Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning

Plaku, Erion; Kavraki, Lydia E.; Vardi, Moshe Y.

doi:10.1109/tro.2010.2047820

Cited by 147 publications

(122 citation statements)

References 47 publications

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“…Another physics-based planning approach [10] integrated the samplingbased motion planing with the discrete search using the workspace decomposition in order to map the planning problem onto a graph searching problem. This work evaluated the performance (in term of planning time) using RRT, Synergistic Combination of Layers of Planning (SyCLoP [11]) and a modified version of the SyCLoP as kinodynamic motion planners. The propagation step was performed using the Bullet [12] physics engine.…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Motion Planning: Evaluation Criteria and Benchmarking

Gillani

Akbari

Rosell

2015

Advances in Intelligent Systems and Computing

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Abstract. Motion planning has evolved from coping with simply geometric problems to physics-based ones that incorporate the kinodynamic and the physical constraints imposed by the robot and the physical world. Therefore, the criteria for evaluating physics-based motion planners goes beyond the computational complexity (e.g. in terms of planning time) usually used as a measure for evaluating geometrical planners, in order to consider also the quality of the solution in terms of dynamical parameters. This study proposes an evaluation criteria and analyzes the performance of several kinodynamic planners, which are at the core of physics-based motion planning, using different scenarios with fixed and manipulatable objects. RRT, EST, KPIECE and SyCLoP are used for the benchmarking. The results show that KPIECE computes the time-optimal solution with heighest success rate, whereas, SyCLoP compute the most power-optimal solution among the planners used.

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Section: Introductionmentioning

confidence: 99%

Physics-Based Motion Planning: Evaluation Criteria and Benchmarking

Gillani

Akbari

Rosell

2015

Advances in Intelligent Systems and Computing

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“…In order to get useful tactile force readings, we draw from recent advances in sampling-based motion planning [5]- [13] to design effective exploration strategies of the surface of an unknown object that ensure good local and global coverage. The exploration leverages the idea of selectively sampling the robot workspace to guide the exploration towards unexplored parts of the workspace and locally explore areas where it collects measurements.…”

Section: Introductionmentioning

confidence: 99%

Tactile-Object Recognition From Appearance Information

et al. 2011

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Abstract-This paper explores the connection between sensorbased perception and exploration in the context of haptic object identification. The proposed approach combines (i) object recognition from tactile appearance with (ii) purposeful haptic exploration of unknown objects to extract appearance information. The recognition component brings to bear computer vision techniques by viewing tactile sensor readings as images. We present a bag-of-features framework that uses several tactile image descriptors, some adapted from the vision domain, others novel, to estimate a probability distribution over object identity as an unknown object is explored. Haptic exploration is treated as a search problem in a continuous space to take advantage of sampling-based motion planning to explore the unknown object and construct its tactile appearance.Simulation experiments of a robot arm equipped with a haptic sensor at the end-effector provide promising validation, indicating high accuracy in identifying complex shapes from tactile information gathered during exploration. The proposed approach is also validated by using readings from actual tactile sensors to recognize real objects.

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“…In this paper, two search tree was built, and we connected these two at a probability P, and we use the coverage [9] to control the change of probability p. This paper is organized as follows. In Sect.…”

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confidence: 99%

Bidirectional Variable Probability RRT Algorithm for Robotic Path Planning

Kong

Xiong

Han

2012

Electrical, Information Engineering and Mechatronics 2011

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The traditional goal-bias RRT is mentioned to improve the efficiency, but it has an inherent problem, when there are lesser vertexes, the search toward the goal is often invalid; however, when there are more vertexes, the search toward other regions is often unnecessary. To solve this problem, we introduce a kind bidirectional variable probability RRT algorithm. In this paper, we build two trees, and one tree expands toward to the other tree at a variable probability. This probability is in proportion to the coverage of the trees, that is, when there are lesser vertexes, the searches are mainly toward unexplored regions, and when there are more vertexes, we attach more importance to the connection of two trees. The results show the good performance and convergence speed of the proposed algorithm.Keywords Motion planning Á Nonholonomic constraint Á RRT Á Goal bias Á Dynamics models IntroductionRapidly exploring random tree (RRT) has attracted a lot of recent attention. This algorithm can work effectively and does not need to preprocess the map, especially when we take dynamics into account [1].

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Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning

Cited by 147 publications

References 47 publications

Physics-Based Motion Planning: Evaluation Criteria and Benchmarking

Physics-Based Motion Planning: Evaluation Criteria and Benchmarking

Tactile-Object Recognition From Appearance Information

Bidirectional Variable Probability RRT Algorithm for Robotic Path Planning

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