This paper proposes a dual-loop control strategy to address the trajectory tracking problem of differential wheeled mobile robots (WMRs). First, the kinematic model of the WMR is established, and the dynamic model including the actuators is derived. To tackle the issue of y-axis direction divergence in existing methods, a predefined-time velocity control law based on intermediate variables is proposed. By introducing the y-axis error term into the angular velocity control, the ability to rapidly track the target trajectory is enhanced, providing a reliable velocity tracking target for the dynamic controller. Furthermore, a predefined-time nonsingular fast terminal sliding mode controller is designed, which combines a nonsingular fast terminal sliding surface with predefined-time stability theory to overcome the singularity problem in existing approaches, achieving fast and accurate tracking of velocity errors. Additionally, to improve the system’s disturbance rejection capability, a nonlinear extended state observer (NESO) is proposed to estimate external disturbances and provide feedforward compensation to the dynamic controller. Experimental results demonstrate that the proposed strategy outperforms existing methods in terms of trajectory tracking accuracy and robustness, providing an effective solution for the high-performance control of WMRs.

Aiming at the localization problem of mobile robot in construction scenes, a hybrid localization algorithm with the adaptive weights is proposed, which can effectively improve the robust localization of mobile robot. Firstly, two indicators of localization accuracy and calculation efficiency are set to reflect the robustness of localization. Secondly, the construction scene is defined as an ongoing scene, and the robust localization of mobile robot is achieved by using the measurement of artificial landmarks and matching based on generated features. Finally, the experimental results show that the accuracy of localization is up to 8.22 mm and the most matching efficiency is controlled within 0.027 s. The hybrid localization algorithm that based on adaptive weights can realize a good robustness for tasks such as autonomous navigation and path planning in construction scenes.

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.

With the rapid development of artificial intelligence and robotics, the application of robotics in the chemical domain is driving a transformation toward intelligent and large-scale research in chemistry and material science. However, sample weighing and synthesis reactions constitute critical stages in chemical experiments, which presents significant challenges for robotic gripping of reagent tubes to achieve precise measurements and collision-free path planning autonomously. Therefore, this study aims to address automation of manipulation in chemical experiments, achieving collision-free path planning and optimization under multi-objective constraints. Specifically, the trajectory planning problem for such tasks is formulated as a multi-objective optimization to minimize motion time, joint jerk and energy consumption. Then, an improved optimized multi-objective particle swarm optimization (OMOPSO) algorithm that incorporates seventh-order polynomial interpolation is proposed to improve the smoothness of robotic motion trajectory. A uniform Pareto front is obtained through a reference vector-guided leader selection mechanism, and an update strategy based on $\epsilon$-domination, and inflection point selection is proposed to balance the convergence and diversity of the solution set. Finally, simulation results and demonstrations on a manipulation platform have fully validated the feasibility and practicality of the proposed method, which further provides a reference for robotic execution of chemical experiments.