A lightweight, high strength dexterous manipulator for commercial applications

The concept, design, and features are described of a lightweight, high strength, modular robot manipulator being developed for space and commercial applications. The manipulator has seven fully active degrees of freedom and is fully operational in 1 G. Each of the seven joints incorporates a unique drivetrain design which provides zero backlash operation, is insensitive to wear, and is single fault tolerant to motor or servo amplifier failure. Feedback sensors provide position, velocity, torque, and motor winding temperature information at each joint. This sensing system is also designed to be single fault tolerant. The manipulator consists of five modules (not including gripper). These modules join via simple quick-disconnect couplings and self-mating connectors which allow rapid assembly and/or disassembly for reconfiguration, transport, or servicing. The manipulator is a completely enclosed assembly, with no exposed components or wires. Although the initial prototype will not be space qualified, the design is well suited to meeting space requirements. The control system provides dexterous motion by controlling the endpoint location and arm pose simultaneously. Potential applications are discussed

Constrained trajectory optimization for kinematically redundant arms

Two velocity optimization schemes for resolving redundant joint configurations are compared. The Extended Moore-Penrose Technique minimizes the joint velocities and avoids obstacles indirectly by adjoining a cost gradient to the solution. A new method can incorporate inequality constraints directly to avoid obstacles and singularities in the workspace. A four-link arm example is used to illustrate singularity avoidance while tracking desired end-effector paths

Computational neural learning formalisms for manipulator inverse kinematics

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints

Characterization and control of self-motions in redundant manipulators

The presence of redundant degrees of freedom in a manipulator structure leads to a physical phenomenon known as a self-motion, which is a continuous motion of the manipulator joints that leaves the end-effector motionless. In the first part of the paper, a global manifold mapping reformulation of manipulator kinematics is reviewed, and the inverse kinematic solution for redundant manipulators is developed in terms of self-motion manifolds. Global characterizations of the self-motion manifolds in terms of their number, geometry, homotopy class, and null space are reviewed using examples. Much previous work in redundant manipulator control has been concerned with the redundancy resolution problem, in which methods are developed to determine, or resolve, the motion of the joints in order to achieve end-effector trajectory control while optimizing additional objective functions. Redundancy resolution problems can be equivalently posed as the control of self-motions. Alternatives for redundancy resolution are briefly discussed

Resolution of redundancy using local optimization: A synergy approach

Glavni cilj ovog rada je da promoviše sinergijski pristup koji omogućuje upravljanje redundantnim robotskim mehanizmom. Pokazano je da novo robotsko upravljanje se može ostvariti primenom odgovarajućeg biološkog analogona uvođenjem hipotetičkog upravljanja uz primenu prethodno navedenog sinergijskog pristupa, tako da je moguće razrešiti kinematičku redundansu. Takođe, u ovom radu je razmatrana i mogućnost uključivanja - prebacivanja sinergija u okviru jednog pokreta a prema zahtevima zadatka, što je realizovano korišćenjem logičkog upravljanja sa aspekta sinergijskog upravljanja.The major aim of this study is to promote a synergy approach which allows controlling redundant robotic mechanism. It will be shown that new robot control can be established using an appropriate biological analog where introducing the hypothetical control and applying the synergy approach it will be possible to resolve the kinematics redundancy. Also, in this paper the possibility of switching synergies within a single movement according to the task requirements is treated and the synergy approach with the logical controls proposed to solve this problem

Resolution of redundancy using local optimization: A synergy approach

Glavni cilj ovog rada je da promoviše sinergijski pristup koji omogućuje upravljanje redundantnim robotskim mehanizmom. Pokazano je da novo robotsko upravljanje se može ostvariti primenom odgovarajućeg biološkog analogona uvođenjem hipotetičkog upravljanja uz primenu prethodno navedenog sinergijskog pristupa, tako da je moguće razrešiti kinematičku redundansu. Takođe, u ovom radu je razmatrana i mogućnost uključivanja - prebacivanja sinergija u okviru jednog pokreta a prema zahtevima zadatka, što je realizovano korišćenjem logičkog upravljanja sa aspekta sinergijskog upravljanja.The major aim of this study is to promote a synergy approach which allows controlling redundant robotic mechanism. It will be shown that new robot control can be established using an appropriate biological analog where introducing the hypothetical control and applying the synergy approach it will be possible to resolve the kinematics redundancy. Also, in this paper the possibility of switching synergies within a single movement according to the task requirements is treated and the synergy approach with the logical controls proposed to solve this problem

An evolutionary approach to the trajectory planning of redundant robots

Several kinematic techniques for the trajectory optimization of redundant manipulators control the gripper using the pseudoinverse of the Jacobian. Nevertheless, these algorithms lead to a kind of chaotic motion with unpredictable arm configurations. This paper presents a new technique for solving the inverse kinematics problem for redundant manipulators that combines the closed-loop pseudoinverse method with genetic algorithms.N/

Mathematical modeling and control of redundant robotic manipulators using biological analog

