Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Multi Agent Pathfinding for Noise Restricted Hybrid Fuel Unmanned Aerial Vehicles

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

TL;DR

This work addresses the MAPF problem for a fleet of hybrid-fuel unmanned aerial vehicles which are subject to location-dependent noise restrictions, and presents the experimental results of the algorithms for various graph sizes and number of agents.

Abstract

Multi Agent Path Finding (MAPF) seeks the optimal set of paths for multiple agents from respective start to goal locations such that no paths conflict. We address the MAPF problem for a fleet of hybrid-fuel unmanned aerial vehicles which are subject to location-dependent noise restrictions. We solve this problem by searching a constraint tree for which the subproblem at each node is a set of shortest path problems subject to the noise and fuel constraints and conflict zone avoidance. A labeling algorithm is presented to solve this subproblem, including the conflict zones which are treated as dynamic obstacles. We present the experimental results of the algorithms for various graph sizes and number of agents.

Multi Agent Pathfinding for Noise Restricted Hybrid Fuel Unmanned Aerial Vehicles

TL;DR

This work addresses the MAPF problem for a fleet of hybrid-fuel unmanned aerial vehicles which are subject to location-dependent noise restrictions, and presents the experimental results of the algorithms for various graph sizes and number of agents.

Abstract

Multi Agent Path Finding (MAPF) seeks the optimal set of paths for multiple agents from respective start to goal locations such that no paths conflict. We address the MAPF problem for a fleet of hybrid-fuel unmanned aerial vehicles which are subject to location-dependent noise restrictions. We solve this problem by searching a constraint tree for which the subproblem at each node is a set of shortest path problems subject to the noise and fuel constraints and conflict zone avoidance. A labeling algorithm is presented to solve this subproblem, including the conflict zones which are treated as dynamic obstacles. We present the experimental results of the algorithms for various graph sizes and number of agents.
Paper Structure (9 sections, 1 equation, 7 figures, 1 algorithm)

This paper contains 9 sections, 1 equation, 7 figures, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Problem Formulation
  3. Constant Values
  4. Decision Variables
  5. MILP Formulation
  6. Constraint Tree and Conflict Based Search
  7. Temporal Labeling Algorithm
  8. Results
  9. Conclusions

Figures (7)

  • Figure 1: Conflict Based Search - Partial CT Example
  • Figure 2: Relaxed Problem (No Conflict Constraints)- 5 UAVs - 100 Nodes - Noise Restricted Zones Hidden - 1 Time Step per Edge - Uniform Start Time
  • Figure 3: MAPF CBS Solution - 5 UAVs - 100 Nodes - Noise Restricted Zones Hidden - 1 Time Step per Edge - Uniform Start Time
  • Figure 4: Generator Patterns from CBS Solution - 5 UAVs - 100 Nodes
  • Figure 5: CBS Nodes Searched vs Problem Size
  • ...and 2 more figures