Self-triggered Pursuit of a Single Evader with Uncertain Information

TL;DR

This paper introduces a self-triggered pursuit strategy for a pursuer to capture an evader with uncertain position information, reducing sampling frequency while guaranteeing capture within the same time frame as traditional methods.

Contribution

It extends previous pursuit algorithms to handle position uncertainties and incorporates evader history to improve estimation, ensuring capture without continuous information.

Findings

Guarantees evader capture with uncertain information.

Reduces sampling frequency compared to continuous observation methods.

Maintains performance in terms of time-to-capture.

Abstract

This paper studies a pursuit-evasion problem involving a single pursuer and a single evader, where we are interested in developing a pursuit strategy that doesn't require continuous, or even periodic, information about the position of the evader. We propose a self-triggered control strategy that allows the pursuer to sample the evader's position autonomously, while satisfying desired performance metric of evader capture. The work in this paper builds on the previously proposed self-triggered pursuit strategy which guarantees capture of the evader in finite time with a finite number of evader samples. However, this algorithm relied on the unrealistic assumption that the evader's exact position was available to the pursuer. Instead, we extend our previous framework to develop an algorithm which allows for uncertainties in sampling the information about the evader, and derive tolerable upper-bounds on the error such that the pursuer can guarantee capture of the evader. In addition, we outline the advantages of retaining the evader's history in improving the current estimate of the true location of the evader that can be used to capture the evader with even less samples. Our approach is in sharp contrast to the existing works in literature and our results ensure capture without sacrificing any performance in terms of guaranteed time-to-capture, as compared to classic algorithms that assume continuous availability of information.