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IROS 2022

Multi-Robot Dynamic Swarm Disablement

Conference Paper Accepted Paper Artificial Intelligence ยท Robotics

Abstract

Motivated by the use of robots for pest control in agriculture, this work introduces the Multi-Robot Dynamic Swarm Disablement problem, in which a team of robots is required to disable a swarm of agents (for example, locust agents) passing through an area while minimizing the cumulative time of the swarm members (equivalent to the cumulative damage they cause) in the area. Showing that the problem is hard even in naive settings, we turn to examine algorithms seeking to optimize the robots' performance against the swarm by exploiting the known movement pattern of the swarm agents. Motivated by the poor performance when a weak group of robots attempts to catch a large swarm of agents, whether it is a significant numerical minority or poor speed gaps, we suggest the use of blocking lines: the robots form lines that block the agents along their movement in the environment. We show by both theoretical analysis and rigorous empirical evaluation in different settings that these algorithms outperform common task-assignment-based algorithms, especially for limited robots versus a large swarm.

Authors

Keywords

  • Heuristic algorithms
  • Pest control
  • Agriculture
  • Task analysis
  • Robots
  • Intelligent robots
  • Use Of Robots
  • Environmental Movement
  • Swarm Robotics
  • Robotic Group
  • Agricultural Pest Control
  • Control In Agriculture
  • Horizontal Axis
  • Completion Time
  • Number Of Agents
  • Optimal Path
  • Group Of Agents
  • Assignment Problem
  • Range Of Agents
  • Movement Model
  • Makespan
  • Robot Movement
  • Partial Block
  • Speed Ratio
  • Progress In This Direction
  • Optimal Assignment
  • Robot Path
  • Optimal Movement
  • Single Robot
  • Assignment Algorithm
  • Velocity Of Agent
  • Position Of Agent
  • Optimization Problem
  • Task Completion Time
  • Optimization Criteria

Context

Venue
IEEE/RSJ International Conference on Intelligent Robots and Systems
Archive span
1988-2025
Indexed papers
26578
Paper id
91562263233935242
v2026.09.13