# L\'evy Flight Foraging Hypothesis-based Autonomous Memoryless Search Under Sparse Rewards

## TL;DR

This paper proposes a Le9vy flight-based autonomous search method optimized for time-efficient detection of sparsely distributed targets in large environments, emphasizing memoryless exploration without prior map knowledge.

## Contribution

It introduces a novel Le9vy flight search strategy specifically designed for memoryless, sparse reward environments, addressing a gap in autonomous exploration research.

## Key findings

- Le9vy flight search outperforms traditional methods in sparse environments.
- The approach is effective for large-scale area exploration.
- Results validate the optimality of Le9vy walks for target detection in memoryless scenarios.

## Abstract

Autonomous robots are commonly tasked with the problem of area exploration and search for certain targets or artifacts of interest to be tracked. Traditionally, the problem formulation considered is that of complete search and thus - ideally - identification of all targets of interest. An important problem however which is not often addressed is that of time-efficient memoryless search under sparse rewards that may be worth visited any number of items. In this paper we specifically address the largely understudied problem of optimizing the "time-of-arrival" or "time-of-detection" to robotically search for sparsely distributed rewards (detect targets of interest) within large-scale environments and subject to memoryless exploration. At the core of the proposed solution is the fact that a search-based L\'evy walk consisting of a constant velocity search following a L\'evy flight path is optimal for searching sparse and randomly distributed target regions in the lack of map memory. A set of results accompany the presentation of the method, demonstrate its properties and justify the purpose of its use towards large-scale area exploration autonomy.