Task and Motion Planning

Dantam, Neil T.

doi:10.1007/978-3-642-41610-1_176-1

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…Hauser (2013, 2014) addresses a different aspect of infeasibility by identifying minimal object sets or displacements that ensure motion plan existence. Task and motion planning must also address motion feasibility, such as through feasibility checks (Cambon et al, 2009; Dantam et al, 2018; Dantam 2020b; Erdem et al, 2016; Garrett et al, 2021; Kaelbling and Lozano-Pérez 2013; Lagriffoul and Andres 2016; Srivastava et al, 2014; Thomason and Knepper 2019; Toussaint 2015; Vega-Brown and Roy 2020). Varying approaches to handling feasibility significantly impact the guarantees and performance of such algorithms.…”

Section: Related Workmentioning

confidence: 99%

A sampling and learning framework to prove motion planning infeasibility

Dantam

2023

The International Journal of Robotics Research

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We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces.

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Section: Related Workmentioning

confidence: 99%

A sampling and learning framework to prove motion planning infeasibility

Dantam

2023

The International Journal of Robotics Research

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Review of robot-assisted medical ultrasound imaging systems: Technology and clinical applications

Huang,

Zhou,

2023

Neurocomputing

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Multiple Mobile Robot Task and Motion Planning: A Survey

et al. 2023

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With recent advances in mobile robotics, autonomous systems, and artificial intelligence, there is a growing expectation that robots are able to solve complex problems. Many of these problems require multiple robots working cooperatively in a multi-robot system. Complex tasks may also include the interconnection of task-level specifications with robot motion-level constraints. Many recent works in the literature use multiple mobile robots to solve these complex tasks by integrating task and motion planning. We survey recent contributions to the field of combined task and motion planning for multiple mobile robots by categorizing works based on their underlying problem representations, and identify possible directions for future research. We propose a taxonomy for task and motion planning based on system capabilities, applicable to multi-robot and single-robot systems.

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Task and Motion Planning

Cited by 4 publications

References 38 publications

A sampling and learning framework to prove motion planning infeasibility

A sampling and learning framework to prove motion planning infeasibility

Review of robot-assisted medical ultrasound imaging systems: Technology and clinical applications

Multiple Mobile Robot Task and Motion Planning: A Survey

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