Search and Pursuit-Evasion in Mobile Robotics
Joint research between divergent disciplines has led to signiﬁcant advances in autonomous search and pursuitevasion with mobile robots. While robotics applications have often served as catalysts for vibrant research at the intersection of traditional disciplines, only recently have researchers undertaken the study of robotic systems for search missions and pursuit-evasion contexts. This article surveys recent advances in this area, which leverage both theoretical foundations and practical implementations to forge new and innovative results. Search and pursuit-evasion problems (also known as “one-sided search” and “adversarial search,” respectively) have traditionally been addressed using two contrasting approaches. One perspective has been to design strategies that maximize searcher performance against a worst-case adversary. In such settings, the evader is often characterized by inﬁnite speed, complete awareness of searcher location and intent, and full knowledge of the search environment. Such methods oﬀer guarantees on the success of the search, deﬁned, for example, by capture of the target in ﬁnite time. However, the powerful adversary model may yield solutions that are too conservative in practical applications. In contrast, parallel research has emphasized probabilistic formulations addressing average-case behaviors. Measures of interest can include expected time until detection or expected number of glimpses. The assumption about knowledge about the evader behavior allows incorporating probabilistic uncertainty in target locations, their behaviors, and/or sensor observations.
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Journal of Industrial Electronics and Applications
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