Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments

Abstract

Rapidly-exploring Random Tree star (RRT*) has recently gained immense popularity in the motion planning community as it provides a probabilistically complete and asymptotically optimal solution without requiring the complete information of the obstacle space. In spite of all of its advantages, RRT* converges to optimal solution very slowly. Hence to improve the convergence rate, its bidirectional variants were introduced, the Bi-directional RRT* (B-RRT*) and Intelligent Bi-directional RRT* (IB-RRT*). However, as both variants perform pure exploration, they tend to suffer in highly cluttered environments. In order to overcome these limitations we introduce a new concept of potentially guided bidirectional trees in our proposed Potentially Guided Intelligent Bi-directional RRT* (PIB-RRT*) and Potentially Guided Bi-directional RRT* (PB-RRT*). The proposed algorithms greatly improve the convergence rate and have a more efficient memory utilization. Theoretical and experimental evaluation of the proposed algorithms have been made and compared to the latest state of the art motion planning algorithms under different challenging environmental conditions and have proven their remarkable improvement in efficiency and convergence rate.

Motion Planning Networks

“…As motion planning algorithms are necessary for solving a variety of complicated, high-dimensional problems ranging from autonomous driving [2] to space exploration [3], there arises a critical, unmet need for computationally tractable, real-time algorithms. The quest for developing computationally efficient motion planning methods has led to the development of various sampling-based motion planning (SMP) algorithms such as Rapidly-exploring Random Trees (RRT) [4], optimal Rapidly-exploring Random Trees (RRT*) [5], Potentially guided-RRT* (P-RRT*) [6] and their bi-directional variants [7], [8]. Despite previous efforts to design fast, efficient planning algorithms, the current state-of-the-art struggles to offer methods which scale to the high-dimensional setting that is common in many real-world applications.…”

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

“…The paper presented in [32] used a hybrid algorithm of A* and RRT for indoor navigation of unmanned aerial vehicle. In [33], they proposed a bidirectional RRT based on potentially guidance to quickly plan paths in a messy environment. Most of these methods have certain advantages in terms of speed, but the path obtained lacks optimization.…”

Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment

“…It is well known that a feasible path of UAV should be planned in 3D environment, for 3D environment is more applicable to low-altitude and terrain-following flight. e 3D environment path planning problem has been widely investigated in [17][18][19][20]. A bidirectional spline-RRT * path planning algorithm has been proposed in [17] for fixed-wing UAVs that can find paths in highly constrained planning environments.…”

“…e RGD heuristic guides the randomly sampled states incrementally downhill in the direction of decreasing potential so that the convergence rate is improved. en, the new bidirectional potential gradient heuristics was presented in [20] to potentially guide two rapidly-exploring random trees towards each other in the bidirectional sampling-based motion planning, and hence the two algorithms called Potentially Guided Bidirectional RRT * (PB-RRT * ) and Potentially Guided Intelligent Bidirectional RRT * (PIB-RRT * ) greatly improved the sampling efficiency, resulting in faster convergence rate and excellent performance in highly cluttered environment. However, the biased sampling technique was not introduced in the PIB-RRT * and PB-RRT * algorithms [20].…”