Event-triggered formation control of n-link networked stochastic robotic manipulators

This article proposes an event-triggered control framework to satisfy the tracking formation performance for a group of uncertain non-linear n-link robotic manipulators. The robotic manipulators are configured as a multi-agent system and they communicate over a directed graph (digraph). Furthermore, the non-linear robotic manipulator-multi-agent systems are subject to stochastic environmental loads. By introducing extra virtual controllers in the final step of the backstepping design, a total number of n event-triggering mechanisms are introduced independently for each link of all the robotic manipulator agents to update the control inputs in a fully distributed manner. More precisely, the actuator of each link of a particular agent is capable of being updated independent of other link actuator updates. A rigorous proof of the convergence of all the closed-loop signals in probability is then given and the Zeno phenomenon is excluded for the control event-triggered architectures. The simulation experiments finally quantify the effectiveness of proposed approach in terms of reducing the number of control updates and handling the stochastic environmental loads.

Optimization of DH Parameters of 6R Robotic Manipulator Using JAYA Approach

Manipulation of robots is carried out by the operators through a sequence of commands. However, the accuracy of the manipulation is still hindered due to parameter uncertainty. This results in less accurate robotic operations and hence affects the job performance. Due to measurement errors and sensor faults, the operation of robots malfunctions. Generally, errors are reduced with the use of high precision sensors and correcting hardware faults. However, corrections can also be made on a software platform to handle the correction process. Presently, the Denavit–Hartenberg (DH) parameters of a robotic manipulator are optimized for forward kinematics problems. The optimization is carried out using the JAYA approach. The 6R MTAB Aristo XT robot is selected as a case study for the experimental validation of the proposed approach. Experimental results reveal that the optimization of DH parameters improves accuracy for forward kinematic estimation problems. The proposed JAYA approach can further be extended to other robotic manipulators for parameter optimization problems.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

Innovative Collaborative Method for Interaction between a Human Operator and Robotic Manipulator Using Pointing Gestures

The concept of “Industry 4.0” relies heavily on the utilization of collaborative robotic applications. As a result, the need for an effective, natural, and ergonomic interface arises, as more workers will be required to work with robots. Designing and implementing natural forms of human–robot interaction (HRI) is key to ensuring efficient and productive collaboration between humans and robots. This paper presents a gestural framework for controlling a collaborative robotic manipulator using pointing gestures. The core principle lies in the ability of the user to send the robot’s end effector to the location towards, which he points to by his hand. The main idea is derived from the concept of so-called “linear HRI”. The framework utilizes a collaborative robotic arm UR5e and the state-of-the-art human body tracking sensor Leap Motion. The user is not required to wear any equipment. The paper describes the overview of the framework’s core method and provides the necessary mathematical background. An experimental evaluation of the method is provided, and the main influencing factors are identified. A unique robotic collaborative workspace called Complex Collaborative HRI Workplace (COCOHRIP) was designed around the gestural framework to evaluate the method and provide the basis for the future development of HRI applications.

Self calibration method of binocular vision based on Conformal geometric algebra

We will study binocular vision for 6-DOF robotic manipulator in conformal geometric algebra approach. We will focus on the case where some information as relative cameras positions, has been lost. In particular, we will use the construction of the manipulator to infer a self calibration method for cameras position based in binocular vision with incomplete information.