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Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs

Drew Scott, Satyanarayana G. Manyam, Isaac E. Weintraub, David W. Casbeer, Manish Kumar

Abstract

Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of multiple energy sources, offer several advantages over the standard single fuel source configuration, the primary one being increased range and efficiency. Multiple power or fuel sources also allow the distinct pitfalls of each source to be mitigated while exploiting the advantages within the mission or path planning. We consider here a UAV equipped with a combustion engine-generator and battery pack as energy sources. We consider the path planning and power-management of this platform in a noise-aware manner. To solve the path planning problem, we first present the Mixed Integer Linear Program (MILP) formulation of the problem. We then present and analyze a label-correcting algorithm, for which a pseudo-polynomial running time is proven. Results of extensive numerical testing are presented which analyze the performance and scalability of the labeling algorithm for various graph structures, problem parameters, and search heuristics. It is shown that the algorithm can solve instances on graphs as large as twenty thousand nodes in only a few seconds.

Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs

Abstract

Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of multiple energy sources, offer several advantages over the standard single fuel source configuration, the primary one being increased range and efficiency. Multiple power or fuel sources also allow the distinct pitfalls of each source to be mitigated while exploiting the advantages within the mission or path planning. We consider here a UAV equipped with a combustion engine-generator and battery pack as energy sources. We consider the path planning and power-management of this platform in a noise-aware manner. To solve the path planning problem, we first present the Mixed Integer Linear Program (MILP) formulation of the problem. We then present and analyze a label-correcting algorithm, for which a pseudo-polynomial running time is proven. Results of extensive numerical testing are presented which analyze the performance and scalability of the labeling algorithm for various graph structures, problem parameters, and search heuristics. It is shown that the algorithm can solve instances on graphs as large as twenty thousand nodes in only a few seconds.
Paper Structure (13 sections, 5 theorems, 1 equation, 9 figures, 3 algorithms)

This paper contains 13 sections, 5 theorems, 1 equation, 9 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Problem Formulation
  4. MILP Formulation
  5. Decision Variables
  6. Constant Parameters
  7. Objective and Constraints
  8. Labeling Algorithm
  9. Optimality
  10. Expected Running Time
  11. Results
  12. Effect of Graph Connectivity
  13. Conclusions and Future Work

Key Result

Lemma 1

The optimal solution of the SUP, defined by Equations MILPobj-MILPdeg3, is a lower bound to the Noise Restricted Hybrid Fuel Shortest Path Problem as described in Equations MILPobj-MILPmisc2.

Figures (9)

  • Figure 1: Graph Examples. Noise Restricted Edges in Red.
  • Figure 2: Example Solution - 2D Euclidean Graph - 550 Nodes
  • Figure 3: Computational Times for 2D Euclidean Graph versus Number of Nodes - i) Top: Results for SUP and SLD Lower Bounds (LB) for NODE and LABEL Selection Methods; ii) Middle: Boxplot for NODE-SUP LB using NODE Selection Algorithm; iii) Middle: Boxplot for NODE-SLD LB using LABEL Selection Algorithm
  • Figure 4: Computational Times for 3D Euclidean Graph versus Number of Nodes - i) Top: Results for SUP and SLD Lower Bounds (LB) for NODE and LABEL Selection Methods; ii) Middle: Boxplot for NODE-SUP LB using NODE Selection Algorithm; iii) Middle: Boxplot for NODE-SLD LB using LABEL Selection Algorithm
  • Figure 5: Computational Times for 2D Lattice Graph versus Number of Nodes - i) Top: Results for SUP and SLD Lower Bounds (LB) for NODE and LABEL Selection Methods; ii) Middle: Boxplot for NODE-SUP LB using NODE Selection Algorithm; iii) Middle: Boxplot for NODE-SLD LB using LABEL Selection Algorithm
  • ...and 4 more figures

Theorems & Definitions (10)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • Lemma 4
  • proof
  • Lemma 5
  • proof