In this paper, we present a simultaneous exploration and object search
f...
Robots have become increasingly prevalent in dynamic and crowded environ...
Rapidly-exploring Random Trees (RRTs) are a popular technique for autono...
Multi-robot path planning is a computational process involving finding p...
Robot teleoperation gains great success in various situations, including...
The development of autonomous vehicles has brought a great impact and ch...
Cooperative object transportation using multiple robots has been intensi...
Autonomous fabric manipulation is a challenging task due to complex dyna...
This article addresses the localization problem in robotic autonomous lu...
The efficiency of sampling-based motion planning brings wide application...
To facilitate recent advances in robotics and AI for delicate collaborat...
This paper introduces a quadrotor's autonomous take-off and landing syst...
Reorienting objects using extrinsic supporting items on the working plat...
Balancing the trade-off between safety and efficiency is of significant
...
Adaptively Informed Trees (AIT*) develops the problem-specific heuristic...
Motion Planning is necessary for robots to complete different tasks.
Rap...
Sampling-based path planning algorithms usually implement uniform sampli...
The sampling-based motion planning algorithms can solve the motion plann...
In this paper, we present a novel path planning algorithm to achieve fas...
Autonomous mobile manipulation robots that can collect trolleys are wide...
Non-prehensile multi-object rearrangement is a robotic task of planning
...
Colonoscopy is a standard imaging tool for visualizing the entire
gastro...
Being able to explore unknown environments is a requirement for fully
au...
Interactions with either environments or expert policies during training...
Sampling-based path planning is a popular methodology for robot path
pla...
Robot path planning is difficult to solve due to the contradiction betwe...
Path planning plays an important role in autonomous robot systems. Effec...