Bio-inspired control of redundant robotic systems: Optimization approach

Osnovni cilj ovog rada je da promoviše pristup biološki inspirisanog sinergijskog upravljanja koji omogućava da se razreši redundansa datog robotizovanog sistema koji se može koristiti i za vojne svrhe. Pokazano je da je moguće razrešiti kinematički redundansu primenom metode lokalne optimizacije i bioloških analogona - sinergijsko upravljački pristup sa uvođenjem logičkog upravljanja i distribuiranog pozicioniranja. Takođe, mogućnost prebacivanja između sinegrija u okviru jedne trajektorije je razmatrano. Na kraju, problem aktuatorske redundanse je postavljen i rešen primenom Pontrjaginovog principa maksimuma. Upravljačka sinergija je ustanovljena primenom postupka optimizacije na koordinacionom nivou. Na kraju, efikasnost predložene biološki inspirisane optimalne upravljačke sinergije je demonstriran na pogodno usvojenom robotskom sistemu sa tri stepena slobode i četiri upravljačke promenljive, kao ilustrativnog primera.The major aim of this paper is to promote a biologically inspired control synergy approach that allows the resolution of redundancy of a given robotized system which can be used for military purposes. It is shown that it is possible to resolve kinematic redundancy using the local optimization method and biological analogues - control synergy approach, introducing hypothetical control and distributed positioning. Also, the possibility of switching synergies within a single trajectory is treated, where the control synergy approach applying logical control is used. The actuator redundancy control problem has been stated and solved using Pontryagin's maximum principle. Control synergy as a class of dynamic synergy is established by the optimization law at the coordination level. Finally, the effectiveness of the suggested biologically inspired optimal control synergy is demonstrated with a suitable robot with three degrees of freedom and four control variables, as an illustrative example.

Passive Motion Paradigm: An Alternative to Optimal Control

In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the “degrees of freedom (DoFs) problem,” the common core of production, observation, reasoning, and learning of “actions.” OCT, directly derived from engineering design techniques of control systems quantifies task goals as “cost functions” and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative “softer” approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that “animates” the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints “at runtime,” hence solving the “DoFs problem” without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.” In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures

Handwriting Kinetics: A Search for Synergies

The purpose of this study was to investigate central nervous system strategies for controlling multi-finger forces in three-dimensional (3-D) space during a circle drawing task. In order to do this the Kinetic Pen, a pen capable of measuring the six-component force and moment of force that each of four individual contacts applies to the pen during writing, was developed. The synergistic actions of the contact forces, defined as kinetic synergy, were investigated in three orthogonal spaces: radial, tangential, and vertical to the circle edge during a circle drawing task. We employed varying directional (clockwise vs. counterclockwise) and pacing (self-paced vs. external-paced) conditions. Results showed that synergies between pen-hand contact forces existed in all components. Radial and tangential component synergies were greater than in the vertical component. Synergies in the clockwise direction were stronger than the counter-clockwise direction in the radial and vertical components. Pace was found to be insignificant in all conditions

Path Following for Robot Manipulators Using Gyroscopic Forces

This thesis deals with the path following problem the objective of which is to make the end effector of a robot manipulator trace a desired path while maintaining a desired orientation. The fact that the pose of the end effector is described in the task space while the control inputs are in the joint space presents difficulties to the movement coordination. Typically, one needs to perform inverse kinematics in path planning and inverse dynamics in movement execution. However, the former can be ill-posed in the presence of redundancy and singularities, and the latter relies on accurate models of the manipulator system which are often difficult to obtain. This thesis presents an alternative control scheme that is directly formulated in the task space and is free of inverse transformations. As a result, it is especially suitable for operations in a dynamic environment that may require online adjustment of the task objective. The proposed strategy uses the transpose Jacobian control (or potential energy shaping) as the base controller to ensure the convergence of the end effector pose, and adds a gyroscopic force to steer the motion. Gyroscopic forces are a special type of force that does not change the mechanical energy of the system, so its addition to the base controller does not affect the stability of the controlled mechanical system. In this thesis, we emphasize the fact that the gyroscopic force can be effectively used to control the pose of the end effector during motion. We start with the case where only the position of the end effector is of interest, and extend the technique to the control over both position and orientation. Simulation and experimental results using planar manipulators as well as anthropomorphic arms are presented to verify the effectiveness of the proposed controller

