Dissipative Control for Physical Human–Robot Interaction

Bowyer, Stuart A; Baena, Ferdinando Rodriguez y

doi:10.1109/tro.2015.2477956

Cited by 51 publications

(41 citation statements)

References 34 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The guidance active constraints used were dynamic non‐energy‐storing methods introduced by Kikuuwe et al, Bowyer and Baena and Enayati et al, here referred to as plastic (P), plastic with redirection (PR) and viscous with redirection (VR) respectively. The key feature of these AC enforcement methods is that they engage only when the operator moves the tool.…”

Section: Methodsmentioning

confidence: 99%

Performance metrics for guidance active constraints in surgical robotics

Enayati

Ferrigno

Momi

2017

Robotics Computer Surgery

View full text Add to dashboard Cite

Active constraint (AC)/virtual fixture (VF) is among the most popular approaches towards the shared execution of subtasks by the surgeon and robotic systems. As more possibilities appear for the implementation of ACs in surgical scenarios, the need to introduce methods that guarantee a safe and intuitive user-interaction increases. The presence of the human in the loop adds a layer of interactivity and adaptability that renders the assessment of such methods non-trivial. In most works, guidance ACs have been evaluated mainly in terms of enhancement of accuracy and completion time with little regard for other aspects such as human factors, even though the continuous engagement of these methods can considerably degrade the user experience. This paper proposes a set of performance metrics and considerations that can help evaluate guidance ACs with reference to accuracy enhancement, force characteristics and subjective aspects. The use of these metrics is demonstrated through two sets of experiments on 12 surgeons and 6 inexperienced users.

show abstract

Section: Methodsmentioning

confidence: 99%

Performance metrics for guidance active constraints in surgical robotics

Enayati

Ferrigno

Momi

2017

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…To make the motion of the tool more natural, we apply control in the nullspace of the surgeon's desired tool pose. This follows recent trends in surgical robots to ensure the surgeon is always in control of the procedure by ensuring that assistive control forces and torques are energetically dissipative [27] [9]. By leaving the surgeon fully in control of the tool, the surgeon retains full responsibility.…”

Section: Introductionmentioning

confidence: 94%

Mass and Friction Optimization for Natural Motion in Hands-On Robotic Surgery

2016

Self Cite

View full text Add to dashboard Cite

Abstract-In hands-on robotic surgery, the surgical tool is mounted on the end effector of a robot and is directly manipulated by the surgeon. This simultaneously exploits the strengths of both humans and robots; such that the surgeon directly feels tool-tissue interactions and remains in control of the procedure, while taking advantage of the robot's higher precision and accuracy. A crucial challenge in hands-on robotics for delicate manipulation tasks, such as surgery, is that the user must interact with the dynamics of the robot at the end effector, which can reduce dexterity and increase fatigue. This paper presents a null-space based optimization technique for simultaneously minimizing the mass and friction of the robot that is experienced by the surgeon. By defining a novel optimization technique for minimizing the projection of the joint friction onto the end effector, and integrating this with our previous techniques for minimizing the belted mass/inertia as perceived by the hand, a significant reduction in dynamics felt by the user is achieved. Experimental analyses in both simulation and human user trials demonstrate that the presented method can reduce the user experienced dynamic mass and friction by, on average, 44% and 41% respectively. The results presented robustly demonstrate that optimizing a robots pose can result in a more natural tool motion, potentially allowing future surgical robots to operate with increased usability, improved surgical outcomes and wider clinical uptake.

show abstract

“…In order to increase a patient's active participation in exoskeleton-assisted rehabilitation training, it is necessary to control the exoskeleton robot to track the movement of the lower limb as per the patient's active motion intention [22,23]. Impedance controller or admittance controller have been widely used in exoskeleton control due to their characteristics of human and robot physical interaction [24,25].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Zhu

Song

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer's active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer's leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient.

show abstract

Dissipative Control for Physical Human–Robot Interaction

Cited by 51 publications

References 34 publications

Performance metrics for guidance active constraints in surgical robotics

Performance metrics for guidance active constraints in surgical robotics

Mass and Friction Optimization for Natural Motion in Hands-On Robotic Surgery

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Contact Info

Product

Resources

About