Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Abstract

Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

“…Over the past decade, mobile robots have been effectively adapted to carry out vital unmanned tasks in various fields. Application areas of path-planning algorithms include but are not confined to security, vigilance [1], planetary exploration [2], route planning of Unmanned Aerial Vehicle (UAV) [3,4], and molecular simulation [5]. Path-planning for mobile robots deals with feasible path generation from a starting position to a goal position by avoiding collision with obstacles in an environment [6].…”

“…Therefore, it is often preferred to acquire a feasible solution rather than to achieve optimality. The criteria of optimal path for mobile robots is often based on one or more features such as shortest distance, low risk, smoothness, maximum area coverage, and fewer energy requirements considering different application constraints [3,6]. One path quality may be desirable based on the type of application.…”

“…Several planners have been used for mobile robots such as potential field, visibility graph, evolutionary meta-heuristic methods, sampling-based methods, and grid-based methods [3]. Each of them has their own advantages and disadvantages.…”

Analysis of the Hybrid Global Path Planning Algorithm for Different Environments

“…The extended algorithmquad-RRT, computes four trees instead of two, this way speeding up the classic RRT, however it requires more hardware and computational resources than the other types of RRT based algorithms. A review on different RRT approaches is given in (Noreen et al, 2016). According to (Noreen et al, 2016), though the RRTs gained immense popularity due to its support for high dimensional complex problems, slow convergence and high memory requirements are still open research issues for the RRT algorithms.…”

“…A review on different RRT approaches is given in (Noreen et al, 2016). According to (Noreen et al, 2016), though the RRTs gained immense popularity due to its support for high dimensional complex problems, slow convergence and high memory requirements are still open research issues for the RRT algorithms. To solve the problem of global path planning, researchers in (Araujo de Lemos et al, 2015) used a splines based algorithm.…”