Multi-Objective Safe-Interval Path Planning With Dynamic Obstacles

Article Automation & Control Systems

A Conflict-Based Search Framework for Multiobjective Multiagent Path Finding

Zhongqiang Ren et al.

Summary: Conventional multi-agent path planners focus on optimizing a single objective, such as path length, but many applications require multiple objectives to be simultaneously optimized. This article presents a novel approach called MO-CBS that leverages prior algorithms to address the curse of dimensionality and compute the Pareto-optimal set efficiently. Numerical results show that MO-CBS outperforms existing state-of-the-art planners.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2023)

Add to Collection

Article Robotics

Multi-Objective Path-Based D* Lite

Zhongqiang Ren et al.

Summary: This article introduces a new multi-objective incremental search algorithm called MOPBD*, which leverages path-based expansion strategy to prune dominated solutions. A sub-optimal variant of MOPBD* is also introduced to improve search efficiency while approximating the Pareto-optimal front. Numerical evaluations show that our approach is more efficient than search from scratch and runs up to an order of magnitude faster than the existing incremental method for multi-objective path planning.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

Add to Collection

Article Robotics

Subdimensional Expansion for Multi-Objective Multi-Agent Path Finding

Zhongqiang Ren et al.

Summary: This letter presents an approach to avoid the "curse of dimensionality" by leveraging prior multi-agent work and a framework called subdimensional expansion, resulting in a new algorithm called multi-objective M*(MOM*). MOM* efficiently computes the complete Pareto-optimal set for multiple agents, naturally trading off sub-optimal approximations of the Pareto-optimal set and computational efficiency.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

Add to Collection

Proceedings Paper Automation & Control Systems