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.

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.

In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range. To address these problems, this paper analyzes the frequency domain model of the QSMO. A QSMO-based parameter adaptation method is proposed to adjust the boundary layer and widen the speed operating range, considering the QSMO bandwidth. A QSMO-based phase lag compensation method is proposed to mitigate steady-state position errors, considering the QSMO phase lag. Then, the PLL model is analyzed to select the estimated speed difference for transient position error compensation. Specifically, a transient position error compensator based on a feedback time delay neural network (FB-TDNN) is proposed. Based on the back propagation learning algorithm, the specific structure and optimal parameters of the FB-TDNN are determined during the offline training process. The proposed parameter adaptation method and two position error compensation methods were validated through simulations in simulated wide-speed operation scenarios, including sudden speed changes. Overall, the proposed scheme fully mitigates steady-state position errors, substantially mitigates transient position errors, and exhibits good stability across a wide speed range.