Abstract
Relative efficient computation of motion plans by a Wheeled Robot Platform (WRP) has resulted through an incremental sampling of the considered environment. Although the existing sampling-based path planning techniques hardly converge into an optimal solution and this creates a challenge for a path planner specifically in case of a complex environment occupied with dynamic obstacles. A partially unknown environment creates reasonable problems for a Point-To-Point (PTP) robot to decide certain steps which would merge to the ultimate optimum path solution. To tackle the aforesaid challenge, this concerned paper proposes a noble algorithmic approach, Mapped-RRT*, for concurrent accumulation of optical information from a concerned surrounding GPS (Global Positioning System)-denied indoor environment by combining 2D (Two-Dimensional) and 3D (Three Dimensional) sensor data followed by the application of a sampling pathfinding a strategy for obtaining near-optimum point to point navigation by a wheeled robot. The VO (Visual Odometry) is obtained from a simultaneously created map of the traversed trajectory and serves as an instant perceptible reference during the movement of the mobile robot. For near-perfect detection of every possible on-path obstacles both 2D as well as 3D sensors are used together to collect fused data based on their respective preferential identification process, and a unique algorithmic approach has been proposed for run time traversal map creation along with probabilistic optimized path plan, executable within the optimum amount of time. A numerical comparison of the proposed technique with the performance of conventional strategies with respect to taken parameters confirms the reliability of the carried-out technique. The experimental results would be a citation for future research work in justifying the constructed algorithm within the domain of clash-free ORN (Optimized Robot Navigation).
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Chaudhuri, R., Deb, S., Saha, S. (2023). Mapped-RRT* a Sampling Based Mobile Path Planner Algorithm. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_11
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