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With future robotic systems likely to hinge in part on the quality of their associated video image processing, a system has been evolved in which interior and exterior profiles of objects seen by a video camera can be computed in real time.

A comprehensive multi‐sensor system using three‐dimensional vision, a laser range‐finder, an ultrasonic proximity sensor, and an infra‐red radar for position location, have been built into a mobile robot.

This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

Mobile robots offer more flexibility and scope than the fixed single arm type. French researchers are putting considerable effort into developing such devices and here results of their efforts are described.

Developing general purpose mobile robots is very much dependent on the availability of suitable sensors. A common choice by researchers is ultrasonics and work at the Laboratory of Applied Advanced Electronic Techniques (LATEA) has resulted in a simple, self‐adapting and self‐correcting ultrasonic sensor,

Branched articulated robots (BARs) are highly non-linear systems; accurate dynamic identification is critical for model-based control in high-speed and heavy-load applications. However, due to some dynamic parameters being redundant, dynamic models are singular, which increases the calculation amount and reduces the robustness of identification. This paper aims to propose a novel methodology for the dynamic analysis and redundant parameters elimination of BARs.

At first, the motion of a rigid body is divided into constraint-dependent and constraint-independent. The redundancy of inertial parameters is analyzed from physical constraints. Then, the redundant parameters are eliminated by mapping posterior links to their antecedents, which can be applied for re-deriving the Newton–Euler formulas. Finally, through parameter transformation, the presented dynamic model is non-singular and available for identification directly.

New formulas for redundant parameters elimination are explicit and computationally efficient. This unifies the redundant parameters elimination of prismatic and revolute joints for BARs, and it is also applicable to other types of joints containing constraints. The proposed approach is conducive to facilitating the modelling phase during the robot identification. Simulation studies are conducted to illustrate the effectiveness of the proposed redundant parameters elimination and non-singular dynamic model determination. Experimental studies are carried out to verify the result of the identification algorithm.

This work proposes to determine and directly identify the non-redundant dynamic model of robots, which can help to reduce the procedure of obtaining the reversible regression matrix for identification.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

A simple ultrasonic sensor coupled with a colour sensing system has been designed and built in the laboratory of the French National Institute of Applied Science at Rennes, and mounted on the laboratory's VESA mobile robot. It can distinguish simple configurations of obstacles and measure their distance from the robot, and within certain limits of illumination can classify obstacles to eight reference colours.

Through a unique design and application of a photo‐elastic material a gripper was developed that was able to perform high precision assembly tasks with a robot on ultra thin‐walled(R/T300).

An extensive survey of over 300 reports worldwide shows that the state‐of‐the‐art in tactile sensing — defined as continuously variable touch sensing over an area where there is special resolution — is primitive. Only now is a new level of sophistication beginning to appear. However, for industrial systems the simplest may prove to be the most reliable.