Physically Plausible Wrench Decomposition for Multieffector Object Manipulation

Abstract

When manipulating an object with multiple effectors such as in multi-digit grasping or multi-agent collaboration, forces and torques (i.e. wrench) applied to the object at different contact points generally do not fully contribute to the resultant object wrench, but partly compensate each other. The current literature, however, lacks a physically plausible decomposition of the applied wrench into its manipulation and internal components. We formulate the wrench decomposition as a convex optimization problem, minimizing the Euclidean norms of manipulation forces and torques. Physical plausibility in the optimization solution is ensured by constraining the internal and manipulation wrench by the applied wrench. We analyze specific cases of 3digit grasping and 2D beam manipulation, and show the applicability of our method to general object manipulation with multiple effectors. The wrench decomposition method is then extended to quantification of measures important in evaluating physical human-human and human-robot interaction tasks. We validate our approach via comparison to the state of the art in simulation and via application to a human-human object transport study.

Effect of External Force on Agency in Physical Human-Machine Interaction

“…Furthermore, in order to evaluate how the different guiding force types influenced the reaching performance, we analyzed the trial duration, peak interaction force and the manipulation share. As defined in Donner et al (2018), the interaction force is calculated as…”

“…Namely, the interaction force represents the force that is compensated by the two agents and has no contribution to the motion of the handle. Thus, excessively high interaction force suggests inefficiency in coordination between the agents, although the interaction force may become a source of communication between agents (Donner et al, 2018). The largest interaction force during a trial was then sampled for statistical analysis as an index of (in)efficiency in interaction.…”

Fully Distributed Cooperation for Networked Uncertain Mobile Manipulators

“…case 1-1: β 1 = β 4 = 0I 3 , β 2 = 0.8I 3 , β 3 = 0.2I 3 and case 1-2: β 1 = 0.35I 3 , β 2 = 0.3I 3 , β 3 = 0.2I 3 , β 4 = 0.15I 3 . Here β r,i is computed based on the physically plausible wrench decomposition method proposed in [34]. After the interconnected system achieve controlled dynamic balance, approximately after t = 1 s, the actual load sharing parameters β r,i match the initially designated desired load distribution parameters β i well.…”

Predefined‐time control of cooperative manipulators

“…Cooperative robots have attracted the attention of the research community due to these systems outperform single robots carrying out some generic tasks. [1][2][3][4][5][6] For that reason, different schemes have been proposed to robustly control cooperative robots manipulating rigid objects. [7][8][9][10] Nonetheless, cooperative motion control is a challenging problem in virtue of the highly coupled and nonlinear cooperative robot model, requiring a dynamic controller to enforce stability, while satisfying the algebraic constraints that arise during the cooperative motion, as stated by Parra-Vega et al 4 It is worth noticing that the problem is exacerbated in the presence of any uncertainty, requiring a robust controller to face disrupting effects.…”

“…Cooperative robots have attracted the attention of the research community due to these systems outperform single robots carrying out some generic tasks 1‐6 . For that reason, different schemes have been proposed to robustly control cooperative robots manipulating rigid objects 7‐10 .…”