On-line joint limit avoidance for torque controlled robots by joint space parametrization

Charbonneau, Marie; Nori, Francesco; Pucci, Daniele

doi:10.1109/humanoids.2016.7803379

Cited by 10 publications

(9 citation statements)

References 34 publications

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“…In [18], joints position bounds are treated outside the QP control constraints-set through an explicit deceleration to push back a joint if it gets closer to its bounds. In [19], a parametrization of the feasible joint-space prevents the joints trajectories from reaching the bound limits.…”

Section: Introductionmentioning

confidence: 99%

Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

Djeha

Tanguy

Kheddar

2020

IEEE Robot. Autom. Lett.

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In this letter, we revisit an open problem of constraints formulation in the context of task-space control frameworks formulated as quadratic programs. In most inverse dynamics implementations, the decision variables are: robot joints acceleration, interaction forces (mostly physical contacts), and robot torques. Nevertheless, many constraints, like distance and velocity bounds, are not written originally in terms of one of these decision variables. Previous work proposed solutions to formulate and enforce joint limits constraints. Yet, none of them worked properly in closed-loop, specifically when bounds are reached or when they are time-varying. First, we show that constraints like collision avoidance, bounds of center of mass, constraints on field-of-view, Cartesian and velocity bounds on a given link... are written in a generic class. Then, we formulate such a class of constraints with gain-parameterized ordinary differential inequality. An adaptive-gain method enforces systematically such class of constraints, and results on a stable behavior when their bounds (even when they vary with time) are reached in closedloop. Experimental results performed on a humanoid robot validate our solution on a large panel of constraints.

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Section: Introductionmentioning

confidence: 99%

Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

Djeha

Tanguy

Kheddar

2020

IEEE Robot. Autom. Lett.

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“…To generate a feasible output, the output of the bounded output programmable oscillator is defined as [19] y = y avg + δy tanh (s 1 ),…”

Section: Bounded Output Programmable Oscillatormentioning

confidence: 99%

An Integrated Programmable CPG with Bounded Output

Pasandi,

Sadeghian,

Keshmiri

et al. 2022

Preprint

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Cyclic motions are fundamental patterns in robotic applications including industrial manipulation and legged robot locomotion. This paper proposes an approach for the online modulation of cyclic motions in robotic applications. For this purpose, we present an integrated programmable Central Pattern Generator (CPG) for the online generation of the reference joint trajectory of a robotic system out of a library of desired periodic motions. The reference trajectory is then followed by the lowerlevel controller of the robot. The proposed CPG generates a smooth reference joint trajectory convergence to the desired one while preserving the position and velocity joint limits of the robot. The integrated programmable CPG consists of one novel bounded output programmable oscillator. We design the programmable oscillator for encoding the desired multidimensional periodic trajectory as a stable limit cycle. We also use the state transformation method to ensure that the oscillator's output and its first time derivative preserve the joint position and velocity limits of the robot. With the help of Lyapunov based arguments, We prove that the proposed CPG provides the global stability and convergence of the desired trajectory. The effectiveness of the proposed integrated CPG for trajectory generation is shown in a passive rehabilitation scenario on the Kuka iiwa robot arm, and also in walking simulation on a seven-link bipedal robot.

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“…Another extension of the DMP framework incorporates joint limits [10]. This extension is based on transforming the DMP into a space in which the joint limits correspond to ±∞ [33]. In Section II-D1 we discussed how ProMPs could use the same technique for joint limit avoidance.…”

Section: Mutual Avoidance With Unbound Waypointsmentioning

confidence: 99%

Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation

Frank,

Paraschos,

van der Smagt

et al. 2021

Preprint

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Versatile movement representations allow robots to learn new tasks and rapidly adapt them to environmental changes, e.g. introduction of obstacles, placing additional robots in the workspace, modification of the joint range due to faults or range of motion constraints due to tool manipulation. Probabilistic movement primitives (ProMP) model robot movements as a distribution over trajectories and they are an important tool due to their analytical tractability and ability to learn and generalise from a small number of demonstrations. Current approaches solve specific adaptation problems, e.g. obstacle avoidance, however, a generic probabilistic approach to adaptation has not yet been developed. In this paper we propose a generic probabilistic framework for adapting ProMPs. We formulate adaptation as a constrained optimisation problem where we minimise the Kullback-Leibler divergence between the adapted distribution and the distribution of the original primitive and we constrain the probability mass associated with undesired trajectories to be low. We derive several types of constraints that can be added depending on the task, such us joint limiting, various types of obstacle avoidance, via-points, and mutual avoidance, under a common framework. We demonstrate our approach on several adaptation problems on simulated planar robot arms and 7-DOF Franka-Emika robots in single and dual robot arm settings.

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On-line joint limit avoidance for torque controlled robots by joint space parametrization

Cited by 10 publications

References 34 publications

Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

An Integrated Programmable CPG with Bounded Output

Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation

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