Tangent space RRT: A randomized planning algorithm on constraint manifolds

Suh, Chansu; Um, Terry Taewoong; Kim, Beobkyoon; Noh, Hakjong; Kim, Munsang; Park, Frank C.

doi:10.1109/icra.2011.5980566

Cited by 30 publications

(20 citation statements)

References 12 publications

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“…The process of interpolation and projection is repeated to grow branches on the configuration manifold. Note, however, that this process tends to produce short branch extensions when the interpolation direction approaches the orthogonal to the manifold [35]. For a fair comparison, both the HC-planner and the CB-RRT are applied on the same formulation used by AtlasRRT.…”

Section: Methodsmentioning

confidence: 99%

“…These approaches are probabilistically complete [34] and easy to implement, but a uniform distribution of samples in the ambient space does not necessarily translate to a uniform distribution in the configuration space [24], and the branch extensions are many times prematurely blocked, as noted in [35], which reduces their efficiency. This problem is illustrated in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…With this method, though, points are still sampled in the ambient space, which can be of much higher dimensionality than the configuration space. Suh et al [35] introduce a lazy RRT scheme where loosely coordinated RRTs are built on tangent spaces that locally approximate the manifold and that have the same dimensionality as the configuration space. However, the quality of the resulting RRT is affected by the possible overlap of tree branches that belong to different tangent spaces.…”

Section: Related Workmentioning

confidence: 99%

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Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

2013

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Abstract-The situation arising in path planning under kinematic constraints, where the valid configurations define a manifold embedded in the joint ambient space, can be seen as a limit case of the well-known narrow corridor problem. With kinematic constraints the probability of obtaining a valid configuration by sampling in the joint ambient space is not low but null, which complicates the direct application of sampling-based path planners. This paper presents the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize a manifold and to coordinate the charts forming an atlas that fully covers it. AtlasRRT simultaneously builds an atlas and a bi-directional Rapidly-Exploring Random Tree (RRT), using the atlas to sample configurations and to grow the branches of the RRTs, and the RRTs to devise directions of expansion for the atlas. The efficiency of AtlasRRT is evaluated in several benchmarks involving high-dimensional manifolds embedded in large ambient spaces. The results show that the combined use of the atlas and the RRTs produces a more rapid exploration of the configuration space manifolds than existing approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

2013

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“…Many practical tasks impose constraints on robot motions, such as machining, welding, and cutting and so on. RRT (rapidly-exploring random tree)/PRM (probabilistic roadmap)-based approaches were used to plan the path with end-effector constraints [5][6][7][8][9]. The Cartesian positions of the end-effector at the via-points were achieved, but the motions of the end-effector between via points are not predictable, in view of the nonlinear effects introduced by the direct kinematics.…”

Section: Introductionmentioning

confidence: 99%

Predictable Trajectory Planning of Industrial Robots with Constraints

Chen

2018

Applied Sciences

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Trajectory prediction is currently attracting considerable attention. This paper proposes geodesic trajectory planning with end-effector and joint constraints to predict the trajectory properties of the end-effector, such as velocities, accelerations, and smoothness. The prediction of the trajectory properties is independent of the joint trajectories. The prediction makes it possible to adjust the trajectory properties in line with a light computational burden. To demonstrate the effectiveness of the proposed method, experiments were conducted using the Efort robot. The experiments show that the proposed method can predict the properties of the trajectory and modify the trajectory to meet the constraints.

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“…[15,21,22]. Whereas the idea of constructing random trees on the tangent bundle is first presented in these papers, the current paper goes further in a number of substantive ways: we derive explicit curvature formulas that are used to bound each tangent space according to the local curvature of the constraint manifold; we develop a more systematic set of rules for, e.g., generating new nodes and lazy projection; we perform a more extensive set of numerical experiments for more complex planning environments, comparing our results with several alternative constrained planning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Tangent bundle RRT: A randomized algorithm for constrained motion planning

Kim

Suh

et al. 2014

Robotica

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SUMMARYThe Tangent Bundle Rapidly Exploring Random Tree (TB-RRT) is an algorithm for planning robot motions on curved configuration space manifolds, in which the key idea is to construct random trees not on the manifold itself, but on tangent bundle approximations to the manifold. Curvature-based methods are developed for constructing tangent bundle approximations, and procedures for random node generation and bidirectional tree extension are developed that significantly reduce the number of projections to the manifold. Extensive numerical experiments for a wide range of planning problems demonstrate the computational advantages of the TB-RRT algorithm over existing constrained path planning algorithms.

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Tangent space RRT: A randomized planning algorithm on constraint manifolds

Cited by 30 publications

References 12 publications

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds

Predictable Trajectory Planning of Industrial Robots with Constraints

Tangent bundle RRT: A randomized algorithm for constrained motion planning

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