Programming robotic assembly for industrial small-batch production is challenging; hence, it is vital to increase robustness and reduce development effort in order to achieve flexible robotic automation. A human who has made an assembly error will often simply undo the process until the error is undone and then restart the assembly. Conceptually, robots could do the same. This paper introduces a programming model that enables robot assembly programs to be executed in reverse. We investigate the challenges in running robot programs backwards and present a classification of reversibility characteristics. We demonstrate how temporarily switching the direction of program execution can be an efficient error recovery mechanism. Moreover, we demonstrate additional benefits arising from supporting reversibility in an assembly language, such as increased code reuse and automatically derived disassembly sequences. As a default approach to reversibility, we use program inversion and statement-level inversion of commands, but with a novel override option providing alternative sequences for asymmetric reverse actions. To efficiently program for this model, this paper introduces a new domain-specific language, SCP-RASQ (Simple C++ Reversible Assembly SeQuences). In initial experiments, where 200 consecutive assemblies of two industrial cases were performed, 18 of 22 errors were corrected automatically using only the trial-and-error capabilities that come from reverse execution.

This study proposes and investigates a new type of bi-criteria minimization (BCM) for the motion planning and control of redundant robot manipulators to address the discontinuity problem in the infinity-norm acceleration minimization (INAM) scheme and to guarantee the final joint velocity of motion to be approximate to zero. This new type is based on the combination of minimum weighted velocity norm (MWVN) and INAM criteria, and thus is called the MWVN–INAM–BCM scheme. In formulating such a scheme, joint-angle, joint-velocity, and joint-acceleration limits are incorporated. The proposed MWVN–INAM–BCM scheme is reformulated as a quadratic programming problem solved at the joint-acceleration level. Simulation results based on the PUMA560 robot manipulator validate the efficacy and applicability of the proposed MWVN–INAM–BCM scheme in robotic redundancy resolution. In addition, the physical realizability of the proposed scheme is verified in practical application based on a six-link planar robot manipulator.

Model predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

This paper addresses the problem of optimal mechanisms design, for the geometric structure and control parameters of mechanisms with complex kinematics, which is one of the most intricate problems in contemporary robot modeling. The problem is stated by means of task requirements and performance constraints, which are specified in terms of the end-effector's position and orientation to accomplish the task. Usually, this problem does not fulfill the characteristics needed to use gradient-based optimization algorithms. In order to circumvent this issue, we introduce case studies of optimization models using evolutionary algorithms (EAs), which deal with the concurrent optimization of both: structure and control parameters. We define and review several optimization models based on the workspace, task and dexterity requirements, such that they guarantee an adequate performance under a set of operating and joint constraints, for a Delta parallel manipulator. Then, we apply several methodologies that can approximate optimal designs. Additionally, we compare the EAs with a quasi-Newton method (the BFGS), in order to show that the last kind of methods is not capable of solving the problem if the initial point is not very close to a local optimum. The results provide directions about the best state-of-the-art EA for addressing different design problems.

A new minimally invasive surgical (MIS) robot consisting of a spherical remote center motion (RCM) mechanism with modular design is proposed. A multi-objective dimensional synthesis model is presented to obtain the excellent performance indices. There are four objectives: a global kinematic index, a compactness index, a global comprehensive stiffness index, and a global dynamic index. Other indices characterizing the design requirement, such as workspace, mechanical parameter, and mass, are chosen as constraints. A new decoupled mechanism is raised to solve the coupled motion between the linear platform and the four degrees of freedom (DoF) of surgical instrument as a result of post-driving motors. Another new mechanical decoupled method is proposed to eliminate the coupled motion between the wrist and the forceps, enhance the dexterity of surgical instrument, and improve the independence of each motor. Then, a 7-DoF MIS robotic prototype based on optimization results has been built up. Experiment results validate the effectiveness of the two mechanical decoupled methods. The position change of the RCM point, accuracy, and repeatability of the MIS robot meet the requirements of MIS. Successful animal experiments validate the effectiveness of the novel MIS robot.

In this paper, a new calibration method for open-chain robotic arms is developed. By incorporating both prior parameter information and artifact measurement data, and by taking recourse to Bayesian inference methods, not only are the robot kinematic parameters updated but also confidence bounds are computed for all measurement data. In other words, for future measurement data not only the most likely end-effector configuration is estimated but also the uncertainty represented as 95% confidence bounds of that pose is computed. To validate the proposed calibration method, a three degree-of-freedom robotic arm was designed, constructed, and calibrated using both typical regression methods and the proposed calibration method. The results of an extensive set of experiments are presented to gauge the accuracy and utility of the proposed calibration method.

The problem of robust adaptive control of a robotic manipulator subjected to uncertain dynamics and joint space constraints is addressed in this paper. Command filters are used to overcome the time derivatives of virtual control, thus reducing the need for desired trajectory differentiations. A barrier Lyapunov function is used to deal with the joint space constraints. A robust adaptive support vector regression architecture is used to reduce filtering errors, approximation errors and handle dynamic uncertainties. The stability analysis of the closed-loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all signals of the closed-loop system are bounded. Simulations show the effectiveness of the proposed control strategy.