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

Despite tremendous advances in robotics, we are still amazed by the proficiency with which humans perform movements. Even new waves of robotic systems still rely heavily on hardcoded motions with a limited ability to react autonomously and robustly to a dynamically changing environment. This thesis focuses on providing possible mechanisms to push the level of adaptivity, reactivity, and robustness of robotic systems closer to human movements. Specifically, it aims at developing these mechanisms for a subclass of robot motions called “reaching movements”, i.e. movements in space stopping at a given target (also referred to as episodic motions, discrete motions, or point-to-point motions). These reaching movements can then be used as building blocks to form more advanced robot tasks. To achieve a high level of proficiency as described above, this thesis particularly seeks to derive control policies that: 1) resemble human motions, 2) guarantee the accomplishment of the task (if the target is reachable), and 3) can instantly adapt to changes in dynamic environments. To avoid manually hardcoding robot motions, this thesis exploits the power of machine learning techniques and takes an Imitation Learning (IL) approach to build a generic model of robot movements from a few examples provided by an expert. To achieve the required level of robustness and reactivity, the perspective adopted in this thesis is that a reaching movement can be described with a nonlinear Dynamical System (DS). When building an estimate of DS from demonstrations, there are two key problems that need to be addressed: the problem of generating motions that resemble at best the demonstrations (the “how-to-imitate” problem), and most importantly, the problem of ensuring the accomplishment of the task, i.e. reaching the target (the “stability” problem). Although there are numerous well-established approaches in robotics that could answer each of these problems separately, tackling both problems simultaneously is challenging and has not been extensively studied yet. This thesis first tackles the problem mentioned above by introducing an iterative method to build an estimate of autonomous nonlinear DS that are formulated as a mixture of Gaussian functions. This method minimizes the number of Gaussian functions required for achieving both local asymptotic stability at the target and accuracy in following demonstrations. We then extend this formulation and provide sufficient conditions to ensure global asymptotic stability of autonomous DS at the target. In this approach, an estimation of the underlying DS is built by solving a constraint optimization problem, where the metric of accuracy and the stability conditions are formulated as the optimization objective and constraints, respectively. In addition to ensuring convergence of all motions to the target within the local or global stability regions, these approaches offer an inherent adaptability and robustness to changes in dynamic environments. This thesis further extends the previous approaches and ensures global asymptotic stability of DS-based motions at the target independently of the choice of the regression technique. Therefore, it offers the possibility to choose the most appropriate regression technique based on the requirements of the task at hand without compromising DS stability. This approach also provides the possibility of online learning and using a combination of two or more regression methods to model more advanced robot tasks, and can be applied to estimate motions that are represented with both autonomous and non-autonomous DS. Additionally, this thesis suggests a reformulation to modeling robot motions that allows encoding of a considerably wider set of tasks ranging from reaching movements to agile robot movements that require hitting a given target with a specific speed and direction. This approach is validated in the context of playing the challenging task of minigolf. Finally, the last part of this thesis proposes a DS-based approach to realtime obstacle avoidance. The presented approach provides a modulation that instantly modifies the robot’s motion to avoid collision with multiple static and moving convex obstacles. This approach can be applied on all the techniques described above without affecting their adaptability, swiftness, or robustness. The techniques that are developed in this thesis have been validated in simulation and on different robotic platforms including the humanoid robots HOAP-3 and iCub, and the robot arms KATANA, WAM, and LWR. Throughout this thesis we show that the DS-based approach to modeling robot discrete movements can offer a high level of adaptability, reactivity, and robustness almost effortlessly when interacting with dynamic environments