Compliant and stable robot control for physical human-robot cooperation

The main goal of this thesis is to accomplish a compliant and stable closed-loop physical human-robot cooperation by guaranteeing the safety metrics for all of the agents in a shared-working environment. There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Initially, having robots capable of safely interacting with humans was of interest for medical applications (e.g., rehabilitation, surgical). Despite significant improvement in some specific applications like medical robotics, there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this thesis, a fractal attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a fractal attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. Thus, the proposed Fractal Impedance Controller (FIC) in this thesis is robust for low-bandwidth applications. I have tested this controller with a torque controlled 7-DoF manipulator. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Furthermore, it can automatically deal with the extra energy introduced by changes in interaction conditions, null-space controller and environment. Therefore, on the one hand these properties make the controller ideal for applications where the dynamic interaction at the end-effector is challenging to be characterized a priori, such as proximate physical human-robot cooperation and unknown dynamics. On the other hand in remote human-robot cooperation, robotic teleoperation provides human-in-the-loop capabilities of complex manipulation tasks in dangerous or remote environments, such as planetary exploration or nuclear decommissioning. This thesis proposes a novel bilateral telemanipulation architecture using the proposed passive FIC, which does not depend upon an active viscous component for guaranteeing stability. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), the proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability in my telemanipulation test scenarios. I also validate its performance with extreme delays up to 1s and communication bandwidths as low as 10Hz. The results of the carried out experiments validate a consistent stability when using the proposed controller in challenging conditions, regardless of operator expertise. The proposed fractal impedance controller in this thesis exploits its non-linear stiffness to adapt to multiple cooperative scenarios without tuning the controller. Furthermore, the FIC has an intuitive method to adjust the impedance that can be performed online without affecting stability. The experimental results, carried out using 2 torque controlled 7-DoF manipulators and the Sigma.7 haptic device, also show that the proposed method can perform tasks such as drilling, moving objects with unknown dynamics, and interacting with humans without re-tuning the controller's impedance in a tele-cooperative manner consisting of multi-agents in the loop. The FIC also allows identifying the highest impedance profile for a robot experimentally, and it bounds the maximum momentum generated while moving. Thus, it opens new possibilities for developing better adaptive controllers by coupling the proposed method with learning and optimisation algorithms to modulate its behaviour without the risk of incurring instability issues.YOUTUBE links to Chapters 3,4 & 5 below

Data-efficient Non-parametric Modelling and Control of an Extensible Soft Manipulator

Data-driven approaches have shown promising results in modeling and controlling robots, specifically soft and flexible robots where developing physics-based models are more challenging. However, these methods often require a large number of real data, and gathering such data is time-consuming and can damage the robot as well. This paper proposed a novel data-efficient and non-parametric approach to develop a continuous model using a small dataset of real robot demonstrations (only 25 points). To the best of our knowledge, the proposed approach is the most sample-efficient method for soft continuum robot. Furthermore, we employed this model to develop a controller to track arbitrary trajectories in the feasible kinematic space. To show the performance of the proposed approach, a set of trajectory-tracking experiments has been conducted. The results showed that the robot was able to track the references precisely even in presence of external loads (up to 25 grams). Moreover, fine object manipulation experiments were performed to demonstrate the effectiveness of the proposed method in real-world tasks. Finally, we compared its performance with common data-driven approaches in seen/useen-before trajectory tracking scenarios. The results validated that the proposed approach significantly outperformed the existing approaches in unseen-before scenarios and offered similar performance in seen-before scenarios

Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance

The ability of animals to interact with complex dynamics is unmatched in robots. Especially important to the interaction performances is the online adaptation of body dynamics, which can be modeled as an impedance behaviour. However, the variable impedance controller still possesses a challenge in the current control frameworks due to the difficulties of retaining stability when adapting the controller gains. The fractal impedance controller has been recently proposed to solve this issue. However, it still has limitations such as sudden jumps in force when it starts to converge to the desired position and the lack of a force feedback loop. In this manuscript, two improvements are made to the control framework to solve these limitations. The force discontinuity has been addressed introducing a modulation of the impedance via a virtual antagonist that modulates the output force. The force tracking has been modeled after the parallel force/position controller architecture. In contrast to traditional methods, the fractal impedance controller enables the implementation of a search algorithm on the force feedback to adapt its behaviour on the external environment instead of on relying on \textit{a priori} knowledge of the external dynamics. Preliminary simulation results presented in this paper show the feasibility of the proposed approach, and it allows to evaluate the trade-off that needs to be made when relying on the proposed controller for interaction. In conclusion, the proposed method mimics the behaviour of an agonist/antagonist system adapting to unknown external dynamics, and it may find application in computational neuroscience, haptics, and interaction control.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Robust high-transparency haptic exploration for dexterous telemanipulation

Robotic teleoperation provides human-in-the-loop capabilities of complex manipulation tasks in dangerous or remote environments, such as for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for guaranteeing stability. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), our proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability in our telemanipulation test scenarios. We also validate its performance with extreme delays up to 1 s and communication bandwidths as low as 10 Hz. All results validate a consistent stability when using the proposed controller in challenging conditions, regardless of operator expertise

A Projected Inverse Dynamics Approach for Multi-arm Cartesian Impedance Control

Lin H-C, Smith J, Kouhkiloui Babarahmati K, Dehio N, Mistry M. A Projected Inverse Dynamics Approach for Multi-arm Cartesian Impedance Control. In: IEEE/RSJ Int. Conf. on Robotics and Automation. 2018.We propose a model-based control framework for multi-arm manipulation of a rigid object subject to external disturbances. The control framework, based on projected inverse dynamics, decomposes the control law into constrained and unconstrained subspaces. Unconstrained components accomplish the motion task with a desired 6-DOF Cartesian impedance behaviour against external disturbances. Meanwhile, the constrained component enforces contact and friction constraints by optimising for contact forces within the constrained subspace. External disturbances are explicitly compensated for without using force/torque sensors at the contact points. The approach is evaluated on a dual-arm platform manipulating a rigid object while coping with unknown object dynamics and human interaction

Fractal Impedance for Passive Controllers

There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a fractal attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a fractal attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector.Comment: Video Available at https://youtu.be/S06_hqn3Nv

HapFIC: an adaptive force/position controller for safe environment interaction in articulated systems

Haptic interaction is essential for the dynamic dexterity of animals, which seamlessly switch from an impedance to an admittance behaviour using the force feedback from their proprioception. However, this ability is extremely challenging to reproduce in robots, especially when dealing with complex interaction dynamics, distributed contacts, and contact switching. Current model-based controllers require accurate interaction modelling to account for contacts and stabilise the interaction. In this manuscript, we propose an adaptive force/position controller that exploits the fractal impedance controller's passivity and non-linearity to execute a finite search algorithm using the force feedback signal from the sensor at the end-effector. The method is computationally inexpensive, opening the possibility to deal with distributed contacts in the future. We evaluated the architecture in physics simulation and showed that the controller can robustly control the interaction with objects of different dynamics without violating the maximum allowable target forces or causing numerical instability even for very rigid objects. The proposed controller can also autonomously deal with contact switching and may find application in multiple fields such as legged locomotion, rehabilitation and assistive robotics

Fractal impedance for passive controllers: a framework for interaction robotics

There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a Fractal Attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a Fractal Attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector