Asymptotically Optimal Planning for Non-Myopic Multi-Robot Information Gathering

This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant samplingbased approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we demonstrate that by introducing bias in the sampling process towards informative areas, the proposed method can quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks which were previously infeasible.

[1]  Vijay Kumar,et al.  Approximate representations for multi-robot control policies that maximize mutual information , 2014, Robotics: Science and Systems.

[2]  Vijay Kumar,et al.  Capt: Concurrent assignment and planning of trajectories for multiple robots , 2014, Int. J. Robotics Res..

[3]  Stergios I. Roumeliotis,et al.  A Bank of Maximum A Posteriori (MAP) Estimators for Target Tracking , 2015, IEEE Transactions on Robotics.

[4]  Michael M. Zavlanos,et al.  Distributed State Estimation Using Intermittently Connected Robot Networks , 2018, IEEE Transactions on Robotics.

[5]  George J. Pappas,et al.  Decentralized active information acquisition: Theory and application to multi-robot SLAM , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Mac Schwager,et al.  Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots , 2016, IEEE Transactions on Robotics.

[7]  Andreas Krause,et al.  Efficient Informative Sensing using Multiple Robots , 2014, J. Artif. Intell. Res..

[8]  George J. Pappas,et al.  On trajectory optimization for active sensing in Gaussian process models , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[9]  J. How,et al.  Information-rich Path Planning with General Constraints using Rapidly-exploring Random Trees , 2010 .

[10]  Henk Wymeersch,et al.  Distributed Estimation With Information-Seeking Control in Agent Networks , 2014, IEEE Journal on Selected Areas in Communications.

[11]  Sulema Aranda,et al.  On Optimal Sensor Placement and Motion Coordination for Target Tracking , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

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

[13]  George J. Pappas,et al.  Information acquisition with sensing robots: Algorithms and error bounds , 2013, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Vijay Kumar,et al.  A decentralized control policy for adaptive information gathering in hazardous environments , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[15]  Vijay Kumar,et al.  Anytime Planning for Decentralized Multirobot Active Information Gathering , 2018, IEEE Robotics and Automation Letters.

[16]  Geoffrey A. Hollinger,et al.  Sampling-based robotic information gathering algorithms , 2014, Int. J. Robotics Res..

[17]  Nathan Michael,et al.  Distributed Submodular Maximization on Partition Matroids for Planning on Large Sensor Networks , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[18]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

[19]  Qing-Long Han,et al.  Mobile Robot Networks for Environmental Monitoring: A Cooperative Receding Horizon Temporal Logic Control Approach , 2019, IEEE Transactions on Cybernetics.

[20]  Michael M. Zavlanos,et al.  Global Planning for Multi-Robot Communication Networks in Complex Environments , 2016, IEEE Transactions on Robotics.

[21]  Basilio Bona,et al.  Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence , 2014, J. Intell. Robotic Syst..

[22]  Jorge Cortés,et al.  A cooperative deployment strategy for optimal sampling in spatiotemporal estimation , 2008, 2008 47th IEEE Conference on Decision and Control.

[23]  Vijay Kumar,et al.  Distributed multi-robot task assignment and formation control , 2008, 2008 IEEE International Conference on Robotics and Automation.

[24]  Ali Abdul Khaliq,et al.  Towards real-world gas distribution mapping and leak localization using a mobile robot with 3d and remote gas sensing capabilities , 2013, 2013 IEEE International Conference on Robotics and Automation.

[25]  George J. Pappas,et al.  Distributed Algorithms for Stochastic Source Seeking with Mobile Robot Networks: Technical Report , 2014, 1402.0051.