Iterative learning of human partner's desired trajectory for proactive human-robot collaboration

A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration

Master of Science

thesisParkinson Disease (PD) is a progressive and chronic movement disorder that affects an individual's ability to walk and move naturally. Research shows that training using virtual reality can offer improvements over traditional therapy and decrease the effects of some PD symptoms. In an effort to address the need for such therapeutic intervention, a Virtual Reality (VR) rehabilitation simulator was developed using 3D graphical displays in concert with haptic Smart Shoes. The system creates challenging virtual terrain to safely train participants in situations that demand greater balance and neuromuscular control. As part of this effort, an Ankle Foot Simulator (AFS) was created to mimic human gait, including ankle and foot response to a variety of terrain features. This thesis describes the development and testing of a novel AFS robot designed to mimic human gait and evaluate Smart Shoe behavior and response to perturbations. The major design requirement for the AFS robot is to reproduce natural gait dynamics by: 1) matching complex trajectories of the ankle, 2) generating Ground Reaction Forces (GRF) during normal walking gait, and 3) mimicking foot/ankle dynamics such as ankle roll over. This thesis focuses on the design and control of the AFS to achieve sufficient Range of Motion (ROM) to mimic human gait, including extreme ankle rollover, while providing appropriately fast dynamics, sufficient load capacity, and high repeatability. Design aspects of the AFS include 1) forward and inverse kinematic derivations of the ankle mechanism, 2) derivations of feedforward components of the control algorithms, and 3) mapping ankle mechanism actuator forces to ankle moments. The AFS robot tracks ankle position and orientation data to within 5.5 mm and 5.5 degrees. The AFS is also able to reproduce GRFs, including dorsal/plantar flexion and inversion/eversion ankle moments with an r2 value of 0.82 or more. The AFS also demonstrates passive ankle stiffness. To improve performance of the AFS, an iterative learning controller is suggested for future work

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

A methodology for the Lower Limb Robotic Rehabilitation system

The overall goal of this thesis is to develop a new functional lower limb robot-assisted rehabilitation system for people with a paretic lower limb. A unilateral rehabilitation method is investigated, where the robot acts as an assistive device to provide the impaired leg therapeutic training through simulating the kinematics and dynamics of the ankle and lower leg movements. Foot trajectories of healthy subjects and post-stroke patients were recorded by a dedicated optical motion tracking system in a clinical gait measurement laboratory. A prototype 6 degrees of freedom parallel robot was initially built in order to verify capability of achieving singularity-free foot trajectories of healthy subjects in various exercises. This was then followed by building and testing another larger parallel robot to investigate the real-sized foot trajectories of patients. The overall results verify the designed robot’s capability in successfully tracking foot trajectories during different exercises. The thesis finally proposes a system of bilateral rehabilitation based on the concept of self-learning, where a passive parallel mechanism follows and records motion signatures of the patient’s healthy leg, and an active parallel mechanism provides motion for the impaired leg based on the kinematic mapping of the motion produced by the passive mechanism

Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation

Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool `RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning

Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art

Robot-assisted rehabilitation has become a new mainstream trend for the treatment of stroke patients with movement disability. Pneumatic muscle (PM) is one of the most promising actuators for rehabilitation robots, due to its inherent compliance and safety features. In this paper, we conduct a systematic review on the soft rehabilitation robots driven by pneumatic muscles. This review discusses up to date mechanical structures and control strategies for PMs-actuated rehabilitation robots. A variety of state-of-the-art soft rehabilitation robots are classified and reviewed according to the actuation configurations. Special attentions are paid to control strategies under different mechanical designs, with advanced control approaches to overcome PM’s highly nonlinear and time-varying behaviors and to enhance the adaptability to different patients. Finally, we analyze and highlight the current research gaps and the future directions in this field, which is potential for providing a reliable guidance on the development of advanced soft rehabilitation robots