Modified continuous Ant Colony Optimisation for multiple Unmanned Ground Vehicle path planning
作者：
Highlights：
• A new ACOPARis designed for optimising the path for each UGV.
• A new random-walk strategy switching between Brownian and Cauchy motion is designed.
• Adaptive waypoints-repair-strategy to improve search accuracy and scalability.
• Multi-agent coordination is designed to avoid the collision among UGVs.
• Experiments validate the superiority of ACOPAR, especially on complex problems.
摘要
•A new ACOPARis designed for optimising the path for each UGV.•A new random-walk strategy switching between Brownian and Cauchy motion is designed.•Adaptive waypoints-repair-strategy to improve search accuracy and scalability.•Multi-agent coordination is designed to avoid the collision among UGVs.•Experiments validate the superiority of ACOPAR, especially on complex problems.
论文关键词：Ant Colony Optimisation,ACO,Unmanned Ground Vehicles,multi-UGV,Path planning
论文评审过程：Received 21 April 2021, Revised 1 January 2022, Accepted 22 January 2022, Available online 10 February 2022, Version of Record 17 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116605
