37,139 research outputs found
A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions
This paper presents an admittance controller based on the passivity theory
for a powered upper-limb exoskeleton robot which is governed by the nonlinear
equation of motion. Passivity allows us to include a human operator and
environmental interaction in the control loop. The robot interacts with the
human operator via F/T sensor and interacts with the environment mainly via
end-effectors. Although the environmental interaction cannot be detected by any
sensors (hence unknown), passivity allows us to have natural interaction. An
analysis shows that the behavior of the actual system mimics that of a nominal
model as the control gain goes to infinity, which implies that the proposed
approach is an admittance controller. However, because the control gain cannot
grow infinitely in practice, the performance limitation according to the
achievable control gain is also analyzed. The result of this analysis indicates
that the performance in the sense of infinite norm increases linearly with the
control gain. In the experiments, the proposed properties were verified using 1
degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was
used to lift and maneuver the unknown payload.Comment: Accepted in IEEE/ASME Transactions on Mechatronics (T-MECH
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks
AbstractâThis paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index TermsâFeedforward force, human motor control, impedance, robotic control. I
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