IEEE Transactions on Pattern Analysis and Machine Intelligence · 1996 · 76 citations · 19 references
EngineeringAutonomous SystemsRobotics EngineeringAutonomous VehiclesMulti-robot SystemsRobot PerceptionDecision-level FusionDistributed SensingMission PlanningMulti-robot CoordinationMulti-robot PerceptionCamera Pan AnglesCamera Tilt AnglesCooperative Mission PlanningInformation GainInformation Gain PlanningInformation-gain MaximizationMulti-vehicle CoordinationSensor Selection
This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPA's Unmanned Ground Vehicle program.
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Genetic algorithms in search, optimization, and machine learning
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