Sampling-based Path Planning Algorithms: A Survey

TL;DR

This survey reviews sampling-based path planning algorithms like PRM and RRT, focusing on their optimization techniques and applications in autonomous robot navigation in complex environments.

Contribution

It provides a comprehensive overview of sampling-based algorithms, including their optimized versions PRM* and RRT*, highlighting their mechanisms and benefits.

Findings

PRM and RRT are effective for high-dimensional spaces.

Optimized versions PRM* and RRT* improve path quality and convergence.

Sampling-based algorithms are crucial for dynamic and unknown environments.

Abstract

Path planning is a classic problem for autonomous robots. To ensure safe and efficient point-to-point navigation an appropriate algorithm should be chosen keeping the robot's dimensions and its classification in mind. Autonomous robots use path-planning algorithms to safely navigate a dynamic, dense, and unknown environment. A few metrics for path planning algorithms to be taken into account are safety, efficiency, lowest-cost path generation, and obstacle avoidance. Before path planning can take place we need map representation which can be discretized or open configuration space. Discretized configuration space provides node/connectivity information from one point to another. While in open/free configuration space it is up to the algorithm to create a list of nodes and then find a feasible path. Both types of maps are populated by obstacle positions using perception obstacle detection techniques to represent current obstacles from the perspective of the robot. For open configuration spaces, sampling-based planning algorithms are used. This paper aims to explore various types of Sampling-based path-planning algorithms such as Probabilistic RoadMap (PRM), and Rapidly-exploring Random Trees (RRT). These two algorithms also have optimized versions - PRM* and RRT* and this paper discusses how that optimization is achieved and is beneficial.