# Autonomous Driving Control Based on the Perception of a Lidar Sensor and Odometer
###### Abstract  
Recently, many artificial intelligence applications in smart cars have been utilized in real life. Making an unmanned ground vehicle (UGV) capable of moving autonomously has become a critical topic. Hence, in this work, a novel method for a UGV to realize path planning and obstacle avoidance is presented using a deep deterministic policy gradient approach (DDPG). More specifically, the lidar sensor mounted on the vehicle is utilized to measure the distance between the vehicle and the surrounding obstacles, and the odometer measures the mileage of the vehicle for the purpose of estimating the current location. Then, the above sensed data are treated as the training data for the DDPG training procedure, and several experiments are performed in different settings utilizing the robot operating system (ROS) and the Gazebo simulator with a real robot module, TurtleBot3, to present a comprehensive discussion. The simulation results show that using the presented design and reward architecture, the DDPG method is better than the classic deep Q-network (DQN) method, e.g., taking fewer steps to reach the goal, less training time to find the smallest number of steps for reaching the goal, and so on. unmanned ground vehicle; deep reinforcement learning; deep deterministic policy gradient; deep Q-network +
Footnote †: journal: Applied sciences  
0000-0001-9993-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-33000-3300-33000-3300-3300  
a modified DRL method. In this work, based on a deep deterministic policy gradient approach (DDPG) [4; 5; 6], a novel method of controlling a UGV autonomously is presented in order to achieve the goal of intelligent path planning and obstacle avoidance. This method allows the UGV to determine how to utilize high-level actions autonomously. In particular, the UGV acquires information only via lidar and the odometer, with no other sensors. Obviously, since the design is not affected by the GPS issue, it can be applied to both outdoor and indoor situations. Finally, based on the robot operating system (ROS) simulator [7; 8], namely, Gazebo [9], a simulation study is presented. More specifically, the TurtleBot3 module is utilized to realize these experiments. In the simulation, the effects of taking different sets of actions in different environments, as well as adopting different DRL approaches, i.e., DQN and DDPG, are investigated. The results show that the utilization of various sets of DRL methods, actions, and environments lead to different performances and accuracies. Hence, selecting the proper DRL method and action sets for a specific environment is important for the goal of intelligent path planning and obstacle avoidance. More importantly, the work considers the perception of both the lidar sensor and odometer mounted on a real machine, namely, TurtleBot3, based on different methods with distinct settings simultaneously, to present a comprehensive discussion. Several experiments with distinct settings are performed utilizing the robot operating system (ROS) and the Gazebo simulator with a real robot model, TurtleBot3, in order to present a comprehensive discussion. Here, we also mention some related studies [3; 10; 11; 12; 13; 14; 15]. For example, in [10], the authors implemented their work using only a lidar sensor, based on DDPG methods. In [11], although the authors also realized their work based on the perception of a lidar sensor and odometer, they only considered the \(e\)-greedy policy of a DQN with different parameters for updating the neural network. In addition, in [12; 13], the authors' method was implemented based on virtual robots. Rather than using a real robot machine, they directly assumed the effectiveness of the simulation properties of a virtual robot, e.g., the gyration radius and mass, the maximum speed and acceleration, and so on. Obviously, this may reduce the level of confidence.