications , 2012, IEEE Robotics & Automation Magazine.

[18]  G. Viswanathan,et al.  The influence of turning angles on the success of non-oriented animal searches. , 2008, Journal of Theoretical Biology.

[19]  A. Reynolds Bridging the gulf between correlated random walks and Lévy walks: autocorrelation as a source of Lévy walk movement patterns , 2010, Journal of The Royal Society Interface.

[20]  Lino Marques,et al.  Particle swarm-based olfactory guided search , 2006, Auton. Robots.

[21]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.