Lazy Toggle PRM: A single-query approach to motion planning

Denny, Jory; Shi, Kensen; Amato, Nancy M.

doi:10.1109/icra.2013.6630904

Cited by 24 publications

(14 citation statements)

References 19 publications

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“…Denny et al proposed a 'lazy planning' to improve the collision checking by the assumption that only 10% of the collisions checks are positive. 12 Conversely, algorithms like RRT connect 13 and the one proposed by Urmson and Simmons 14 increases the algorithm performance by heuristically biasing the tree growth. This tree growth has also been adapted by Denny et al, 15 who adapted Figure 1.…”

Section: Related Workmentioning

confidence: 99%

Planning with ants

Viseras

Losada

Merino

2016

International Journal of Advanced Robotic Systems

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Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm's completeness but does not necessarily lead to the most efficient solution. In previous works it has been shown that the use of heuristics to modify the sampling strategy could incur an improvement in the algorithm performance. However, these heuristics only apply to solve the shortest path-planning problem. Here we propose a framework that allows us to incorporate arbitrary heuristics to modify the sampling strategy according to the user requirements. This framework is based on 'learning from experience'. Specifically, we introduce a utility function that takes the contribution of the samples to the tree construction into account; sampling at locations of increased utility then becomes more frequent. The idea is realized by introducing an ant colony optimization concept in the RRT/RRT* algorithm and defining a novel utility function that permits trading off exploitation versus exploration of the state space. We also extend the algorithm to allow an anytime implementation. The scheme is validated with three scenarios: one populated with multiple rectangular obstacles, one consisting of a single narrow passage and a maze-like environment. We evaluate its performance in terms of the cost and time to find the first path, and in terms of the evolution of the path quality with the number of iterations. It is shown that the proposed algorithm greatly outperforms state-of-the-art RRT and RRT* algorithms.

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Section: Related Workmentioning

confidence: 99%

Planning with ants

Viseras

Losada

Merino

2016

International Journal of Advanced Robotic Systems

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“…The path planning problem of an autonomous robotic system and its requirements have attracted researchers to this research area in the recent years. [1][2][3][4][5][6][7][8][9][10] An automated robot has the ability to acclimate to the surrounding environment and interact with it without direct human intervention. In order to build an effective autonomous system, fast and robust techniques are required to deal with the real-time nature of such applications.…”

Section: Introductionmentioning

confidence: 99%

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Fareh¹,

Baziyad²,

Rahman³

et al. 2019

Robotica

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SummaryThis paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

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“…So-called “lazy planning” approaches such as Bohlin and Kavraki (2000) or Denny et al (2013), are alternative approaches to limit the number of edge checks that also produce partially incorrect or incomplete roadmaps. However the graphs they produce are typically neither sparse nor a proper subset of G full and are typically only suited to single query planning.…”

Section: Introductionmentioning

confidence: 99%

Matrix completion as a post-processing technique for probabilistic roadmaps

Esposito

Wright

2019

The International Journal of Robotics Research

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Inspired by the recent literature on matrix completion, this paper describes a novel post-processing algorithm for probabilistic roadmaps (PRMs). We argue that the adjacency matrix associated with real roadmaps can be decomposed into the sum of low-rank and sparse matrices. Given a PRM with n vertices and only [Formula: see text] collision-checked candidate edges, our algorithm numerically computes a relaxation of this decomposition, which estimates the status of all [Formula: see text] possible edges in the full roadmap with high accuracy, without performing any additional collision checks. Typical results from our experiments on problems from the Open Motion Planning Library indicate that after checking [Formula: see text] of the possible edges, the algorithm estimates the full visibility graph with [Formula: see text] accuracy. The practical utility of the algorithm is that the average path length across the resulting denser edge set is significantly shorter (at the cost of somewhat increased spatial complexity and query times). An ancillary benefit is that the resulting low-rank plus sparse decomposition readily reveals information that would be otherwise difficult to compute, such as the number of convex cells in free configuration space and the number of vertices in each. We believe that this novel connection between motion planning and matrix completion provides a new perspective on sampling-based planning and may guide future algorithm development.

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Lazy Toggle PRM: A single-query approach to motion planning

Cited by 24 publications

References 19 publications

Planning with ants

Planning with ants

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Matrix completion as a post-processing technique for probabilistic roadmaps

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