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Flow-Inspired Multi-Robot Real-Time Scheduling Planner

Han Liu, Yu Jin, Tianjiang Hu, Kai Huang

TL;DR

This work addresses the problem of rapidly guiding multiple robots through obstacle-rich environments by introducing a real-time, flow-inspired scheduling planner that builds a network abstraction of the map using Boustrophedon decomposition. The planner assigns robots to paths based on congestion through a three-stage process: path set search, MIQP-based path node selection with link-queueing and running costs, and local position allocation, while collision avoidance remains a separate, higher-frequency layer. Key contributions include the first application of flow-based ideas to real-time multi-robot scheduling, a network of PathNodes and PathPos with derived capacities, and an MIQP formulation that balances detours, waiting, and throughput. The approach is validated in both simulated forest/maze maps and real-world flight tests with ten drones, demonstrating improved traversal time and real-time scalability, with potential for more complex scenarios and obstacle shapes.

Abstract

Collision avoidance and trajectory planning are crucial in multi-robot systems, particularly in environments with numerous obstacles. Although extensive research has been conducted in this field, the challenge of rapid traversal through such environments has not been fully addressed. This paper addresses this problem by proposing a novel real-time scheduling scheme designed to optimize the passage of multi-robot systems through complex, obstacle-rich maps. Inspired from network flow optimization, our scheme decomposes the environment into a network structure, enabling the efficient allocation of robots to paths based on real-time congestion data. The proposed scheduling planner operates on top of existing collision avoidance algorithms, focusing on minimizing traversal time by balancing robot detours and waiting times. Our simulation results demonstrate the efficiency of the proposed scheme. Additionally, we validated its effectiveness through real world flight tests using ten quadrotors. This work contributes a lightweight, effective scheduling planner capable of meeting the real-time demands of multi-robot systems in obstacle-rich environments.

Flow-Inspired Multi-Robot Real-Time Scheduling Planner

TL;DR

This work addresses the problem of rapidly guiding multiple robots through obstacle-rich environments by introducing a real-time, flow-inspired scheduling planner that builds a network abstraction of the map using Boustrophedon decomposition. The planner assigns robots to paths based on congestion through a three-stage process: path set search, MIQP-based path node selection with link-queueing and running costs, and local position allocation, while collision avoidance remains a separate, higher-frequency layer. Key contributions include the first application of flow-based ideas to real-time multi-robot scheduling, a network of PathNodes and PathPos with derived capacities, and an MIQP formulation that balances detours, waiting, and throughput. The approach is validated in both simulated forest/maze maps and real-world flight tests with ten drones, demonstrating improved traversal time and real-time scalability, with potential for more complex scenarios and obstacle shapes.

Abstract

Collision avoidance and trajectory planning are crucial in multi-robot systems, particularly in environments with numerous obstacles. Although extensive research has been conducted in this field, the challenge of rapid traversal through such environments has not been fully addressed. This paper addresses this problem by proposing a novel real-time scheduling scheme designed to optimize the passage of multi-robot systems through complex, obstacle-rich maps. Inspired from network flow optimization, our scheme decomposes the environment into a network structure, enabling the efficient allocation of robots to paths based on real-time congestion data. The proposed scheduling planner operates on top of existing collision avoidance algorithms, focusing on minimizing traversal time by balancing robot detours and waiting times. Our simulation results demonstrate the efficiency of the proposed scheme. Additionally, we validated its effectiveness through real world flight tests using ten quadrotors. This work contributes a lightweight, effective scheduling planner capable of meeting the real-time demands of multi-robot systems in obstacle-rich environments.
Paper Structure (16 sections, 16 equations, 11 figures, 2 tables)

This paper contains 16 sections, 16 equations, 11 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. related work
  3. Problem Statement
  4. Network construction
  5. $\operatorname{PathNode}$ and $\operatorname{PathPos}$
  6. State variable
  7. Control variable
  8. Scheduling Planner
  9. Path set search
  10. Path node selection
  11. Link queuing cost
  12. Running cost
  13. Local position allocation
  14. Experiments
  15. Real World Flight Test
  16. ...and 1 more sections

Figures (11)

  • Figure 1: A motivation example.
  • Figure 2: The network construction process. The blue circles represent the $\operatorname{PathNode}$ of the network, and the red circles represent the $\operatorname{PathPos}$ of each node.
  • Figure 3: The capacity of link
  • Figure 4: State and control variables example
  • Figure 5: The framework of our scheduling planner
  • ...and 6 more figures