14 research outputs found
Optimal Torque and Stiffness Control in Compliantly Actuated Robots
Abstract — Anthropomorphic robots that aim to approach human performance agility and efficiency are typically highly redundant not only in their kinematics but also in actuation. Variable-impedance actuators, used to drive many of these devices, are capable of modulating torque and passive impedance (stiffness and/or damping) simultaneously and independently. Here, we propose a framework for simultaneous optimisation of torque and impedance (stiffness) profiles in order to optimise task performance, tuned to the complex hardware and incorporating real-world constraints. Simulation and hardware experiments validate the viability of this approach to complex, state dependent constraints and demonstrate task performance benefits of optimal temporal impedance modulation. Index Terms — Variable-stiffness actuation, physical constraints, optimal control
Design, modelling and control of a brachiating power line inspection robot
The inspection of power lines and associated hardware is vital to ensuring the reliability of the transmission and distribution network. The repetitive nature of the inspection tasks present a unique opportunity for the introduction of robotic platforms, which offer the ability to perform more systematic and detailed inspection than traditional methods. This lends itself to improved asset management automation, cost-effectiveness and safety for the operating crew. This dissertation presents the development of a prototype industrial brachiating robot. The robot is mechanically simple and capable of dynamically negotiating obstacles by brachiating. This is an improvement over current robotic platforms, which employ slow, high power static schemes for obstacle negotiation. Mathematical models of the robot were derived to understand the underlying dynamics of the system. These models were then used in the generation of optimal trajectories, using nonlinear optimisation techniques, for brachiating past line hardware. A physical robot was designed and manufactured to validate the brachiation manoeuvre. The robot was designed following classic mechanical design principles, with emphasis on functional design and robustness. System identification was used to capture the plant uncertainty and a feedback controller was designed to track the reference trajectory allowing for energy optimal brachiation swings. Finally, the robot was tested, starting with sub-system testing and ending with testing of a brachiation manoeuvre proving the prospective viability of the robot in an industrial environment
Optimal Control for Articulated Soft Robots
Soft robots can execute tasks with safer interactions. However, control
techniques that can effectively exploit the systems' capabilities are still
missing. Differential dynamic programming (DDP) has emerged as a promising tool
for achieving highly dynamic tasks. But most of the literature deals with
applying DDP to articulated soft robots by using numerical differentiation, in
addition to using pure feed-forward control to perform explosive tasks.
Further, underactuated compliant robots are known to be difficult to control
and the use of DDP-based algorithms to control them is not yet addressed. We
propose an efficient DDP-based algorithm for trajectory optimization of
articulated soft robots that can optimize the state trajectory, input torques,
and stiffness profile. We provide an efficient method to compute the forward
dynamics and the analytical derivatives of series elastic actuators
(SEA)/variable stiffness actuators (VSA) and underactuated compliant robots. We
present a state-feedback controller that uses locally optimal feedback policies
obtained from DDP. We show through simulations and experiments that the use of
feedback is crucial in improving the performance and stabilization properties
of various tasks. We also show that the proposed method can be used to plan and
control underactuated compliant robots, with varying degrees of underactuation
effectively.Comment: 14 pages, 15 figures, IEEE Transaction on Robotics (TRO
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
Dynamic Modeling, Design and Control of Wire-Borne Underactuated Brachiating Robots: Theory and Application
The ability of mobile robots to locomote safely in unstructured environments will be a cornerstone of robotics of the future. Introducing robots into fully unstructured environments is known to be a notoriously difficult problem in the robotics field. As a result, many of today's mobile robots are confined to prepared level surfaces in laboratory settings or relatively controlled environments only. One avenue for deploying mobile robots into unstructured settings is to utilize elevated wire networks. The research conducted under this thesis lays the groundwork for developing a new class of wire-borne underactuated robots that employs brachiation -- swinging like an ape -- as a means of locomotion on flexible cables.
Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics and vibrations. This thesis studies, from concept through experiments, the dynamic modeling techniques and control algorithms for wire-borne underactuated brachiating robots, to develop advanced locomotion strategies that enable the robots to perform energy-efficient and robust brachiation motions on flexible cables. High-fidelity and approximate dynamic models are derived for the robot-cable system, which provide the ability to model the interactions between the cable and the robot and to include the flexible cable dynamics in the control design. An optimal trajectory generation framework is presented in which the flexible cable dynamics are explicitly accounted for when designing the optimal swing trajectories. By employing a variety of control-theoretic methods such as robust and adaptive estimation, control Lyapunov and barrier functions, semidefinite programming and sum-of-squares optimization, a set of closed-loop control algorithms are proposed. A novel hardware brachiating robot design and embodiment are presented, which incorporate unique mechanical design features and provide a reliable testbed for experimental validation of the wire-borne underactuated brachiating robots. Extensive simulation results and hardware experiments demonstrate that the proposed multi-body dynamic models, trajectory optimization frameworks, and feedback control algorithms prove highly useful in real world settings and achieve reliable brachiation performance in the presence of uncertainties, disturbances, actuator limits and safety constraints.Ph.D
Recommended from our members
On the discretisation of actuation in locomotion: Impulse- and shape-based modelling for hopping robots
In an age where computers challenge the smartest human beings in cognitive tasks, the
conspicuous discrepancy between robot and animal locomotion appears paradoxical. While
animals can move around autonomously in complex environments, today’s robots struggle
to independently operate in such surroundings. There are many reasons for robots’ inferior
performance, but arguably the most important one is our missing understanding of complexity.
