Abstract:When there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three‐point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy‐APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefini… Show more
“…This LIDAR has a radial distance range of up to 40 m, with a precision between 30 and 50 mm, covers a plane of 270 • around the sensor, and provides 1081 points per scan, at a rate of 40 scans/s. The mobile robot processes the information gathered by this 2D LIDAR for simultaneous localization and mapping (SLAM) [53], obstacle avoidance [54,55], and autonomous path-planning and path-tracking.…”
This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak.
“…This LIDAR has a radial distance range of up to 40 m, with a precision between 30 and 50 mm, covers a plane of 270 • around the sensor, and provides 1081 points per scan, at a rate of 40 scans/s. The mobile robot processes the information gathered by this 2D LIDAR for simultaneous localization and mapping (SLAM) [53], obstacle avoidance [54,55], and autonomous path-planning and path-tracking.…”
This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak.
“…For instance, Zhao et al [32] enhanced the manipulator's predictive ability by incorporating dynamic virtual target points and utilized an extreme point jump-out function to escape oscillations. Zhang et al [33] employed tangent APF to avoid local oscillations and introduced the back virtual obstacle setting strategy-APF algorithm, which enables the agent to return to previous steps and withdraw from concave obstacles. In a rule-based fashion, Zheng et al [34] specified the condition for adding obstacles, compelling the resultant force to deflect when its angle to the obstacle center is too small.…”
Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations.
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