Improved Discrete RRT for Coordinated Multi-robot Planning

Hvězda, Jakub; Kulich, Miroslav; Přeučil, Libor

doi:10.5220/0006865901810189

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Graphbased or discrete sampling-based algorithms also exist, where instead of sampling from the free space, nodes of the graph are sampled for planning. Examples of such algorithms include [59,64,65], d-RRT [66], and d-RRT* [67]; however, as Branicky et. al state [64], "the performance of the RRT in discrete space is degraded by a decreased bias toward unexplored areas."…”

Section: Supported Path Planning Algorithmsmentioning

confidence: 99%

Systematic Comparison of Path Planning Algorithms using PathBench

Hsueh¹,

Toma²,

Jaafar³

et al. 2022

Preprint

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Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly-exploring random tree (RRT) are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. An unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported; e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real world robot to demonstrate PathBench's support for Robot Operating System (ROS). PathBench is open source 1 .

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Section: Supported Path Planning Algorithmsmentioning

confidence: 99%

Systematic Comparison of Path Planning Algorithms using PathBench

Hsueh¹,

Toma²,

Jaafar³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Fig. 2: Example problem of CARP algorithm [13] Algorithm 1 Improved MRdRRT algorithm [13] 1: T .init (s)…”

Section: Proposed Use Of Carp Algorithm As Local Connectormentioning

confidence: 99%

“…The paper is an extended version of the paper [13] presented at the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO). In comparison to the ICINCO paper, the following modifications were made:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Randomized Searching for Multi-robot Coordination

Hvězda

Kulich

Přeučil

2019

Informatics in Control, Automation and Robotics

Self Cite

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In this text, we propose a novel approach for solving the coordination of a fleet of mobile robots, which consists of finding a set of collision-free trajectories for individual robots in the fleet. This problem is studied for several decades, and many approaches have been introduced. However, only a small minority is applicable in practice because of their properties-small computational requirement, producing solutions near-optimum, and completeness. The approach we present is based on a multi-robot variant of Rapidly Exploring Random Tree algorithm (RRT) for discrete environments and significantly improves its performance. Although the solutions generated by the approach are slightly worse than one of the best state-of-the-art algorithms presented in [23], it solves problems where ter Mors's algorithm fails.

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The boundary node method for multi‐robot multi‐goal path planning problems

2021

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In this paper, we investigate the multi-goal path planning problem to find the shortest collision-free path connecting a given set of goal points located in the robot working environment. This problem combines two sub-problems: first, optimize the sequence of the goal points located in the free workspace; second, compute the shortest collision-free path between goal points. In this study, a genetic algorithm is used to optimize the sequence of the goal points that the robot should visit. Once the sequence of the goal points is available, our developed method called Boundary Node Method (BNM) is applied to generate an initial collision-free path between every pair of the sequenced goal points. Subsequently, an additional developed method called Path Enhancement Method (PEM) is used to find an optimal or nearoptimal path by reducing the overall initial path length. In the BNM, the robot and its immediate surroundings are simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves between goals with boundary nodes in the working environment depending on the boundary node's potential value. The potential value of each point in the working environment is calculated based on the proposed potential function. Additionally, this article investigates the multi-goal path planning problem for multi-robot systems, when each goal reached by several robots. Finally, simulations and experiments are performed on a real mobile robot to demonstrate the effectiveness of the developed methods.

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Improved Discrete RRT for Coordinated Multi-robot Planning

Cited by 4 publications

References 16 publications

Systematic Comparison of Path Planning Algorithms using PathBench

Systematic Comparison of Path Planning Algorithms using PathBench

On Randomized Searching for Multi-robot Coordination

The boundary node method for multi‐robot multi‐goal path planning problems

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