article · Engineering Research Express
Path planning presents significant challenges for mobile service robots operating in unknown and dynamic environments, as many conventional approaches only function reliably in static conditions. An integrated navigation framework combines the Bidirectional Rapidly-exploring Random Tree Star algorithm for optimal global path planning with the Dynamic Window Approach for local path adjustments. To support accurate positioning, Adaptive Monte Carlo Localization is implemented alongside mapping produced through Simultaneous Localization And Mapping and data from Light Detection and Ranging sensors. This configuration enables robots to successfully evade both stationary and moving obstacles. Furthermore, the You Only Look Once object detection algorithm is incorporated and trained, granting the system the capability to recognise surrounding people and objects. Both simulated trials and physical experiments demonstrate that this combined navigation strategy outperforms existing state-of-the-art algorithms in dynamic environments.
Mobile service robots must navigate unpredictably changing spaces safely without colliding with humans or moving obstacles. By joining global and local planning tools with real-time localization and object recognition, this research provides a verified method for guiding autonomous machines through complex, shared environments, improving their operational reliability outside of controlled static settings.
This technology applies directly to mobile service robots intended for dynamic indoor or outdoor environments where people and obstacles move unpredictably. Developers of autonomous service robotics could adopt this integrated navigation, localization, and vision pipeline. Having been tested in both computer simulations and physical experiments, the approach sits at an applied, validated stage of development, though specific commercial deployment timelines or industry-specific integrations are not detailed in the abstract.
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Abstract Path planning is an important task for mobileF service robots. Most of the available path-planning algorithms are applicable only in static environments. Achieving path planning becomes a difficult task in an unknown, dynamic environment. To solve the path planning problem in an unknown dynamic environment, this paper proposes a Bidirectional Rapidly-exploring Random Tree Star-Dynamic Window Approach (BRRT*-DWA) algorithm with Adaptive Monte Carlo Localization (AMCL). Bidirectional Rapidly-exploring Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used as a localization technique. The robot can navigate using the map file of the unknown environment created by Simultaneous Localization And Mapping (SLAM) and the data from the Light Detection and Ranging (LiDAR) sensor while avoiding dynamic and static obstacles. In addition, the object identification algorithm You Only Look Once (YOLO) was adopted, trained, and used for the robot to recognize objects and people. Results obtained from both simulation and experiment show the proposed method can achieve better performance in a dynamic environment compared with other state-of-the-art algorithms.
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DOI: 10.1088/2631-8695/ad61bd
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