Path planning for multi-quadrotor 3D boundary surveillance using non-autonomous discrete memristor hyperchaotic system

TL;DR

This paper introduces a novel boundary surveillance method using a non-autonomous discrete memristor hyperchaotic system to generate unpredictable, adaptable, and independent trajectories for multiple quadrotors, enhancing coverage and coordination.

Contribution

It develops a hyperchaotic map-based path planning approach that improves unpredictability, adaptability, and multi-robot coordination in 3D boundary surveillance tasks.

Findings

Hyperchaotic trajectories are highly unpredictable and adaptable.

Multiple quadrotors can operate simultaneously with minimal interference.

The method enhances robustness and flexibility in boundary coverage.

Abstract

Recent studies have shown that chaotic maps are well-suited for applications requiring unpredictable behaviour, making them a valuable tool for enhancing unpredictability and complexity. A method is developed using 3D parametric equations to make boundary surveillance more robust and flexible to effectively cover and adapt to the ever-changing situations of boundary surveillance. A non-autonomous discrete memristor hyperchaotic map is utilized to significantly enhance the unpredictability of trajectories in boundary surveillance. Since the scaling property is an affine transformation, and the hyperchaotic systems are invariant under affine transformations, the adaptability of hyperchaotic trajectories is visually demonstrated by applying the scaling property for robust path planning. Furthermore, hyperchaotic systems enable the generation of mutually independent trajectories with slight variations in initial conditions, allowing multiple quadrotors to operate simultaneously along a single guiding path. This minimizes the chance of overlap and interference, ensuring effective and coordinated surveillance.