activity
20242026
collaborators

6 papers

cs.RO2026

LSRP*: Scalable and Anytime Planning for Multi-Agent Path Finding with Asynchronous Actions

Dachuan Liu, Shuai Zhou, Shizhe Zhao +1

Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path cos…

cs.RO2026

Optimal Solutions for the Moving Target Vehicle Routing Problem via Branch-and-Price with Relaxed Continuity

Anoop Bhat, Geordan Gutow, Zhongqiang Ren +2

The Moving Target Vehicle Routing Problem (MT-VRP) seeks trajectories for several agents that intercept a set of moving targets, subject to speed, time window, and capacity constra…

cs.RO2025

Ergodic Trajectory Planning with Dynamic Sensor Footprints

Ziyue Zheng, Yongce Liu, Hesheng Wang +1

This paper addresses the problem of trajectory planning for information gathering with a dynamic and resolution-varying sensor footprint. Ergodic planning offers a principled frame…

cs.RO2025

HEHA: Bounded Suboptimal Planning for Constrained Routing and Its Use in Heterogeneous Multi-Robot Exploration of Unknown Environments

Longrui Yang, Yiyu Wang, Jingfan Tang +5

This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots,…

cs.RO2025

Conflict-Based Search and Prioritized Planning for Multi-Agent Path Finding Among Movable Obstacles

Shaoli Hu, Shizhe Zhao, Zhongqiang Ren

This paper investigates Multi-Agent Path Finding Among Movable Obstacles (M-PAMO), which seeks collision-free paths for multiple agents from their start to goal locations among sta…

cs.MA2024

Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions

Shuai Zhou, Shizhe Zhao, Zhongqiang Ren

Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path cos…