autonomousexploration and coverage in 3D environments recently has became a rapidly developing research field. Emerging 3D reconstruction methods, designed specifically for exploration and coverage, allows capturing ...
详细信息
ISBN:
(纸本)9783030261184;9783030261177
autonomousexploration and coverage in 3D environments recently has became a rapidly developing research field. Emerging 3D reconstruction methods, designed specifically for exploration and coverage, allows capturing an environment in a greater details. However, not much work addresses certain difficulties inherent to dense clutter environments. We observed those difficulties and made an attempt that seeks to expand the applicability of such methods to more demanding scenarios. Automating the process of testing and evaluation by designing a dense clutter environment generation algorithm (DCEGen) allows us to measure comparative performance of available algorithms. We focus on path-planning algorithms used in an unmanned ground vehicles. The algorithm was implemented and verified using Gazebo simulator.
Recent developments in 3D reconstruction systems enable to capture an environment in great detail. Several studies have provided algorithms that deal with a path-planning problem of total coverage of observable space ...
详细信息
ISBN:
(纸本)9789897583803
Recent developments in 3D reconstruction systems enable to capture an environment in great detail. Several studies have provided algorithms that deal with a path-planning problem of total coverage of observable space in time-efficient manner. However, not much work was done in the area of globally optimal solutions in dense clutter environments. This paper presents a novel solution for autonomousexploration of a cluttered 2.5D environment using an unmanned ground mobile vehicle, where robot locomotion is limited to a 2D plane, while obstacles have a 3D shape. Our explorationalgorithm increases coverage of 3D environment mapping comparatively to other currently available algorithms. The algorithm was implemented and tested in randomly generated dense clutter environments in MATLAB.
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