## 2 Advanced Mobile Robotics
In low-texture environments, data association and closed-loop detection are challenging problems in the SLAM (simultaneous localization and mapping) method. [PERSON] et al. [13] showed that the data association process and the back-end optimization stage with sensors, the IMU (Inertial Measurement Unit) sensor and a 2D LiDAR (Light Detection and Ranging), can be improved to enhance navigation performance. The SLAM algorithm is applied to a non-flat road with a 3D LiDAR sensor by [PERSON] et al. [14]. The data association problem for map consistency is solved with iterative matching algorithm, reducing the computation cost. [PERSON] et al. [15] showed that mobile robots can detect and recognize household furniture, using the analysis and integration of geometric features over 3D points with a color-depth camera. A spatial model of the environment is demanding work for the navigation map. [PERSON] et al. [16] introduced a new object-mapping algorithm, approximating point clouds with multiple ellipsoids. Mobile service robot needs to handle the human-machine interactive scene, and [PERSON] et al. [17] proposed a topological map construction pipeline with regional dynamic growth algorithm; the map has a representation of topological information as well as occupied information. Autonomous service robots in an indoor complex environment need to find paths with obstacles and also interact with patients. [PERSON] et al. [18] showed an adaptive neuro-fuzzy system with 3D depth camera, infrared sensors and sonar sensors for path planning of a service robot.The service robot also used facial features for personal recognition. Recently, control of UAVs (unmanned aerial vehicles) has been a challenging problem and an example of a task is that a robot needs to avoid collision with an enemy UAV in its flying path to the goal. [PERSON] et al. [19] formulated this as a Markov decision process and applied temporal-difference reinforcement learning to the robot control. The learned policy can achieve a good performance to reach the goal without colliding with the enemy. For a UAV controller, fast and iterative real-time auto-tuning of parameters has been tested for altitude control by [PERSON] [20]. It considered environmental disturbances as well as change of environmental conditions. UAV (Unmanned Aerial Vehicle) application can be available for wheat crops. [PERSON] et al. [21] investigated the working efficiency of a UAV with sprayers in the field. Spraying technology using UAVs (Unmanned Aerial Vehicles) can be applied to agricultural production for protecting plants against pesticides. [PERSON] et al. [22] showed a PWM (Pulse Width Modulation) spray system with UAVs, based on the plant diseases and insect pests map in the target area. It also considers actual droplet deposition and deposition density in the operation unit, using PID (Proportional Integrative Derivative) control. [PERSON] et al. [23] proposed a robust fault diagnosis method for quadcopter UAVs (Unmanned Aerial Vehicles). It uses a sliding mode observer to estimate the fault magnitude and location. Inspired by the flight mechanism of the insect dragonfly, a wing root control mechanism was introduced to the MAV (micro air vehicle) stabilizing hovering, by [PERSON] and [PERSON] [24]. It was shown that the mechanism can control the flight mode easily. A nonlinear robust adaptive control scheme was proposed by [PERSON] et al. [25] to handle the path following control problem, for example, steering a USV (unmanned surface vessel) to follow the desired path with disturbances. They used radial basis function neural networks for the controller. An attitude-tracking control was applied to an AUV (autonomous underwater vehicle) by [PERSON] et al. [26]. It was involved with a disturbance-rejection control for hover and transition mode of the vehicle. AUVs (autonomous underwater vehicles) need spot hover and high-speed capabilities to explore an ocean. For this application, [PERSON] et al. [27] presented an adaptive nonlinear control to an AUV with tri-tiltrotor. [PERSON] et al.