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Avoiding Obstacles with Geometric Constraints on LiDAR Data for Autonomous Robots

Sarkar, M and Prabhakar, M and Ghose, D (2023) Avoiding Obstacles with Geometric Constraints on LiDAR Data for Autonomous Robots. In: 3rd Congress on Intelligent Systems, CIS 2022, 5-6 September 2022, Bengaluru, pp. 749-761.

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Official URL: https://doi.org/10.1007/978-981-19-9225-4_54


Obstacle avoidance continues to be a major component of any motion planning algorithms for autonomous mobile robots operating in previously unknown environment. We present a novel approach of designing obstacle avoidance algorithms with two-dimensional LiDAR data. We derive mathematical expressions for the region of interest (RoI) using conic section of an ellipse on the two-dimensional data points collected from the LiDAR. The derivation is based on the physical constraints from the robot’s body, and actuator limits for successfully avoiding any obstacle on the path. One of the major challenges while designing an obstacle avoidance algorithm using the greedy approach of avoiding the closest obstacle from the LiDAR data is the freezing robot problem. Most often the robot freezes as it gets stuck at a local minima between two closely placed obstacles. We propose to mitigate this problem by considering an elliptical field of view (FoV) for the LiDAR data and giving importance to all the obstacles inside the elliptical section proportional to their respective distances from the robot. We test our algorithm in the simulated environment of ROS-Gazebo and show how elliptical FoV is better at navigating narrow passages compared to conventional circular FoV based algorithms

Item Type: Conference Paper
Publication: Lecture Notes in Networks and Systems
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords: Motion planning; Optical radar; Robot programming, Autonomous Mobile Robot; Avoiding obstacle; Ellipticals; Field of views; Geometric constraint; LiDAR; Motion planning algorithms; Obstacle avoidance algorithms; Obstacles avoidance; Two-dimensional, Image segmentation
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 18 Apr 2023 10:20
Last Modified: 18 Apr 2023 10:20
URI: https://eprints.iisc.ac.in/id/eprint/81334

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