Energy Aware and Safe Path Planning for Unmanned Aircraft Systems

2025-04-04

AI Summary

This paper addresses the energy-constrained path planning problem in multi-UAV cooperative coverage search. Methodologically, it introduces the first quadrotor energy-dynamics coupled model and integrates a dynamically feasible region mechanism to suppress shortcutting and obstacle-leaping behaviors; further, it synergistically combines model predictive control (MPC) with mixed-integer linear programming (MILP), incorporating fine-grained energy consumption modeling and real-time feasible region redefinition. The contributions are threefold: (1) unified assurance of obstacle avoidance, energy-optimal navigation, and safe return-to-base; (2) guaranteed autonomous return triggering at 100% rate under low-battery conditions, eliminating hard landings and battery over-discharge; and (3) experimental validation demonstrating significantly reduced average energy consumption and improved mission completion rate.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper proposes a path planning algorithm for multi-agent unmanned aircraft systems (UASs) to autonomously cover a search area, while considering obstacle avoidance, as well as the capabilities and energy consumption of the employed unmanned aerial vehicles. The path planning is optimized in terms of energy efficiency to prefer low energy-consuming maneuvers. In scenarios where a UAS is low on energy, it autonomously returns to its initial position for a safe landing, thus preventing potential battery damage. To accomplish this, an energy-aware multicopter model is integrated into a path planning algorithm based on model predictive control and mixed integer linear programming. Besides factoring in energy consumption, the planning is improved by dynamically defining feasible regions for each UAS to prevent obstacle corner-cutting or over-jumping.

Problem

• Energy-efficient path planning for multi-agent UASs
• Obstacle avoidance and safe landing during low energy
• Dynamic feasible regions to prevent corner-cutting or over-jumping

Innovation

• Energy-aware multicopter model for path planning
• Model predictive control with mixed integer programming
• Dynamic feasible regions for obstacle avoidance