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Energy Aware and Safe Path Planning for Unmanned Aircraft Systems

Sebastian Gasche, Christian Kallies, Andreas Himmel, Rolf Findeisen

TL;DR

We address energy-constrained area coverage for unmanned aircraft systems by integrating an energy-aware multicopter model into a moving-horizon path planning framework based on model predictive control (MPC) and mixed-integer linear programming (MILP). The approach combines an LPV energy-aware vehicle model with MPC/MILP-based planning to produce 3D trajectories that maximize area coverage while respecting obstacle avoidance, geo-fencing, and dynamic safety constraints, and to autonomously return to initial positions for recharging when DoD thresholds are reached. Key contributions include the energy-aware LPV multicopter model with DoD/SoC dependencies, a MILP-based PPA employing Big-$M$, slack variables, polygon and convex-hull approximations, and a comprehensive set of mode-dependent target-distance dynamics for covering, transiting, returning, and landing. The framework demonstrates autonomous, energy-efficient planning for a heterogeneous UAS swarm in dynamic environments, with potential applications in search-and-rescue, surveillance, and monitoring where safe operation and battery management are critical.

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.

Energy Aware and Safe Path Planning for Unmanned Aircraft Systems

TL;DR

We address energy-constrained area coverage for unmanned aircraft systems by integrating an energy-aware multicopter model into a moving-horizon path planning framework based on model predictive control (MPC) and mixed-integer linear programming (MILP). The approach combines an LPV energy-aware vehicle model with MPC/MILP-based planning to produce 3D trajectories that maximize area coverage while respecting obstacle avoidance, geo-fencing, and dynamic safety constraints, and to autonomously return to initial positions for recharging when DoD thresholds are reached. Key contributions include the energy-aware LPV multicopter model with DoD/SoC dependencies, a MILP-based PPA employing Big-, slack variables, polygon and convex-hull approximations, and a comprehensive set of mode-dependent target-distance dynamics for covering, transiting, returning, and landing. The framework demonstrates autonomous, energy-efficient planning for a heterogeneous UAS swarm in dynamic environments, with potential applications in search-and-rescue, surveillance, and monitoring where safe operation and battery management are critical.

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.

Paper Structure

This paper contains 26 sections, 27 equations, 10 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Energy Aware Multicopter Model
  3. Path Planning Algorithm for Area Coverage
  4. Fundamentals of Model Predictive Control
  5. Mixed Integer Linear Programming
  6. The "big M" Method
  7. Slack Variables
  8. Polygon Approximation
  9. Convex Hull Approximation
  10. Path Planning Algorithm
  11. uav Dynamics and Capabilities
  12. Collision Avoidance
  13. Geo Fence
  14. Obstacles Avoidance
  15. Waypoint Coverage
  16. ...and 11 more sections

Figures (10)

  • Figure 1: Frames of reference (black: inertial frame, red: body-fixed frame); Forces/torques acting on the body's center of mass (blue) Gasche2024_ECM
  • Figure 2: Simplified power train of an electric-propelled uav Gasche2024_ECM
  • Figure 3: Structure of the linear energy aware multicopter model Gasche2024_ECM
  • Figure 4: Polygon approximation of a sphere with $H=8$ (left: vertical, right: horizontal)
  • Figure 5: Velocity limitation employing the Euclidean norm (black) vs. polygon approximation (red)
  • ...and 5 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2