RRT*-Connect: Faster, asymptotically optimal motion planning

Klemm, Sebastian; Oberländer, Jan; Hermann, A. M.; Roennau, Arne; Schamm, Thomas; Zöllner, J. Marius; Dillmann, Rüdiger

doi:10.1109/robio.2015.7419012

Cited by 137 publications

(61 citation statements)

References 18 publications

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“…Particularly, the recognition and selection of tip nodes seem to be interesting. Also, the bidirectional RRT* algorithms, such as [Jordan and Perez, 2013] and [Klemm et al, 2015], could lead to significant improvements in the matter of computational time.…”

Section: Resultsmentioning

confidence: 99%

Accelerated RRT* and Its Evaluation on Autonomous Parking

Vlasak

Sójka

Hanzálek

2019

Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems

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Finding a collision-free path for autonomous parking is usually performed by computing geometric equations, but the geometric approach may become unusable under challenging situations where space is highly constrained. We propose an algorithm based on Rapidly-Exploring Random Trees Star (RRT*), which works even in highly constrained environments and improvements to RRT*based algorithm that accelerate computational time and decrease the final path cost. Our improved RRT* algorithm found a path for parallel parking maneuver in 95 % of cases in less than 0.15 seconds.

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Section: Resultsmentioning

confidence: 99%

Accelerated RRT* and Its Evaluation on Autonomous Parking

Vlasak

Sójka

Hanzálek

2019

Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems

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“…In addition, there are algorithms that optimize paths based on the principle of triangular inequality, such as RRT*-Smart algorithm [ 16 ] and Quick-RRT* algorithm [ 17 ], to derive a path that is close to the optimal. Many algorithms [ 18 , 19 , 20 , 21 ] that extend the RRT algorithm have been studied.…”

Section: Introductionmentioning

confidence: 99%

Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Kang

Lim

Choi

et al. 2021

Sensors

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This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

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“…A second adaptation is the RRT* [11] where local optimization is performed to shorten the path length in joint space. These two changes have been combined in [13] to reduce calculation times and to find optimal paths at the same time. There also exist many other adjustments, such as [22], where an extension for real-time capability is presented, in which the path is reshaped during execution to generate an optimal path.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mobility of Robotic Arms in Collision-free Motion Planning

Kaden

Thomas

2021

J Intell Robot Syst

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A major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.

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RRT*-Connect: Faster, asymptotically optimal motion planning

Cited by 137 publications

References 18 publications

Accelerated RRT* and Its Evaluation on Autonomous Parking

Accelerated RRT* and Its Evaluation on Autonomous Parking

Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Optimizing Mobility of Robotic Arms in Collision-free Motion Planning

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