Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path’s guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified.

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm.

Real-time detection of contact states, such as stick-slip interaction between a robot and an object on its end effector, is crucial for the robot to grasp and manipulate the object steadily. This paper presents a novel tactile sensor based on electromagnetic induction and its application on stick-slip interaction. An equivalent cantilever-beam model of the tactile sensor was built and capable of constructing the relationship between the sensor output and the friction applied on the sensor. With the tactile sensor, a new method to detect stick-slip interaction on the contact surface between the object and the sensor is proposed based on the characteristics of friction change. Furthermore, a prototype was developed for a typical application, stable wafer transferring on a wafer transfer robot, by considering the spatial magnetic field distribution and the sensor size according to the requirements of wafer transfer. The experimental results validate the sensing mechanism of the tactile sensor and verify its feasibility of detecting stick-slip on the contact surface between the wafer and the sensor. The sensing mechanism also provides a new approach to detect the contact state on the soft-rigid surface in other robot-environment interaction systems.

RNA viruses cause numerous infectious diseases in humans and animals. The crosstalk between RNA viruses and the innate DNA sensing pathways attracts increasing attention. Recent studies showed that the cGAS-STING pathway plays an important role in restricting RNA viruses via mitochondria DNA (mtDNA) mediated activation. However, the mechanisms of cGAS mediated innate immune evasion by RNA viruses remain unknown. Here, we report that seneca valley virus (SVV) protease 3C disrupts mtDNA mediated innate immune sensing by cleaving porcine cGAS (pcGAS) in a species-specific manner. Mechanistically, a W/Q motif within the N-terminal domain of pcGAS is a unique cleavage site recognized by SVV 3C. Three conserved catalytic residues of SVV 3C cooperatively contribute to the cleavage of pcGAS, but not human cGAS (hcGAS) or mouse cGAS (mcGAS). Additionally, upon SVV infection and poly(dA:dT) transfection, pcGAS and SVV 3C colocalizes in the cells. Furthermore, SVV 3C disrupts pcGAS-mediated DNA binding, cGAMP synthesis and interferon induction by specifically cleaving pcGAS. This work uncovers a novel mechanism by which the viral protease cleaves the DNA sensor cGAS to evade innate immune response, suggesting a new antiviral approach against picornaviruses.

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent’s transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system.