1,559 research outputs found
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Spinal cord stimulation has enabled humans with motor complete spinal cord
injury (SCI) to independently stand and recover some lost autonomic function.
Quantifying the quality of bipedal standing under spinal stimulation is
important for spinal rehabilitation therapies and for new strategies that seek
to combine spinal stimulation and rehabilitative robots (such as exoskeletons)
in real time feedback. To study the potential for automated electromyography
(EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients
undergoing electrical spinal cord stimulation using both video and
multi-channel surface EMG recordings during spinal stimulation therapy
sessions. The quality of standing under different stimulation settings was
quantified manually by experienced clinicians. By correlating features of the
recorded EMG activity with the expert evaluations, we show that multi-channel
EMG recording can provide accurate, fast, and robust estimation for the quality
of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis
shows that the total number of EMG channels needed to effectively predict
standing quality can be reduced while maintaining high estimation accuracy,
which provides more flexibility for rehabilitation robotic systems to
incorporate EMG recordings
A functional electrical stimulation system for human walking inspired by reflexive control principles
This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions. The functionality of the functional electrical stimulation system was tested by analysis of the gait function of seven healthy volunteers during functional electrical stimulation–assisted treadmill walking compared to unassisted walking. The results demonstrated that the system was successful in synchronising muscle activation throughout the gait cycle and was able to promote functional hip and ankle movements. Overall, the study demonstrates the potential of human-inspired robotic systems in the design of assistive devices for bipedal walking
Humanoid robot walking control on inclined planes
The humanoid bipedal structure is suitable for a assitive robot functioning in the human environment. However, the bipedal walk is a difficult control problem. Walking just on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the feet is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. The average value of the body pitch angle is used as the inputs to the fuzzy logic system. A foot pitch orientation compensator implemented independently for the two feet complements the fuzyy controller. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the control method presented is successful in enabling the robot to climb slopes of 8.5 degrees (15 percent grade)
LeggedWalking on Inclined Surfaces
The main contribution of this MS Thesis is centered around taking steps
towards successful multi-modal demonstrations using Northeastern's
legged-aerial robot, Husky Carbon. This work discusses the challenges involved
in achieving multi-modal locomotion such as trotting-hovering and
thruster-assisted incline walking and reports progress made towards overcoming
these challenges. Animals like birds use a combination of legged and aerial
mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve
multi-modal locomotion. Chukars use forces generated by their flapping wings to
manipulate ground contact forces and traverse steep slopes and overhangs.
Husky's design takes inspiration from birds such as Chukars. This MS thesis
presentation outlines the mechanical and electrical details of Husky's legged
and aerial units. The thesis presents simulated incline walking using a
high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
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