Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

TL;DR

This paper presents a finite element method-based approach for multi-agent ergodic area surveying that effectively handles obstacles and constraints, demonstrating promising simulation results for autonomous surveying tasks.

Contribution

It introduces a finite element approximation of the potential field within the HEDAC framework, enabling obstacle inclusion and constraint handling in multi-agent ergodic control.

Findings

Effective obstacle and boundary avoidance in simulations

Robust handling of minimal clearance and curvature constraints

Promising results for real-world autonomous surveying applications

Abstract

Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of the Helmholtz partial differential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance distance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escape route maneuvers. This approach provides a guaranteed collision avoidance mechanism while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the effectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering difficulties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indicate real-world applicability of the proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.