In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies.

This article presents the development of a robot capable of modifying its size through a wheel reconfiguration strategy. The reconfigurable wheel design is based on a four-bar retractable mechanism that achieves variation of the effective radius of the wheel. A reconfiguration index is introduced based on the number of retractable mechanisms that predicts the radius of configuration according to the number of mechanisms implemented in the wheel. The kinematics of the retractable mechanism is studied to determine the theoretical reconfiguration radius during the transformation process, it is also evaluated numerically with the help of the GeoGebra software, and it is validated experimentally by image analysis using the Tracker software. The transformation process of the robot is investigated through an analysis of forces that consider the wheel in contact with the obstacle, the calculation of the wheel torque and the height of the obstacle to be overcome are presented. On the other hand, the experimental validation of the robot reconfiguration process is presented through the percentage of success shown by the robot to overcome obstacles of 50, 75, 100 and 125 mm. In addition, measurements of energy consumption during the transformation process are reported. Reconfigurable wheels, capable of adapting their size, offer innovative solutions to various challenges across different applications such as robotic exploration and search and rescue missions to industrial settings. Some key issues that these wheels can address include terrain adaptability enhancing a robot’s mobility over uneven surfaces, or obstacles; enhanced robotic design; cost-effective design; space efficiency; and versatility in applications.

Soft fingers play an increasingly important role in robotic grippers to achieve adaptive grasping with variable stiffness features. Previous studies of soft finger design have primarily focused on the optimization of the structural parameters of existing finger structures, but limited efforts have been put into the design methodology from fundamental grasping mechanisms to finger structures with desired grasping force features. To this aim, a fundamental architecture of soft fingers is proposed for analyzing common soft finger features and the influence of the internal structures on the overall grasping performance. In addition, three general performance metrics are introduced to evaluate the grasping performance of soft finger designs. Then, a novel method is proposed to combine the variable stiffness structure with the fundamental architecture to compensate for the grasping force of the finger and linearization. Subsequently, an embodiment design is proposed with a cantilever spring-based variable stiffness (CSVS) mechanism based on the method, and a multi-objective optimization method is employed to optimize the design. Besides, the CSVS features are analyzed through finite element analysis (FEA), and by comparing the grasping performance between the V-shape finger and the CSVS finger, it is demonstrated that the design method can effectively shorten the pre-grasp stage and linearize the grasping force in the post-grasp stage while reducing the likelihood of sliding friction between the finger and the grasped object. Finally, experiments are conducted to validate the accuracy of the FEA model, the effectiveness of the design methodology, and the adaptability of the CSVS finger.