5 papers · 1 filter
VIP: Variation-based Iterative-learning Planning for Robotic Navigation
Shuli Lv, Pengda Mao, Chen Min +4
Over the past decade, autonomous robotic systems have been increasingly deployed in applications such as surveying, search and rescue, and last-mile delivery. These applications re…
Energy-Optimal Spatial Iterative Learning within a Virtual Tube
Chen Min, Shuli Lv, Pengda Mao +3
Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are s…
An Efficient Real-Time Planning Method for Swarm Robotics Based on an Optimal Virtual Tube
Pengda Mao, Shuli Lv, Chen Min +2
Robot swarms navigating through unknown obstacle environments are an emerging research area that faces challenges. Performing tasks in such environments requires swarms to achieve…
Virtual-Tube-Based Cooperative Transport Control for Multi-UAV Systems in Constrained Environments
Runxiao Liu, Pengda Mao, Xiangli Le +3
This paper proposes a novel control framework for cooperative transportation of cable-suspended loads by multiple unmanned aerial vehicles (UAVs) operating in constrained environme…
Tube RRT*: Efficient Homotopic Path Planning for Swarm Robotics Passing-Through Large-Scale Obstacle Environments
Pengda Mao, Shuli Lv, Quan Quan
Recently, the concept of homotopic trajectory planning has emerged as a novel solution to navigation in large-scale obstacle environments for swarm robotics, offering a wide rangin…