Autonomous mobile robots have become integral to daily life, providing crucial services across diverse domains. This paper focuses on path following, a fundamental technology and critical element in achieving autonomous mobility. Existing methods predominantly address tracking through steering control, neglecting velocity control or relying on path-specific reference velocities, thereby constraining their generality. In this paper, we propose a novel approach that integrates the conventional pure pursuit algorithm with deep reinforcement learning for a nonholonomic mobile robot. Our methodology employs pure pursuit for steering control and utilizes the soft actor-critic algorithm to train a velocity control strategy within randomly generated path environments. Through simulation and experimental validation, our approach exhibits notable advancements in path convergence and adaptive velocity adjustments to accommodate paths with varying curvatures. Furthermore, this method holds the potential for broader applicability to vehicles adhering to nonholonomic constraints beyond the specific model examined in this paper. In summary, our study contributes to the progression of autonomous mobility by harmonizing conventional algorithms with cutting-edge deep reinforcement learning techniques, enhancing the robustness of path following.

With the rapid development of automation and intelligent technology, mobile robots have shown wide application potential in many fields, and accurate navigation systems are the key to robots completing tasks. This paper proposes an enhanced pure pursuit path tracking algorithm for mobile robots, which is optimized using NSGA-II, with high-precision GNSS navigation for accurate positioning. The improved algorithm considers the dynamic characteristics and real–world operating conditions of the robot, optimizing steering decisions to enhance path tracking accuracy. Experimental results demonstrate the effectiveness of the algorithm: with a look–ahead distance of 0.5 and a maximum linear velocity of 3, the average absolute pose error (APE) is reduced by 14.63%, while a velocity of 4 reduces the APE by 55.94%. The enhanced algorithm significantly reduces path deviation and improves navigation performance.

The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of $100\%$. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.

In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

Simultaneous localization and mapping (SLAM) based on light detection and ranging (LiDAR) is a trending research direction. This paper proposes a tightly coupled LiDAR inertial odometry (LIO) system that integrates geometric and textural information from the environment, aiming to address the key challenge of autonomous robot navigation. Most existing methods only consider geometric information and assume that reflective surfaces are ideal Lambertian surfaces, neglecting the variable reflective properties of surfaces at different angles and distances. Innovatively, we propose a new LiDAR intensity model that compensates for distortions in intensity values caused by incident angles and distances. Furthermore, we use intensity discontinuities as line features to reflect texture changes in the environment. These points are utilized to calculate residuals of line features, combined with surface features derived from geometric information, and integrated into the observation equations as part of an error-state Kalman filter sensor fusion scheme, thus constructing a tightly coupled LiDAR-inertial odometry system. Compared with the classic LOAM algorithm based on feature extraction and a state-of-the-art algorithm FAST-LIO, the results demonstrate that our system improves trajectory accuracy by approximately 16.1% with LOAM, 15.8% with LOAM+IMU and 11.2% with FAST LIO, rotation accuracy error precision by approximately 15.9% with LOAM, 15.3% with LOAM+IMU and 8.2% with FAST-LIO, demonstrate that propsed method significantly outperforms these methods in terms of localization accuracy and path generation precision.