A data collection decision-making framework for a multi-tier collaboration of heterogeneous orbital, aerial, and ground craft

An algorithm for the autonomous identification of and tasking to collect additional data required to complete a goal is presented. This assertion-form goal is decomposed autonomously into an initial set of data collection tasks. Once these are completed, information gaps may exist or new information collection requirements may be identified. A utilitymaximization, cost-minimization metric is applied to ascertain what data collection tasks craft should be assigned. This decision making process is performed at each level of the hierarchy, decomposing large-scale needs into progressively smaller assignments. The utility of this control approach is assessed for persistent surveillance and planetary science applications.

[1]  Hyondong Oh,et al.  Indoor UAV Control Using Multi-Camera Visual Feedback , 2011, J. Intell. Robotic Syst..

[2]  D. Schulze-Makuch,et al.  Tier-Scalable Reconnaissance Missions For The Autonomous Exploration Of Planetary Bodies , 2007, 2007 IEEE Aerospace Conference.

[3]  Mark A. Tarbell,et al.  Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability , 2007, SPIE Defense + Commercial Sensing.

[4]  Scott A. DeLoach,et al.  Runtime models for automatic reorganization of multi-robot systems , 2011, SEAMS '11.

[5]  Anand V. Panangadan,et al.  Integrated Operational Control of Unattended Distributed Coastal Sensor Web Systems With Mobile Autonomous Robots , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Jeremy Straub.  Multi-Tier Exploration: An Architecture for Dramatically Increasing Mission ROI , 2012 .

[7]  Hugh F. Durrant-Whyte,et al.  Decentralised cooperative localisation for heterogeneous teams of mobile robots , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  Barbara Hayes-Roth,et al.  A Blackboard Architecture for Control , 1985, Artif. Intell..

[9]  Prithviraj Dasgupta.  Multi-robot task allocation for performing cooperative foraging tasks in an initially unknown environment , 2011 .

[10]  David M. Harland,et al.  Robotic Exploration of the Solar System , 2007 .

[11]  Michael G. Hinchey,et al.  Towards an ASSL specification model for NASA swarm-based exploration missions , 2008, SAC '08.

[12]  Jeremy Straub,et al.  Multi-Tier Planetary Exploration: A New Autonomous Control Paradigm , 2012 .

[13]  Wolfgang Fink.  Generic Prioritization Framework for Target Selection and Instrument Usage for Reconnaissance Mission Autonomy , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[14]  Jeremy Straub.  A feedback-trained autonomous control system for heterogeneous search and rescue applications , 2012, Defense, Security, and Sensing.

[15]  Peter Stone,et al.  Negative information and line observations for Monte Carlo localization , 2008, 2008 IEEE International Conference on Robotics and Automation.

[16]  K. Durga Prasad,et al.  Wireless Sensor Networks – A potential tool to probe for water on Moon , 2011 .

[17]  S. H. Choi,et al.  An auction-based approach with closed-loop bid adjustment to dynamic task allocation in robot teams , 2011 .

[18]  Omar Badran,et al.  Exploration Algorithm Technique for Multi-Robot Collaboration , 2011 .

[19]  Wen-Zhan Song,et al.  Optimized Autonomous Space In-situ Sensor-Web for Volcano Monitoring , 2008 .

[20]  Steve Chien,et al.  A Multi-agent Space, In-situ Volcano Sensorweb , 2010 .

[21]  Naomi Ehrich Leonard,et al.  Virtual leaders, artificial potentials and coordinated control of groups , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[22]  Roberto Furfaro,et al.  Robotic test bed for autonomous surface exploration of Titan, Mars, and other planetary bodies , 2011, 2011 Aerospace Conference.

[23]  Jeremy Straub.  Multi-Tier Exploration Concept Demonstration Mission , 2012 .