Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments

TL;DR

This paper introduces a novel distance-based multi-drone encirclement strategy for complex, obstacle-rich environments, combining target state estimation, anti-synchronization, and cooperative control validated through simulations and experiments.

Contribution

It presents a new distance-based target state estimator and a cooperative encirclement control method for multi-drone systems in GPS-denied, obstacle-filled settings.

Findings

The proposed algorithms are theoretically validated for observability.

Simulation results demonstrate successful encirclement in complex environments.

Experimental validation confirms practical effectiveness.

Abstract

This paper proposes a comprehensive strategy for complex multi-target-multi-drone encirclement in an obstacle-rich and GPS-denied environment, motivated by practical scenarios such as pursuing vehicles or humans in urban canyons. The drones have omnidirectional range sensors that can robustly detect ground targets and obtain noisy relative distances. After each drone task is assigned, a novel distance-based target state estimator (DTSE) is proposed by estimating the measurement output noise variance and utilizing the Kalman filter. By integrating anti-synchronization techniques and pseudo-force functions, an acceleration controller enables two tasking drones to cooperatively encircle a target from opposing positions while navigating obstacles. The algorithms effectiveness for the discrete-time double-integrator system is established theoretically, particularly regarding observability. Moreover, the versatility of the algorithm is showcased in aerial-to-ground scenarios, supported by compelling simulation results. Experimental validation demonstrates the effectiveness of the proposed approach.