# Mobile Robots: Trajectory Analysis, Positioning and Control
## 2 The Present Issue
The authors of Contribution 4 describe the implementation of an autonomous mobile robot (AMR), specifically the MiR100, within the SmartFactory production line environment. The study focuses on integrating the AMR for tasks such as transporting and presenting products produced on the line. Key objectives included establishing effective communication between the robot and the production line, ensuring precise navigation in confined spaces, and testing various positioning markers for optimal accuracy. The results highlighted the system's ability to achieve a high degree of accuracy, with the L-marker proving to be the most reliable for repeated precise positioning and yielding an error margin of \(\pm 3\) mm. This precision was crucial for enabling seamless interaction with robotic arms during product transfers. The study demonstrates the potential of AMRs in enhancing automation in manufacturing processes, offering insights into the challenges of integrating navigation and communication systems in constrained industrial environments. The authors of Contribution 5 tackle the challenge of autonomous exploration for mobile robots in unknown environments using a deep reinforcement learning (DRL) approach. Specifically, they employ the Deep Deterministic Policy Gradient (DDPG) algorithm to guide a robot equipped with a laser sensor as it navigates and maps indoor spaces. Their custom-designed environment models the mapping process with real-time visualization, allowing the DDPG agent to make decisions based on laser sensor input, producing linear and angular velocities as outputs. The study demonstrates the effectiveness of occupancy reward-driven exploration, where the reward function incentivizes the robot to prioritize unexplored areas while penalizing actions leading to collisions or inefficiencies. The results show that the DDPG algorithm successfully optimizes the robot's motion, enabling efficient mapping with fewer collisions and smoother trajectories compared to alternative strategies. This work underscores the potential of DRL in addressing complex navigation and mapping tasks in robotics. The authors of Contribution 6 perform a comparative study of linear and nonlinear control methods for the feedback trajectory control of an X3D quadrotor. Their research focuses on evaluating four different control strategies: two linear methods (Proportional-Integral-Derivative (PID) and Linear Quadratic Regulator (LQR)) and two nonlinear methods (Fuzzy Logic Controller and Model Reference Adaptive PID Controller based on the MIT Rule). Their study aims to assess the transient performance of these controllers in terms of rise, settling, and peak times and overshoot. Using a Simulink model of the X3D quadrotor, the authors found that the LQR controller outperformed the other methods, offering robust stability and minimal overshoot. While the PID controller showed simplicity in implementation, it lacked robustness against disturbances. Nonlinear controllers, such as the Fuzzy Logic and Adaptive PID Controller, demonstrated better handling of system nonlinearities but required complex parameter tuning. These findings provide a comprehensive analysis of control strategies for quadrotors, emphasizing the balance between performance and complexity for different application scenarios. The authors of Contribution 7 propose an advanced real-time stereo visual odometry (SVO) system using an improved Kanade-Lucas-Tomasi (KLT) method. Their work addresses the computational challenges inherent in traditional SVO approaches by introducing novel techniques, including feature inheritance, an adaptive KLT tracker, and a simplified KLT matcher. These innovations minimize the time-consuming processes of feature detection and stereo matching while maintaining localization accuracy. The adaptive KLT tracker optimizes tracking by dynamically adjusting the feature window size based on average disparity, translation velocity, and yaw angle, thereby mitigating scale distortion and affine transformations.