This thesis introduces the notion of discrete actuation for the study of locomotion in
robots and animals. The actuation of a system with discrete actuation is restricted to be
applied at a finite number of instants in time and is impulsive. We find that, despite their
simplicity, such systems can predict various experimental observations and inspire novel
technologies for robot design and control. We further find that, through the study of discrete
actuation, causal relationships between actuation and resulting behaviour are revealed and
become quantifiable, which relates the findings presented in this thesis to the broader concepts
of complexity, self-organisation, and self-stability.
We present four case studies in Chapters 3-6 which demonstrate how the concept of
discrete actuation can be employed to understand the physics of locomotion and to facilitate
novel robot technologies. We first introduce the impulsive eccentric wheel model which is
a discretely actuated system for the study of hopping locomotion. We find that the model
predicts robot hopping trajectories and animal related hopping characteristics by reducing the
dynamics of hopping–usually described by hybrid differential equations–to analytic maps.
The reduction of complexity of the model equations reveals the underlying physics of the
locomotion process, and we identify the importance of robot shape and mass distribution
for the locomotion performance. As a concrete application of the model, we compare the
energetics of hopping and rolling locomotion in environments with obstacles and find when
it is better to hop than to roll, based on the fundamental physical principles we discover in
the model analysis. The theoretical insights of this modelling approach enable new actuation
techniques and design for robots which we display in Robbit; a robot that uses strictly convex
foot shapes and rotational impulses to induce hopping locomotion. We show that such
systems outperform hopping with non-strictly convex shapes in terms of energy effective and robust locomotion. A system with discrete actuation motivates the exploitation of shape
and the environment to improve locomotion dynamics, which reveals advantageous effect
of inelastic impacts between the robot foot and the environment. We support this idea with
experimental results from the robot CaneBot which can change its foot shape to induce timed
impacts with the environment. Even though inelastic impacts are commonly considered
detrimental for locomotion dynamics, we show that their appropriate control improves the
locomotion speed considerably.
The findings presented in this thesis show that discrete actuation for locomotion inspires
novel ways to appreciate locomotion dynamics and facilitates unique control and design
technologies for robots. Furthermore, discrete actuation emphasises the definition of causality
in complex systems which we believe will bring robots closer to the locomotion behaviour of
animals, enabling more agile and energy effective robots
On probabilistic inference approaches to stochastic optimal control
While stochastic optimal control, together with associate formulations like Reinforcement
Learning, provides a formal approach to, amongst other, motor control,
it remains computationally challenging for most practical problems. This thesis
is concerned with the study of relations between stochastic optimal control and
probabilistic inference. Such dualities { exempli ed by the classical Kalman Duality
between the Linear-Quadratic-Gaussian control problem and the filtering
problem in Linear-Gaussian dynamical systems { make it possible to exploit advances
made within the separate fields. In this context, the emphasis in this work
lies with utilisation of approximate inference methods for the control problem.
Rather then concentrating on special cases which yield analytical inference
problems, we propose a novel interpretation of stochastic optimal control in the
general case in terms of minimisation of certain Kullback-Leibler divergences. Although
these minimisations remain analytically intractable, we show that natural
relaxations of the exact dual lead to new practical approaches. We introduce two
particular general iterative methods ψ-Learning, which has global convergence
guarantees and provides a unifying perspective on several previously proposed
algorithms, and Posterior Policy Iteration, which allows direct application of inference
methods. From these, practical algorithms for Reinforcement Learning,
based on a Monte Carlo approximation to ψ-Learning, and model based stochastic
optimal control, using a variational approximation of posterior policy iteration,
are derived.
In order to overcome the inherent limitations of parametric variational approximations,
we furthermore introduce a new approach for none parametric approximate
stochastic optimal control based on a reproducing kernel Hilbert space
embedding of the control problem.
Finally, we address the general problem of temporal optimisation, i.e., joint
optimisation of controls and temporal aspects, e.g., duration, of the task. Specifically, we introduce a formulation of temporal optimisation based on a generalised
form of the finite horizon problem. Importantly, we show that the generalised
problem has a dual finite horizon problem of the standard form, thus bringing
temporal optimisation within the reach of most commonly used algorithms.
Throughout, problems from the area of motor control of robotic systems are
used to evaluate the proposed methods and demonstrate their practical utility