Visibility-Based Pursuit-Evasion in a Polygonal Environment
WADS '97 Proceedings of the 5th International Workshop on Algorithms and Data Structures
Randomized single-query motion planning in expansive spaces
Randomized single-query motion planning in expansive spaces
Randomized pursuit-evasion with limited visibility
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Visibility-based Pursuit-evasion with Limited Field of View
International Journal of Robotics Research
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs
Journal of Intelligent and Robotic Systems
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
Randomized pursuit-evasion in a polygonal environment
IEEE Transactions on Robotics
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In this work, we present a strategy for intercepting a highly maneuverable evader while exploiting delayed and imperfect state information. The imperfect sensing arises from uncertain sensor measurement, data losses, communication delays and conflict resolution among objectives such as communication and detection. This problem is further complicated by the challenge of predicting evader's plan-of-action with incomplete and delayed information about evader's current states. To solve this problem, we suggest a probabilistic approach using a) probability distribution of evader's feasible trajectory envelope that can be reached in sequential time-steps and, b) evader's possible motivations such as intelligent evasion and heading to valuable landmarks and way outs. The method is not critically based on exact model of the evader's behaviors and robust to imperfect measurement about evader's location. High uncertainty in detection and estimation of evader's location is exploited by probabilistically modeling its motion and action plan. We have demonstrated performance of this approach in simulations using trade off between success of interception and rate of uncertainty in sensing and communication lags.