RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems

Pareekutty, Nahas; James, Francis G.; Ravindran, Balaraman; Shah, Suril V.

doi:10.1115/detc2016-60547

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Yet, their approach cannot replace the actual heuristic function. Pareekutty et al [12] use value iteration to iteratively create a quality grid map during planning, which guides the node expansion of a RRT planner. However, their approach uses a discretized state space and does not allow pretraining of the heuristic.…”

Section: Related Workmentioning

confidence: 99%

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Bernhard¹,

Gieselmann²,

Esterle³

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search-or optimization-based motion planners. With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higherdimensional problems. However, these methods guarantee optimality of the resulting policy only in a statistical sense, which impedes their usage in safety critical systems, such as autonomous vehicles. Thus, we propose the Experience-Based-Heuristic-Search algorithm, which overcomes the statistical failure rate of a Deep-reinforcement-learning-based planner and still benefits computationally from the pre-learned optimal policy. Specifically, we show how experiences in the form of a Deep Q-Network can be integrated as heuristic into a heuristic search algorithm. We benchmark our algorithm in the field of path planning in semi-structured valet parking scenarios. There, we analyze the accuracy of such estimates and demonstrate the computational advantages and robustness of our method. Our method may encourage further investigation of the applicability of reinforcement-learning-based planning in the field of self-driving vehicles.

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Section: Related Workmentioning

confidence: 99%

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Bernhard¹,

Gieselmann²,

Esterle³

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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“…The shortcomings of RRT is high path cost. To address the drawback of RRT, some works were proposed in Refs [8–11]. Lindqvist et al .…”

Section: Introductionmentioning

confidence: 99%

“…Pareekutty et al . put forward a method that named as qRRT, which is an optimal motion planning algorithm for nonholonomic systems [9]. To deduce the cost of generated path, it uses the principle of learning through experience.…”

Section: Introductionmentioning

confidence: 99%

Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space

Wang,

Ju,

et al. 2023

IEEJ Transactions Elec Engng

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Path planning is one of the fundamental issues in the research field of robot. In the last decades, Rapidly‐exploring Random Tree star (RRT*) was one of the chosen path planner for robot with its probabilistically complete. In this paper, an improved bidirectional RRT* path planner for robot is proposed (Bi‐RRT*), which has planned path with safety and smoothly guarantee. The main contributions of this paper include, firstly, combining the backtracking idea of the Quick‐RRT algorithm with the greedy search strategy of the RRT‐Connect algorithm, which can reduce the path cost and improve the operation efficiency of the algorithm. Second, in order to further reduce the running time of the algorithm, a new collision detection algorithm based on cross‐product is proposed to replace the conventional collision detection strategy. To ensure the quality of the path, two post‐processing strategies, path optimization strategy and path smoothing strategy, are proposed. Path optimization strategy is based on triangle inequality, and two optimization methods are proposed to effectively reduce the path cost. The path smoothing strategy based on the Bezier curve improves the continuity of a smooth arc and is better applied to path smoothing. Simulation results for both virtual and real environments show the advantages of combining Quick‐RRT and RRT‐Connect and verify the effectiveness of the proposed collision detection algorithm. Compared with similar algorithms, the proposed Bi‐RRT algorithm has higher operational efficiency, smaller path cost, smoother planned paths, and improved path quality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Graph Based RRT Optimization for Autonomous Mobile Robots

Aguilar

Sandoval

Caballeros

et al. 2018

Lecture Notes in Computer Science

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RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems

Cited by 5 publications

References 11 publications

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space

Graph Based RRT Optimization for Autonomous Mobile Robots

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RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems

Cited by 5 publications

References 11 publications

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Bi‐RRT*: An Improved Bidirectional RRT* Path Planner for Robot in Two‐Dimensional Space

Graph Based RRT Optimization for Autonomous Mobile Robots

Contact Info

Product

Resources

About

Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space