U ovom radu razmatran je problem realizacije novih matematičkih modela redudantnih sistema kao i upravljanja korišćenjem pogodnih bioloških analogona. Ideja je bila u tome da se imitira ljudsko ponašanje i to je posebno značajno za zadatke koji su slični onim zadacima karakterističnim za ljude. Prvo, može se primeniti biološki koncept distribuiranog pozicioniranja (DP) koji je zasnovan na inercijalnim osobinama i aktuatorskim mogućnostima zglobova redudantnih sistema. Drugo, predloženo je korišćenje biološkog analogona - sinergije koja je posledica postojanja invarijantnih osobina u izvršavanju funkcionalnih kretanja. Na kraju predloženo je korišćenje drugih bioloških principa kao što su: princip minimuma interakcije koji ima važnu ulogu u hijerarhijskoj strukturi upravljanja i principa samopodešavanja, koji dozvoljava efikasnu realizaciju upravljanja koje je zasnovano na iterativnom prirodnom učenju. .In this paper it is considered problem of realization new mathematical models of redundant systems as well as control using suitable biological analog. The idea was to try to imitate human behavior and this is specially convenient for tasks which are similar to those characteristic for humans (e.g., assembling in industry, different jobs at home and in health service). If we consider speed, accuracy and stability of motion then the overall performance (taking into account all three of parameters) with machines is still far behind human reaching and grasping. Human arm movements are considered to be stable, fast and accurate due to properties of muscles, musculo-skeletal structures and hierarchical control. It was observed in the execution of functional motions that certain trajectories are preferred from the infinite number of options. Such behavior of organisms can be only explained by the existence of inherent optimization laws in self-organized systems governing the acquisition of motor skills. Existence of invariant features in the execution of functional motions points out that central nervous system (CNS) uses synergy [Bernstein, 1967](i.e. rule(s) that can be developed by the CNS based on some principles). The control of arm movement in humans relies very much on distributed usage of different joints, and inherent optimization of muscles which are active. Analysis of multi joint coordination in humans is an important source of information for synthesis of dynamic patterns in machines. In that way, model of redundant system is obtained using biomechanical principle - synergy i.e. introducing linear or nonlinear relations between independent parameters or their first derivatives which uniquely define redundant system. Moreover, one can introduced hypothetical control using joint actuator synergy approach as suggested [Bernstein, 1967] •which imposes a specific constraints) on the control variables. Also, it can be applied biological concept called distributed positioning (DP) which is based on the inertial properties and actuation capabilities of joints of redundant system. The redundancy and DP concept [Potkonjak 1990] could be used for solving the trajectory that has problems with increased dynamic requirements. The concept of DP allows us to separate the smooth and accelerated components of required motions applying appropriate smoothing technique. The inverse kinematics of redundant robot has been solved at the coordination level via (DP) concept. Moreover, it is here proposed using other biological principles such as: principle of minimum interaction which takes a main role in hierarchical structure of control and self-adjusting principle, which allows efficiently realization of control based on iterative natural learning. Motor control is organized as a multilevel structure, is generally accepted. In assistive system involves man as the decision maker, a hierarchical control structure can be proposed with three levels from the left to right: -voluntary level, coordination level, actuator level. This imposes the system is decomposed into several sybsystems with strong coupling between subsystems. Explanation of previous can be understood using the principle of maximum autonomy or minimum information exchange [Tomović, Bellman, 1970]. According to this principle, the optimal solution is to delegate the execution of functional motions to the coordination level and local regulators once the task and the task parameters have been selected. Learning control for controlling dynamics systems, a class of tracking systems is applied where it is required to repeat a given task to desired precision. The common observation that human beings can learn perfect skills trough repeated trials motivations the idea of iterative learning control for systems performing repetitive tasks. Therefore, iterative learning control requires less a priori knowledge about the controlled system in the controller design phase and also less computational effort than many other kinds of control. For improving the properties of tracking is proposed applying biological analog -principle of self-adaptability, [Grujuc,1989 ] which introduce local negative feedback on control with great amplifying.

Investigation of cyclicity of kinematic resolution methods for serial and parallel planar manipulators

Kinematic redundancy of manipulators is a well-understood topic, and various methods were developed for the redundancy resolution in order to solve the inverse kinematics problem, at least for serial manipulators. An important question, with high practical relevance, is whether the inverse kinematics solution is cyclic, i.e., whether the redundancy solution leads to a closed path in joint space as a solution of a closed path in task space. This paper investigates the cyclicity property of two widely used redundancy resolution methods, namely the projected gradient method (PGM) and the augmented Jacobian method (AJM), by means of examples. Both methods determine solutions that minimize an objective function, and from an application point of view, the sensitivity of the methods on the initial configuration is crucial. Numerical results are reported for redundant serial robotic arms and for redundant parallel kinematic manipulators. While the AJM is known to be cyclic, it turns out that also the PGM exhibits cyclicity. However, only the PGM converges to the local optimum of the objective function when starting from an initial configuration of the cyclic trajector