A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning

The adjustment times of the attitude alignment are fluctuated due to the fluctuation of the contact force signal caused by the disturbing moments in the compliant peg-in-hole assembly. However, these fluctuations are difficult to accurately measure or definition as a result of many uncertain factors in the working environment. It is worth noting that gravitational disturbing moments and inertia moments significantly impact these fluctuations, in which the changes of the peg concerning the mass and the length have a crucial influence on them. In this paper, a visual grasping strategy based on deep reinforcement learning is proposed for peg-in-hole assembly. Firstly, the disturbing moments of assembly are analyzed to investigate the factors for the fluctuation of assembly time. Then, this research designs a visual grasping strategy, which establishes a mapping relationship between the grasping position and the assembly time to improve the assembly efficiency. Finally, a robotic system for the assembly was built in V-REP to verify the effectiveness of the proposed method, and the robot can complete the training independently without human intervention and manual labeling in the grasping training process. The simulated results show that this method can improve assembly efficiency by 13.83%. And, when the mass and the length of the peg change, the proposed method is still effective for the improvement of assembly efficiency.

Genetic Algorithm-based Task Allocation in Multiple Modes of Human-Robot Collaboration Systems with Two Cobots

In this paper, we introduce a human-robot collaboration (HRC) mold assembly cell to cope with small-volume mold production and reduce the risk of musculoskeletal disorders (MSDs) on a human worker during manual mold assembly operation. Besides, the wide variety of types and weights of the mold components motivated us to design an HRC system that consists of two robots. Therefore, we propose two collaboration modes for HRC systems using two robots and develop a task-allocation model to demonstrate the application of these collaboration modes in the mold assembly. The task-allocation model assigns a task based on the task characteristics and capability of agents in the collaboration cell. First, we decompose the assembly operation into functional actions to analyze the characteristics of tasks. Then, we obtain the agent assignment preference based on task characteristics and capability of agents using the analytic network process. Finally, we apply the genetic algorithm in the final task allocation to minimize assembly time, use of a less capable agent, and ergonomic risk. This paper contributes to expanding the HRC system with two robots in the mold assembly to allow the execution of a greater diversity of tasks and improve the assembly time and MSD risk level for the human worker.

Chitosan-Based Adhesive: Optimization of Tensile Shear Strength in Dry and Wet Conditions

Commercial adhesives present a high bond strength and water resistance, but they are considered non-healthier products. Chitosan can be considered as an interesting biosourced and biodegradable alternative, despite its low water resistance. Here, its wood bonding implementation and its tensile shear strength in dry and wet conditions were investigated depending on its structural characteristics. Firstly, the spread rate, open assembly time, drying pressure, drying temperature, and drying time have been determined for two chitosans of European pine double lap specimens. An adhesive solution spread rate of 1000 g·m−2, an open assembly time of 10 min, and a pressure temperature of 55 °C for 105 min led to a bond strength of 2.82 MPa. Secondly, a comparison between a high molecular weight/low deacetylation degree chitosan and a lower molecular weight/higher deacetylation degree chitosan was conducted. Tests were conducted with beech simple lap specimens in accordance with the implementation conditions and the conditioning treatments in wet and dry environments required for thermoplastic wood adhesive standards used in non-structural applications (EN 204 and EN 205). The results clearly revealed the dependence of adhesive properties and water resistance on the structural features of chitosans (molecular weight and deacetylation degree), explaining the heterogeneity of results published notably in this field.