Abstract
To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot’s navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.
Highlights
Mobile robots have various applications in various fields, and their autonomous navigation in ambient space is crucial [1]
Most scholars have optimized the global path and extracted key nodes as the navigation points of the robot, but most of them have not considered that the selection of navigation points is not significant if the navigation points are too far from the current position of the robot
The dynamic window approach (DWA) used in Chi et al [7] is based on the turning point of the global path as the key navigation point, and when the navigation point is less helpful in guiding the robot, it is costly for the robot to search for a path that can bypass this obstacle
Summary
Mobile robots have various applications in various fields, and their autonomous navigation in ambient space is crucial [1]. Ao et al [16] took the motion characteristics of the USV into account for smoothing the paths, the continuous functions in orientation and curvature achieved, which reduced the fuel consumption and space-time overhead of USV to a certain extent In theory, such algorithms ensure an optimal global path obtained, but it may not be of high practical value due to the unpredictability of local information. To achieve global path tracking and local unknown obstacle avoidance for the robot in complex and unfamiliar environments, Chi et al [7] use the improved A* algorithm to plan the robot’s navigation path and the DWA for local obstacle avoidance. Shao et al [23] exploit improved ACO to plan the robot’s path and use DWA for dynamic obstacle avoidance. AnBt aCsoleodny oOnptitmhizisat,iown (eAwCOi)ll set the corresponding motion speed for the mo Aantncdoloonbytaopintimthizeatpioonsisitainoneffiincifeontrmheuartiisotinc aflogorreitahmchthmatoumsesednitstirnibuatdedvcaonmc-e for th putatiolne.cTt hdeaatnatsacnhdooesexethceudtieretchtieonoobfsmtaocvleemaenvtomidaiannlycbeassetrdaotnegthye.accumulated
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