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The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: (a) a real time lane detection system is proposed, based on vision system functions, using webcam camera; (b) In order to detect curve lanes, deep learning is applied for lane detection, based on fully Convolutional Neural Network (CNN); (c) The cubic spline interpolation method is used for path generation, based on Global Positioning System (GPS) data, where distance between two adjacent path generation points is same. Compared with the connection method of the cubic polynomial fitting algorithm, the curve fitted path by the cubic spline interpolation method is smoother and more satisfied with the vehicle motion pattern; (d) Based on frenet coordinate frame, optimize trajectory planning method is used to plan safe, easy and comfort trajectory. Compared to the Cartesian coordinate frame, frenet coordinate frame simplifies the solution of road curve fitting problems, especially in the case of complex road environment; (e) Fuzzy sliding mode control method is proposed for vehicle steering control. Finally, the simulation and experiment results are used to prove the effectiveness of the proposed methods.

This paper presents four novel collision avoidance processes for nonholonomic mobile robots to generate effective collision-free trajectories when forming and maintaining a formation. A collision priority strategy integrates the static and dynamic collision priorities to avoid a collision efficiently and effectively. In addition, it minimizes the turning angle of the follower robot and decreases system computation time. When avoiding collisions between robots, a novel collision avoidance algorithm is used to find a safe waypoint for the robot, based on the velocity of each robot. An adaptive tracking control algorithm, using the Lyapunov analysis, guarantees that the robotʹs trajectory and velocity tracking errors converge to zero considering parametric uncertainties of both the kinematic and dynamic models. The simulation and experiment results validate the effectiveness of the proposed method.

For real applications, rotary inverted pendulum systems have been known as the basic model in nonlinear control systems. If researchers have no deep understanding of control, it is difficult to control a rotary inverted pendulum platform using classic control engineering models, as shown in section 2.1. Therefore, without classic control theory, this paper controls the platform by training and testing reinforcement learning algorithm. Many recent achievements in reinforcement learning (RL) have become possible, but there is a lack of research to quickly test high-frequency RL algorithms using real hardware environment. In this paper, we propose a real-time Hardware-in-the-loop (HIL) control system to train and test the deep reinforcement learning algorithm from simulation to real hardware implementation. The Double Deep Q-Network (DDQN) with prioritized experience replay reinforcement learning algorithm, without a deep understanding of classical control engineering, is used to implement the agent. For the real experiment, to swing up the rotary inverted pendulum and make the pendulum smoothly move, we define 21 actions to swing up and balance the pendulum. Comparing Deep Q-Network (DQN), the DDQN with prioritized experience replay algorithm removes the overestimate of Q value and decreases the training time. Finally, this paper shows the experiment results with comparisons of classic control theory and different reinforcement learning algorithms.

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