Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

Motion planning is a fundamental problem in autonomous robotics that requires finding a
path to a specified goal that avoids obstacles and takes into account a robot's limitations and …

Informed sampling-based planning algorithms exploit problem knowledge for better search
performance. This knowledge is often expressed as heuristic estimates of solution cost and …

Optimal path planning is the problem of finding a valid sequence of states between a start
and goal that optimizes an objective. Informed path planning algorithms order their search …

In actual welding scenarios, an effective path planner is needed to find a collision-free path
in the configuration space for the welding manipulator with obstacles around. However, as a …

Path planning is an active area of research essential for many applications in robotics.
Popular techniques include graph-based searches and sampling-based planners. These …

The offline path planning problem for arc welding robots becomes increasingly difficult to
achieve with the increasing product complexity and the open work environment, such as the …

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex
environments is more challenging than conventional fixed-shaped robots due to their …

Sampling-based methods are widely adopted solutions for robot motion planning. The
methods are straightforward to implement, effective in practice for many robotic systems. It is …

Path planning for unmanned aerial vehicles (UAVs) has been widely considered in various
tasks. Existing path planning algorithms, such as A* and Jump Point Search, have been …