No abstract

Selecting the optimal speed for dynamic obstacle avoidance in complex man–machine environments is a challenging problem for mobile robots inspecting hazardous gases. Consideration of personal space is important, especially in a relatively narrow man–machine dynamic environments such as warehouses and laboratories. In this study, human and robot behaviors in man–machine environments are analyzed, and a man–machine social force model is established to study the robot obstacle avoidance speed. Four typical man–machine behavior patterns are investigated to design the robot behavior strategy. Based on the social force model and man–machine behavior patterns, the fuzzy-PID trajectory tracking control method and the autonomous obstacle avoidance behavior strategy of the mobile robot in inspecting hazardous gases in a relatively narrow man–machine dynamic environment are proposed to determine the optimal robot speed for obstacle avoidance. The simulation analysis results show that compared with the traditional PID control method, the proposed controller has a position error of less than 0.098 m, an angle error of less than 0.088 rad, a smaller steady-state error, and a shorter convergence time. The crossing and encountering pattern experiment results show that the proposed behavior strategy ensures that the robot maintains a safe distance from humans while performing trajectory tracking. This research proposes a combination autonomous behavior strategy for mobile robots inspecting hazardous gases, ensuring that the robot maintains the optimal speed to achieve dynamic obstacle avoidance, reducing human anxiety and increasing comfort in a relatively narrow man–machine environment.

A variety of toxic and hazardous gases exist in many working or storage environments, and once a leak occurs, it will cause a wide range of casualties and immeasurable economic losses. To improve the timeliness and flexibility of the inspection equipment in a man-machine hybrid environment, this research provides an effective and practical method to improving the autonomous behavior strategy of robots in complex environments. The team designs a mobile inspection robot used to inspect hazardous gas which can be controlled remotely or inspect dangerous gas independently first, the robot is controlled by a host computer through a hybrid architecture control system mainly composed by Micro Controller Unit (MCU), Raspberry Pi based on Wi-Fi formed by a wireless router. The robot uses the hub motors as the driving wheels, equipped with multiple sensors and cameras to complete forward and backward, turning, possesses target tracking, obstacle avoidance and real-time image transmission functions softly. Based on the robot kinematic model built, this paper studies the interfering objects four typical behavior patterns, establishes a social force model to guide the robot behavioral decision-making under dynamic obstacles. To verify the effectiveness of the combination motion strategy, Matlab is used to establish a shared environment space, trajectory simulation of the state of human crossing behavior and encounter behavior, and the speed change trend of the mobile robot is recorded. Combines the dynamic obstacle avoidance strategy presented based on fuzzy thinking, an autonomous tracking strategy by adopting Fuzzy-PID controller is proposed, the experiments under fixed trajectory indoors verifies that when the robot deviates from the predetermined trajectory during inspection operations, the Fuzzy-PID control system can effectively help the robot return to the target trajectory, which the PTZ is stable and the video image is clear and the frame rate is more than 10 frames per second shows the good inspection performance. The results of simulation and test are similar, and the robot position error up to 10mm, the angle error up to 0.1 rad, which draws the reliability and accuracy of the inspection method,. And the robot can fast responsive, the small-scale time delay of the speed change does not affect the obstacle avoidance effectiveness, the robot and human will always maintain a sufficient safety distance to reduce the "sense of crisis" that humans towards to the robot.

Compared with the public inspection places like airports, squares, and waiting halls, places such as laboratories, warehouses are generally relatively narrow, and humans may suddenly appear in the passages. Once a hazardous or toxic gas leak occurs in such spaces, it will cause a wide range of casualties and immeasurable economic losses. To ensure the safety of personal and property, realize real-time monitoring in unattended or dangerous situations, a robot moving mechanism with an effective and practical autonomous behavior strategy is proposed in this paper. Firstly, a mobile robot equipped with a hazardous gases detector and a high-definition (HD) industrial camera is designed, which can be controlled remotely or work autonomously. Next, a fixed-point and a fixed-trajectory method is adopted to avoidance: establish the social force model; study the human's (interfering object's) typical behavior patterns; based on Fuzzy thinking, a dynamic obstacle avoidance strategy is presented, and combine the dynamic obstacle avoidance strategy, a combination autonomous behavior strategy with a Fuzzy-PID controller is proposed. Finally, the crossing pattern and encountering pattern simulations and experiments results show that the robot can realize the dynamic obstacle avoidance in a relatively narrow man-machine environment smoothly and always maintain a sufficient safety distance to reduce the "sense of crisis" humans towards the robot. The trajectory experiments in such spaces verify that when the robot deviates from the predetermined position during inspection operations, the combination autonomous behavior strategy can help the robot return to the target trajectory. Furthermore, compared with the PID control method, the robot position error up to 0.098m, the angle error up to 0.088rad, the Fuzzy-PID controller is more stable, accurate, and fast, which draws the stability and reliability of the strategy.