Dynamic non-prehensile object transportation

Lertkultanon, Puttichai; Pham, Quang-Cuong

doi:10.1109/icarcv.2014.7064519

Cited by 20 publications

(16 citation statements)

References 13 publications

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“…Bidirectional RRT Kuffner and LaValle (2000) remarked that growing two trees simultaneously, one rooted at the initial configuration and one rooted at the goal configuration, yielded significant improvement over the classical unidirectional RRT. This idea (see also Nakamura and Mukherjee, 1991) can be easily implemented in the context of AVP-RRT as follows (Lertkultanon and Pham, 2014).…”

Section: Kinodynamic Trajectory Planning Using Avpmentioning

confidence: 99%

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Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

Pham¹,

Caron

Lertkultanon³

et al. 2016

The International Journal of Robotics Research

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Path-velocity decomposition is an intuitive yet powerful approach to address the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate and simpler steps : first, find a path in the configuration space that satisfies the geometric constraints (path planning), and second, find a time-parameterization of that path satisfying the kinodynamic constraints. A fundamental requirement is that the path found in the first step should be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints in the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm -Admissible Velocity Propagation -which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail 1 .

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Section: Kinodynamic Trajectory Planning Using Avpmentioning

confidence: 99%

“…We first reduced the three aforementioned conditions to the form of inequality (1). Details of this reduction can be found in Lertkultanon and Pham (2014). We next used the bidirectional version of AVP-RRT presented in Section 3.2.…”

Section: Examples Of Applicationsmentioning

confidence: 99%

Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

Pham¹,

Caron

Lertkultanon³

et al. 2016

The International Journal of Robotics Research

Self Cite

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“…But due to the size of the optimization problem, these techniques are often extremely slow, taking minutes or hours to complete. Some numerical approaches have been presented for dexterous manipulation [17,20] as well as nonprehensile grasp [16,19,22], and they are computationally challenging to be extended to high dimensional problems.…”

Section: Related Workmentioning

confidence: 99%

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Luo

Hauser²

2015

Robotics: Science and Systems XI

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Abstract-Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible trajectory along a fixed path under frictional contact, where friction is uncertain and actuator effort is noisy. It introduces a robust time-scaling method that is able to accept confidence intervals on uncertain parameters, and uses a convex parameterization that allows dynamically-feasible motions under contact to be computed in seconds. This is combined with an iterative learning method that uses feedback from execution to learn confidence bounds on modeling parameters. Experiments on a manipulator performing a "waiter" task, on which an object is moved on a carried tray as quickly as possible, demonstrate this method can compensate for modeling uncertainties within a handful of iterations.

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“…Bobrow et al [16], McCarthy and Bobrow [84] planned motions for robots with limited joint torque transporting heavy payloads by formulating and solving TOPP. In more recent works, researchers proposed different TOPP formulations to solve the "waiter motion" problem [70,43,78,31]. This problem consists in a robot, equipped with a flat plate as its end-effector, transports an object using only frictional and gravitational forces.…”

Section: Time-optimal Path Parameterization In Robotic Applicationsmentioning

confidence: 99%

“…This problem consists in a robot, equipped with a flat plate as its end-effector, transports an object using only frictional and gravitational forces. Lertkultanon and Pham [70] included the Zero Moment Point [128] constraint in their TOPP formulation to ensure that the object does not fall. Additionally, Csorvási et al [31], Debrouwere et al [34] proposed to include both dry and viscous friction constraints to prevent slipping.…”

Section: Time-optimal Path Parameterization In Robotic Applicationsmentioning

confidence: 99%

Time-optimal motion planning and control

Pham¹

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Bobrow et al. [16] proved that the optimal time-parameterization consists of alternatively maximally accelerating and decelerating segments and suggested the first NI-based algorithm to solve TOPP. Around the same period, Pfeiffer and Johanni [94], Shin and McKay [113] developed other NI-based algorithms that follow the same basic principle. All of these initial NI-based algorithms involve expensive computations for finding switching points-which are points at which the velocity profile changes from maximally decelerating to maximally accelerating [16]. A development was made by Slotine and Yang [119], who classified the switch points into tangent, singular and discontinuous switch points and introduced procedures for finding them. This development leads to significant improvement in computational cost. Later on, Zlajpah [131] introduced trap regions to handle joint velocity constraints in NI-based algorithms.These initial NI-based algorithms, however, were prone to failure in the presence of singular switch points, also known as zero-inertia points [119]. Only until recently, Kunz and Stilman [66] developed an NI-based algorithm that can handle singular switch points for robots subject to joint velocity and acceleration constraints in a realiable manner. Subsequently Pham [99] proposed a more general approach that can account for singular switch points resulting from "Second-Order Constraints", which include joint torque bound, Zero Moment Point constraint, frictional constraint in addition to joint acceleration bounds.

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Dynamic non-prehensile object transportation

Cited by 20 publications

References 13 publications

Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

Robust Trajectory Optimization Under Frictional Contact with Iterative Learning

Time-optimal motion planning and control

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