Adversarial patrolling with reactive point processes
- Publication Type:
- Conference Proceeding
- Australasian Conference on Robotics and Automation, ACRA, 2016, 2016-December pp. 39 - 46
- Issue Date:
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© 2018 Australasian Robotics and Automation Association. All rights reserved. Adversarial patrolling is an algorithmic problem where a robot visits sites within a given area so as to detect the presence of an adversary. We formulate and solve a new variant of this problem where intrusion events occur at discrete locations and are assumed to be clustered in time. Unlike related formulations, we model the behaviour of the adversary using a stochastic point process known as the reactive point process, which naturally models temporally self-exciting events such as pest intrusion and weed growth in agriculture. We present an asymptotically optimal, anytime algorithm based on Monte Carlo tree search that plans the motion of a robot given a separate event detection system in order to regulate event propagation at the sites it visits. We illustrate the behaviour of our algorithm in simulation using several scenarios, and compare its performance to a lawnmower planning algorithm. Our results indicate that our formulation and solution are promising in enabling practical applications and further theoretical extensions.
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