Extension of the Rapidly Exploring Random Tree Algorithm with Key Configurations for Nonholonomic Motion Planning

Szadeczky-Kardoss, Emese Gincsaine; Kiss, Bálint

doi:10.1109/icmech.2006.252554

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…Another extension of RRT is presented in [13], where the authors suggest the usage of so-called key configurations for tackling problems such as moving through narrow passages with non-holonomic vehicles.…”

Section: Related Work a Rapidly-exploring Random Treesmentioning

confidence: 99%

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Bojadžić¹,

Kunze²,

Osmanković³

et al. 2021

Preprint

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This paper presents a novel hierarchical motion planning approach based on Rapidly-Exploring Random Trees (RRT) for global planning and Model Predictive Control (MPC) for local planning. The approach targets a three-wheeled cycle rickshaw (trishaw) used for autonomous urban transportation in shared spaces. Due to the nature of the vehicle, the algorithms had to be adapted in order to adhere to non-holonomic kinematic constraints using the Kinematic Single-Track Model.The vehicle is designed to offer transportation for people and goods in shared environments such as roads, sidewalks, bicycle lanes but also open spaces that are often occupied by other traffic participants. Therefore, the algorithm presented in this paper needs to anticipate and avoid dynamic obstacles, such as pedestrians or bicycles, but also be fast enough in order to work in real-time so that it can adapt to changes in the environment. Our approach uses an RRT variant for global planning that has been modified for single-track kinematics and improved by exploiting dead-end nodes. This allows us to compute global paths in unstructured environments very fast. In a second step, our MPC-based local planner makes use of the global path to compute the vehicle's trajectory while incorporating dynamic obstacles such as pedestrians and other road users.Our approach has shown to work both in simulation as well as first real-life tests and can be easily extended for more sophisticated behaviors.

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Section: Related Work a Rapidly-exploring Random Treesmentioning

confidence: 99%

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Bojadžić¹,

Kunze²,

Osmanković³

et al. 2021

Preprint

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“…Often the methods used for mobile (wheeled) robots path planning are transferred to the legged robots. The example are the methods presented in [12] and [2], [17]. In [12] the RRT method was applied for 6-legged robot.…”

Section: Related Workmentioning

confidence: 99%

“…The example are the methods presented in [12] and [2], [17]. In [12] the RRT method was applied for 6-legged robot. Described in these publications algorithms are providing interesting improvements however they have the drawbacks as well.…”

Section: Related Workmentioning

confidence: 99%

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Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Szumowski¹,

Zielińska²

2021

JAMRIS

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Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.

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“…As proposed in ref. [25], increasing the sampling probability around key configurations close to narrow passages can help to find a solution. But it is not trivial to find such key configuration, since narrow passages are located in the configuration space and usually hard to estimate from the workspace.…”

Section: Introductionmentioning

confidence: 99%

ADD-RRV for motion planning in complex environments

2021

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In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

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Extension of the Rapidly Exploring Random Tree Algorithm with Key Configurations for Nonholonomic Motion Planning

Cited by 13 publications

References 6 publications

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

ADD-RRV for motion planning in complex environments

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