An Overview on Principles for Energy Efficient Robot Locomotion

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied

Design and Control of Compliant Actuation Topologies for Energy-Efficient Articulated Robots

Considerable advances have been made in the field of robotic actuation in recent years. At the heart of this has been increased use of compliance. Arguably the most common approach is that of Series-Elastic Actuation (SEA), and SEAs have evolved to become the core component of many articulated robots. Another approach is integration of compliance in parallel to the main actuation, referred to as Parallel- Elastic Actuation (PEA). A wide variety of such systems has been proposed. While both approaches have demonstrated significant potential benefits, a number of key challenges remain with regards to the design and control of such actuators. This thesis addresses some of the challenges that exist in design and control of compliant actuation systems. First, it investigates the design, dynamics, and control of SEAs as the core components of next-generation robots. We consider the influence of selected physical stiffness on torque controllability and backdrivability, and propose an optimality criterion for impedance rendering. Furthermore, we consider disturbance observers for robust torque control. Simulation studies and experimental data validate the analyses. Secondly, this work investigates augmentation of articulated robots with adjustable parallel compliance and multi-articulated actuation for increased energy efficiency. Particularly, design optimisation of parallel compliance topologies with adjustable pretension is proposed, including multi-articulated arrangements. Novel control strategies are developed for such systems. To validate the proposed concepts, novel hardware is designed, simulation studies are performed, and experimental data of two platforms are provided, that show the benefits over state-of-the-art SEA-only based actuatio

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Safe and Compliant Control of Redundant Robots Using Superimposition of Passive Task-Space Controllers

Safe and compliant control of dynamic systems in interaction with the environment, e.g., in shared workspaces, continues to represent a major challenge. Mismatches in the dynamic model of the robots, numerical singularities, and the intrinsic environmental unpredictability are all contributing factors. Online optimization of impedance controllers has recently shown great promise in addressing this challenge, however, their performance is not sufficiently robust to be deployed in challenging environments. This work proposes a compliant control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy. Our control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimization is used). We leverage and introduce a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. Our experimental results demonstrate that the proposed method achieves sub-centimeter tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. he proposed framework achieves such results without knowledge of the robot dynamics and thanks to its passivity is intrinsically stable. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation

A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation

Coupling Disturbance Compensated MIMO Control of Parallel Ankle Rehabilitation Robot Actuated by Pneumatic Muscles

To solve the poor compliance and safety problems in current rehabilitation robots, a novel two-degrees-offreedom (2-DOF) soft ankle rehabilitation robot driven by pneumatic muscles (PMs) is presented, taking advantages of the PM’s inherent compliance and the parallel structure’s high stiffness and payload capacity. However, the PM’s nonlinear, time-varying and hysteresis characteristics, and the coupling interference from parallel structure, as well as the unpredicted disturbance caused by arbitrary human behavior all raise difficulties in achieving high-precision control of the robot. In this paper, a multi-input-multi-output disturbance compensated sliding mode controller (MIMO-DCSMC) is proposed to tackle these problems. The proposed control method can tackle the un-modeled uncertainties and the coupling interference existed in multiple PMs’ synchronous movement, even with the subject’s participation. Experiment results on a healthy subject confirmed that the PMs-actuated ankle rehabilitation robot controlled by the proposed MIMO-DCSMC is able to assist patients to perform high-accuracy rehabilitation tasks by tracking the desired trajectory in a compliant manner

Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer

In this paper, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real training. Position control has been popular as it has been shown to be more sample efficient and intuitive to combine with other planning algorithms. However, for position control gain tuning is required to achieve the best possible policy performance. We show that instead, using a torque-based action space enables task-and-robot agnostic learning with less parameter tuning and mitigates the sim-to-reality gap by taking advantage of torque control's inherent compliance. Also, we accelerate the torque-based-policy training process by pre-training the policy to remain upright by compensating for gravity. The paper showcases the first successful sim-to-real transfer of a torque-based deep reinforcement learning policy on a real human-sized biped robot. The video is available at https://youtu.be/CR6pTS39VRE