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.
Highlights
The term path planning refers to collision free path generation from an initial state to a specified goal state with optimal or near optimal cost
It is essential to introduce the basic operations of Rapidly-exploring Random Tree (RRT)* prior to describe its variant approaches. These procedures are found in all RRT* variants, but their implementation may differ in different planners and applications
Research over the past decade has revealed that traditional path planning methods are not feasible for non-holonomic, www.ijacsa.thesai.org cluttered and high dimensional problems
Summary
The term path planning refers to collision free path generation from an initial state to a specified goal state with optimal or near optimal cost. Many other evolutionary algorithms such as Artificial Bee Colony (ABC) [22], Bacterial Foraging Optimization (BFO) [23], Bio Inspired Neural Networks [24, 25], and Fire Fly algorithm [26] are often trapped in local optimum, and bear high computational cost They are highly sensitive to search space size and data representation scheme of problem [27, 28]. Major advantages of Sampling Based Planning (SBP) are low computational cost, applicability to high dimensional problems and better success rate for complex problems [8, 29]. Introduced by Karaman and Frazzoli [7], RRT* was a major breakthrough in optimal path planning for high dimensional problems. These procedures are found in all RRT* variants, but their implementation may differ in different planners and applications
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