Cooperative Dynamic Manipulation of Unknown Flexible Objects
Cooperative Dynamic Manipulation of Unknown Flexible Objects
Joint Energy Injection Based on Simple Pendulum Fundamental Dynamics 0 1 2
Philine Donner 0 1 2
Franz Christange 0 1 2
Jing Lu 0 1 2
Martin Buss 0 1 2
0 Department of Electrical and Computer Engineering, Chair of Renewable and Sustainable Energy Systems (ENS), Technical University of Munich , Munich , Germany
1 Institute for Advanced Study, Technical University of Munich , Munich , Germany
2 Department of Electrical and Computer Engineering, Chair of Automatic Control Engineering (LSR), Technical University of Munich , Munich , Germany
Cooperative dynamic manipulation enlarges the manipulation repertoire of human-robot teams. By means of synchronized swinging motion, a human and a robot can continuously inject energy into a bulky and flexible object in order to place it onto an elevated location and outside the partners' workspace. Here, we design leader and follower controllers based on the fundamental dynamics of simple pendulums and show that these controllers can regulate the swing energy contained in unknown objects. We consider a complex pendulum-like object controlled via acceleration, and an “arm-flexible object-arm” system controlled via shoulder torque. The derived fundamental dynamics of the desired closed-loop simple pendulum behavior are similar for both systems. We limit the information available to the robotic agent about the state of the object and the partner's intention to the forces measured at its interaction point. In contrast to a leader, a follower does not know the desired energy level and imitates the leader's energy flow to actively contribute to the task. Experiments with a robotic manipulator and real objects show the efficacy of our approach for human-robot dynamic cooperative object manipulation.
Physical human-robot interaction; Cooperative manipulators; Adaptive control; Dynamics; Haptics; Intention estimation
B Philine Donner
1 Introduction
Continuous energy injection during synchronized swinging
motion enables a human and a robot to lift a bulky
flexible object together onto an elevated location. This example
scenario is illustrated in Fig. 1a and combines the
advantages of cooperative and dynamic manipulation. Cooperative
manipulation allows for the manipulation of heavier and
bulkier objects than one agent could manipulate on its own.
A commonly addressed physical human–robot collaboration
scenario is, e.g., cooperative transport of rigid bulky objects
[44]. Such object transport tasks are performed by kinematic
manipulation, i.e., the rigid object is rigidly grasped by the
manipulators [32]. In contrast, dynamic object manipulation
makes use of the object dynamics, with the advantage of
an increased manipulation repertoire: simpler end effectors
can handle a greater variety of objects faster and outside
the workspace of the manipulator. Dynamic manipulation
examples are juggling, throwing, catching [29] as well as the
manipulation of underactuated mechanisms [8], such as the
flexible and the pendulum-like objects in Fig. 1a, b.
In this article, we take a first step towards combining
the advantages of cooperative and dynamic object
manipulaFig. 1 Approach overview: (1) Interpretation of flexible object
swinging as a combination of pendulum swinging and rigid object swinging.
(2) Approximation of pendulum swinging by the t-pendulum with 1D
acceleration inputs and of flexible object swinging by the afa-system
with 1D torque inputs. (3) Projection of the t-pendulum and the
afation by investigating cooperative swinging of underactuated
objects. The swinging motion naturally synchronizes the
motion of the cooperating agents. Energy can be injected in
a favorable arm configuration for a human interaction
partner (stretched arm) and task effort can be shared among the
agents. Moreover, the accessible workspace of the human
arm and robotic manipulator is increased by the swinging
motion of the object and by a possible subsequent throwing
phase. In order to approach the complex task of cooperative
flexible object swinging in Fig. 1a, we split it up into its
two extremes, which are swinging of pendulum-like objects
which oscillate themselves (b) and swinging of rigid objects,
where the agents’ arms together with the rigid object form
an oscillating entity (c). In our initial work, we treated
pendulum-like object swinging [13] based on the
assumption that all system parameters are known. This assumption
was alleviated in [14] by an adaptive approach.
The contribution of this work is three-fold: firstly, we
experimentally verify the adaptive approach presented in
[14]. Secondly, we combine our results from
cooperative swinging of pendulum-like objects and human–human
swinging of rigid objects in [15], towards cooperative
swinging of flexible objects. Our third contribution lies in the
unified presentation of modeling the desired oscillation of
pendulum-like and flexible objects through simple pendulum
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