Motion planning in high-dimensional spaces
This is an incremental overview for researchers and practitioners in robotics, summarizing existing methods without introducing new solutions.
The paper provides an overview of motion planning strategies for high-dimensional spaces, comparing grid search, sampling-based, and trajectory optimization approaches to address the need for fast algorithms in complex dynamic environments.
Motion planning is a key tool that allows robots to navigate through an environment without collisions. The problem of robot motion planning has been studied in great detail over the last several decades, with researchers initially focusing on systems such as planar mobile robots and low degree-of-freedom (DOF) robotic arms. The increased use of high DOF robots that must perform tasks in real time in complex dynamic environments spurs the need for fast motion planning algorithms. In this overview, we discuss several types of strategies for motion planning in high dimensional spaces and dissect some of them, namely grid search based, sampling based and trajectory optimization based approaches. We compare them and outline their advantages and disadvantages, and finally, provide an insight into future research opportunities.