Model-free optimal trajectories in the image space: Application to robot vision control

International audienceSince image-based servoing is a local control solution, it requires the definition of intermediate subgoals in the sensor space when the initial robot position is far from the desired one. This paper addresses the problem of generating and tracking realistic and optimal smooth trajectories of complex features in the image space. The model of the observed target and the internal camera parameters are assumed to be unknown. First a closed-form smooth collineation path (related to a reference plane) between given starts and end-points is obtained. This path is generated in order to correspond to an optimal camera path. The trajectories of the image features (corresponding to points belonging to or not belonging to the reference plane) are then derived and tracked using image based control

Approximation of the inverse kinematics of a robotic manipulator using a neural network

A fundamental property of a robotic manipulator system is that it is capable of accurately following complex position trajectories in three-dimensional space. An essential component of the robotic control system is the solution of the inverse kinematics problem which allows determination of the joint angle trajectories from the desired trajectory in the Cartesian space. There are several traditional methods based on the known geometry of robotic manipulators to solve the inverse kinematics problem. These methods can become impractical in a robot-vision control system where the environmental parameters can alter. Artificial neural networks with their inherent learning ability can approximate the inverse kinematics function and do not require any knowledge of the manipulator geometry. This thesis concentrates on developing a practical solution using a radial basis function network to approximate the inverse kinematics of a robot manipulator. This approach is distinct from existing approaches as the centres of the hidden-layer units are regularly distributed in the workspace, constrained training data is used and the training phase is performed using either the strict interpolation or the least mean square algorithms. An online retraining approach is also proposed to modify the network function approximation to cope with the situation where the initial training and application environments are different. Simulation results for two and three-link manipulators verify the approach. A novel real-time visual measurement system, based on a video camera and image processing software, has been developed to measure the position of the robotic manipulator in the three-dimensional workspace. Practical experiments have been performed with a Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance of the radial basis function network is analysed for the manipulator operating in two and three-dimensional space and the practical results are compared to the simulation results. Advantages and disadvantages of the proposed approach are discussed

Global stability of Clifford-valued Takagi-Sugeno fuzzy neural networks with time-varying delays and impulses

summary:In this study, we consider the Takagi-Sugeno (T-S) fuzzy model to examine the global asymptotic stability of Clifford-valued neural networks with time-varying delays and impulses. In order to achieve the global asymptotic stability criteria, we design a general network model that includes quaternion-, complex-, and real-valued networks as special cases. First, we decompose the $n$-dimensional Clifford-valued neural network into $2^mn$-dimensional real-valued counterparts in order to solve the noncommutativity of Clifford numbers multiplication. Then, we prove the new global asymptotic stability criteria by constructing an appropriate Lyapunov-Krasovskii functionals (LKFs) and employing Jensen's integral inequality together with the reciprocal convex combination method. All the results are proven using linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the effectiveness of the achieved results

Locomotion Optimization of Photoresponsive Small-scale Robot: A Deep Reinforcement Learning Approach

Soft robots comprise of elastic and flexible structures, and actuatable soft materials are often used to provide stimuli-responses, remotely controlled with different kinds of external stimuli, which is beneficial for designing small-scale devices. Among different stimuli-responsive materials, liquid crystal networks (LCNs) have gained a significant amount of attention for soft small-scale robots in the past decade being stimulated and actuated by light, which is clean energy, able to transduce energy remotely, easily available and accessible to sophisticated control. One of the persistent challenges in photoresponsive robotics is to produce controllable autonomous locomotion behavior. In this Thesis, different types of photoresponsive soft robots were used to realize light-powered locomotion, and an artificial intelligence-based approach was developed for controlling the movement. A robot tracking system, including an automatic laser steering function, was built for efficient robotic feature detection and steering the laser beam automatically to desired locations. Another robot prototype, a swimmer robot, driven by the automatically steered laser beam, showed directional movements including some degree of uncertainty and randomness in their locomotion behavior. A novel approach is developed to deal with the challenges related to the locomotion of photoresponsive swimmer robots. Machine learning, particularly deep reinforcement learning method, was applied to develop a control policy for autonomous locomotion behavior. This method can learn from its experiences by interacting with the robot and its environment without explicit knowledge of the robot structure, constituent material, and robotic mechanics. Due to the requirement of a large number of experiences to correlate the goodness of behavior control, a simulator was developed, which mimicked the uncertain and random movement behavior of the swimmer robots. This approach effectively adapted the random movement behaviors and developed an optimal control policy to reach different destination points autonomously within a simulated environment. This work has successfully taken a step towards the autonomous locomotion control of soft photoresponsive robots

Design and implementation of robotic control for industrial applications

Background: With the pressing need for increased productivity and delivery of end products of uniform quality, industry is turning more and more to computer-based automation. At the present time, most of industrial automated manufacturing is carried out by specialpurpose machines, designed to perform specific functions in a manufacturing process. The inflexibility and generally high cost of these machines often referred to as hard automation systems, have led to a broad-based interest in the use of robots capable of performing a variety of manufacturing functions in a more flexible working environment and at lower production costs. A robot is a reprogrammable general-purpose manipulator with external sensors that can perform various assembly tasks. A robot may possess intelligence, which is normally due to computer algorithms associated with its controls and sensing systems. Industrial robots are general-purpose, computer-controlled manipulators consisting of several rigid links connected in series by revolute or prismatic joints. Most of today’s industrial robots, though controlled by mini and microcomputers are basically simple positional machines. They execute a given task by playing back a prerecorded or preprogrammed sequence of motion that has been previously guided or taught by the hand-held control teach box. Moreover, these robots are equipped with little or no external sensors for obtaining the information vital to its working environment. As a result robots are used mainly for relatively simple, repetitive tasks. More research effort has been directed in sensory feedback systems, which has resulted in improving the overall performance of the manipulator system. An example of a sensory feedback system would be: a vision Charge-Coupled Device (CCD) system. This can be utilized to manipulate the robot position dependant on the surrounding robot environment (various object profile sizes). This vision system can only be used within the robot movement envelop