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

Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco$^2$ manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system

Man-machine cooperation in advanced teleoperation

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints

The dynamic control of robotic manipulators in space

Described briefly is the work done during the first half year of a three-year study on dynamic control of robotic manipulators in space. The research focused on issues for advanced control of space manipulators including practical issues and new applications for the Virtual Manipulator. In addition, the development of simulations and graphics software for space manipulators, begun during the first NASA proposal in the area, has continued. The fabrication of the Vehicle Emulator System (VES) is completed and control algorithms are in process of development

Inverse kinematics problem in robotics using neural networks

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods

An optimization approach for the inverse kinematics of a highly redundant robot

This paper describes a robot with 12 degrees of freedom for pick-and-place operations using bricks. In addition, an optimization approach is proposed, which determines the state of each joint (that establishes the pose for the robot) based on the target position while minimizing the effort of the servomotors avoiding the inverse kinematics problem, which is a hard task for a 12 DOF robot manipulator. Therefore, it is a multi-objective optimization problem that will be solved using two optimization methods: the Stretched Simulated Annealing method and the NSGA II method. The experiments conducted in a simulation environment prove that the proposed approach is able to determine a solution for the inverse kinematics problem. A real robot formed by several servomotors and a gripper is also presented in this research for validating the solutions.info:eu-repo/semantics/publishedVersio

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.

Closed-loop inverse kinematics for redundant robots: Comparative assessment and two enhancements

Motivated by the need of a robust and practical Inverse Kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used Closed-Loop IK (CLIK) methods for redundant robots, analysing their main points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is a new filter for the singular values of the Jacobian matrix that guarantees that its conditioning remains stable, while none of the filters found in literature is successful at doing so. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that improves on the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.This research is partially funded by the CSIC project CINNOVA (201150E088) and the Catalan grant 2009SGR155. A. Colomé is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (AP2010-1989).Peer Reviewe

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.