首页|New Intelligent Systems Study Results Reported from Southeast University (Bi-HS- RRT $$∧\t ext {X}$$ X : an efficient samplingbased motion planning algorithm for unknown dynamic environments)
New Intelligent Systems Study Results Reported from Southeast University (Bi-HS- RRT $$∧\t ext {X}$$ X : an efficient samplingbased motion planning algorithm for unknown dynamic environments)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on intelligent systems have been pr esented. According to news reporting originating from Southeast University by Ne wsRx correspondents, research stated, “In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in co mplex environments.” The news journalists obtained a quote from the research from Southeast Universit y: “Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still chall enging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS- RRT $$∧\t ext {X}$$ X , which facilit ates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by emplo ying the bidirectional search. When dynamic obstacles render the planned path in feasible, the bidirectional search is reactivated promptly to reconstruct the se arch tree in a local area, thereby significantly reducing the search planning ti me. Additionally, this paper adopts a hybrid heuristic sampling strategy to opti mize the planned path quality and search efficiency. The convergence of the prop osed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region.”
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