Online search of unknown terrains using a dynamical system-based path planning approach

TL;DR

This paper presents a scalable chaotic path planning method for robots that improves coverage efficiency in unknown environments by coupling two chaotic systems, achieving faster coverage with unpredictable yet effective trajectories.

Contribution

Introduces a novel technique coupling two chaotic systems to enhance coverage speed and scalability in unknown environment exploration.

Findings

49% improvement in coverage performance

Maintains unpredictability while achieving near-optimal coverage

Scalable approach for obstacle avoidance and environment scanning

Abstract

Surveillance and exploration of large environments is a tedious task. In spaces with limited environmental cues, random-like search is an effective approach as it allows the robot to perform online coverage of environments using simple algorithm designs. One way to generate random-like scanning search is to use nonlinear dynamical systems to impart chaos into the searching robot's controller. This will result in the generation of unpredictable yet deterministic trajectories, allowing designers to control the system and achieve a high scanning coverage of an area. However, the unpredictability comes at the cost of increased coverage time and a lack of scalability, both of which have been ignored by the state-of-the-art chaotic path planners. This work introduces a new, scalable technique that helps a robot to steer away from the obstacles and cover the entire search space in a short period of time. The technique involves coupling and manipulating two chaotic systems to reduce the coverage time and enable scanning of unknown environments with different online properties. Using this new technique resulted in an average 49% boost in the robot's performance compared to the state-of-the-art planners. the overall search performance of the chaotic planner remained comparable to optimal systems while still ensuring unpredictable paths.