Decoupling Constraints from Sampling-Based Planners

Kingston, Zachary; Moll, Mark; Kavraki, Lydia E.

doi:10.1007/978-3-030-28619-4_62

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…This paper extends the work presented in Kingston et al (2017). We present a more complete formulation of the algorithms used by the projection-and continuation-based space representations, proofs that the framework preserves probabilistic completeness and asymptotic optimality of sampling-based planners for both the projection-and continuation-based spaces, an open-source implementation of the framework, and additional empirical results including implementation of the framework for NASA's Robonaut 2.…”

Section: Contributionsmentioning

confidence: 69%

Exploring implicit spaces for constrained sampling-based planning

Kingston

Moll

Kavraki

2019

The International Journal of Robotics Research

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We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.

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Section: Contributionsmentioning

confidence: 69%

Exploring implicit spaces for constrained sampling-based planning

Kingston

Moll

Kavraki

2019

The International Journal of Robotics Research

Self Cite

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“…Researchers have explored sampling-based approaches to generate constrained point-to-point trajectories for high-DOF robotic systems (Kingston et al, 2017, 2018, 2019). Dalibard et al (2009) proposed an approach where task-constrained Jacobian-based local motions were generating to connect nodes in the RRT-Connect trees for generating whole-body motions in humanoids.…”

Section: Related Workmentioning

confidence: 99%

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

Kabir

Thakar

Malhan

et al. 2021

The International Journal of Robotics Research

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We present an approach to generate path-constrained synchronous motion for the coupled ensemble of robots. In this article, we refer to serial-link manipulators and mobile bases as robots. We assume that the relative motion constraints among the objects in the environment are given. We represent the motion constraints as path constraints and pose the problem of path-constrained synchronous trajectory generation as a non-linear optimization problem. Our approach generates configuration space trajectories for the robots to manipulate the objects such that the given motion constraints among the objects are satisfied. We present a method that formulates the problem as a discrete parameter optimization problem and solves it using successive constraint refinement techniques. The method adaptively selects the parametric representation of the configuration variables for a given scenario. It also generates an approximate solution as the starting point for the successive constraint refinement stages to reduce the computation time. We discuss in detail why successive constraint refinement strategies are useful for solving this class of problems. We demonstrate the effectiveness of the proposed method on challenging test cases in simulation and physical environments with high-degree-of-freedom robotic systems.

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“…Such chains arise frequently in today's robots and manipulation systems ( Fig. 1), which explains the growing interest they arouse in the literature [2,30,34,37,38,53,54].…”

Section: Introductionmentioning

confidence: 99%

“…New challenging test cases are also included for demonstration, including tasks that require the throwing of objects at a given velocity, and bimanual manipulations of heavy loads, which were difficult to solve with [14]. It is worth noting that, while some path planning approaches have previously dealt with closed kinematic chains [2,18,29,34,37,54,65,70], none of these approaches has considered the dynamics of the system into the planner. Our kinodynamic planner, in fact, can also be seen as an extension of the work in [34] to cope with dynamic constraints.…”

Section: Introductionmentioning

confidence: 99%

A Randomized Kinodynamic Planner for Closed-Chain Robotic Systems

2021

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Kinodynamic RRT planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, cooperating arms manipulating an object, or legged robots keeping their feet in contact with the environment. The state space of such systems is an implicitly-defined manifold, which complicates the design of the sampling and steering procedures, and leads to trajectories that drift away from the manifold when standard integration methods are used. To address these issues, this report presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to both generate random states, and to dynamically steer the system towards such states. The steering method is based on computing linear quadratic regulators from the atlas charts, which greatly increases the planner efficiency in comparison to the standard method that simulates random actions. The atlas also allows the integration of the equations of motion as a differential equation on the state space manifold, which eliminates any drift from such manifold and thus results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. We illustrate the performance of the approach in significantly complex tasks, including planar and spatial robots that have to lift or throw a load at a given velocity using torque-limited actuators.

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Decoupling Constraints from Sampling-Based Planners

Cited by 15 publications

References 35 publications

Exploring implicit spaces for constrained sampling-based planning

Exploring implicit spaces for constrained sampling-based planning

Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots

A Randomized Kinodynamic Planner for Closed-Chain Robotic Systems

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