This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

Bolts constitute a very important subset of mechanical fasteners. In order to tighten bolts, a degree of bolt preload scatter is to be expected. Since the torque control of tightening bolts is the most popular means of controlling the preload, an appropriate tightening torque becomes pivotal. This paper investigates the torque control problem of bolt tightening process. This process is not as simple as it looks because the inherently nonlinear process contains many uncertainties. To conquer the adverse effects of the uncertainties, this paper designs an adaptive-gain second-order sliding mode controller. Theoretically, such design can guarantee that the bolt tightening process has the closed-loop stability in the sense of Lyapunov. From the aspect of practice, the control method is carried out by a platform. Some comparisons illustrate the feasibility and effectiveness of the designed controller.

This article focuses on the control of a group of nonholonomic mobile robots. A leader-follower coordinated control scheme is developed to achieve formation maneuvers of such a multi-robot system. The scheme adopts the methodology of integral sliding mode control to form up and maintain the robots in predefined trajectories. The dynamic equations of the scheme are subject to mismatched uncertainties. The mismatched uncertainties challenge formation stabilization because they cannot be suppressed by the invariance of integral sliding mode control. In light of Lyapunov’s direct method, a sufficient condition is drawn to guarantee the reachability condition of integral sliding mode control in the presence of the mismatched uncertainties. To verify the feasibility and effectiveness of the proposed strategy, simulation results are illustrated by an uncertain multi-robot system composed of three nonholonomic mobile robots.