Visibility-based Pursuit-evasion with Limited Field of View

Gerkey, Brian; Thrun, Sebastian; Gordon, Geoff

doi:10.1177/0278364906065023

Cited by 151 publications

(104 citation statements)

References 31 publications

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“…In an indoor environment, the pursuit-evasion problem can be solved with a guarantee that the evader will be caught. Such results are found in [16,17]. Some of this work also deals with the situation where the area is unknown.…”

Section: Introductionsupporting

confidence: 61%

Minimum Time Multi-UGV Surveillance

Anisi

Ögren

2009

Optimization and Cooperative Control Strategies

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Abstract. This chapter addresses the problem of concurrent task and path planning for a number of surveillance Unmanned Ground Vehicles (UGVs) such that a user defined area of interest is covered by the UGVs' sensors in minimum time. We first formulate the problem, and show that it is in fact a generalization of the Multiple Traveling Salesmen Problem (MTSP), which is known to be N P-hard. We then propose a solution that decomposes the problem into three subproblems. The first is to find a maximal convex covering of the search area. Most results on static coverage use disjoint partitions of the search area, e.g., triangulation, to convert the continuous sensor positioning problem into a discrete one. However, by a simple example, we show that a highly overlapping set of maximal convex sets is better suited for minimum time coverage. The second subproblem is a combinatorial assignment and ordering of the sets in the cover. Since the Tabu search algorithm is known to perform well on various routing problems, we use it as a part of our proposed solution. Finally, the third subproblem utilizes a particular shortest path subroutine in order to find the vehicle paths, and calculate the overall objective function used in the Tabu search. The proposed algorithm is illustrated by a number of simulation examples.

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Section: Introductionsupporting

confidence: 61%

Minimum Time Multi-UGV Surveillance

Anisi

Ögren

2009

Optimization and Cooperative Control Strategies

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“…The type of tasks for which the presented approach is applicable is described in Section 3. Examples of other application domains include variations on the pursuit and evasion task during which a robotic vehicle attempts to keep track of another moving object operating in a certain area (Gerkey et al 2006), and robotic soccer (Kohl and Miikkulainen 2008).…”

Section: Resultsmentioning

confidence: 99%

Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments

Gupta

2011

Auton Robot

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We address the problem of automated action selection policy synthesis for unmanned vehicles operating in adverse environments. We introduce a new evolutionary computation-based approach using which an initial version of the policy is automatically generated and then gradually refined by detecting and fixing its shortcomings. The synthesis technique consists of the automated extraction of the vehicle's exception states and Genetic Programming (GP) for automated composition and optimization of corrective sequences of commands in the form of macro-actions to be applied locally.The focus is specifically on automated synthesis of a policy for Unmanned Surface Vehicle (USV) to efficiently block the advancement of an intruder boat toward a valuable target. This task requires the USV to utilize reactive planning complemented by short-term forward planning to generate specific maneuvers for blocking. The intruder is human-competitive and exhibits a deceptive behavior so that the USV cannot exploit regularity in its attacking behavior. We compared the performance of a hand-coded blocking policy to the performance of a policy that was automatically synthesized. Our results show that the performance of the automatically generated policy exceeds the performance of the hand-coded policy and thus demonstrates the feasibility of the proposed approach.

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“…In an indoor environment, the pursuit-evasion problem can be solved with a guarantee that a moving evader will be caught [12]. Some of this work also deals with the situation where the area is unknown [13].…”

Section: Related Workmentioning

confidence: 99%

Cooperative surveillance missions with multiple unmanned ground vehicles (UGVs)

Anisi¹,

Ögren

Hu³

et al. 2008

2008 47th IEEE Conference on Decision and Control

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Abstract-This paper proposes an optimization based approach to multi-UGV surveillance. In particular, we formulate both the minimum time-and connectivity constrained surveillance problems, show N P-hardness of them and propose decomposition techniques that allow us to solve them efficiently in an algorithmic manner.The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypointpaths for all UGVs such that every point of the area is visible from a point on a path and such that the time for executing the search in parallel is minimized. Here, the sensor's field of view are assumed to be occluded by the obstacles and limited by a maximal sensor range. The connectivity constrained formulation extends the first by additionally requiring that the information graph induced by the sensors is connected at the time instants when the UGVs stop to perform the surveillance task. The second formulation is relevant to situation when mutual visibility is needed either to transmit the sensor data being gathered, or to protect the team from hostile persons trying to approach the stationary UGVs.

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Visibility-based Pursuit-evasion with Limited Field of View

Cited by 151 publications

References 31 publications

Minimum Time Multi-UGV Surveillance

Minimum Time Multi-UGV Surveillance

Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments

Cooperative surveillance missions with multiple unmanned ground vehicles (UGVs)

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