Keyframe Tracking-based Path Planner for Vision-based Autonomous Mobile Robots

Choi, Jihoon; Chae, Hee-Won; Song, Jae-Bok

doi:10.23919/iccas47443.2019.8971460

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Many path planning methods have been explored in different approaches to obtain autonomous ability [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Some approaches in the literature [ 25 , 26 , 27 , 28 ] are developed under a robot operating system (ROS), offering a core set of software for operating robots that can be extended according to the prebuilt libraries and packages.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Roy

Sheu

et al. 2023

Sensors

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Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.

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