AdaptiveON: Adaptive Outdoor Local Navigation Method for Stable and Reliable Actions

Liang, Jing; Weerakoon, Kasun; Guan, Tianrui; Karapetyan, Nare; Manocha, Dinesh

doi:10.1109/lra.2022.3229907

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…in sand or mud). 12,13 Hence, navigating on rough/non-smooth terrains could lead to higher vibrations, 14 higher energy consumption, 15 odometry errors (i.e., due to wheel slips), and motion blur in RGB images which could significantly affect the navigation performance. 16,17 Hence, understanding terrain surface properties (i.e., surface awareness) and performing navigation along smoother regions is equally important as elevation awareness during outdoor navigation tasks.…”

Section: Introductionmentioning

confidence: 99%

Terrain-aware autonomous robot navigation in outdoor environments

Weerakoon

Sathyamoorthy

Manocha

2023

Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023

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In this work, we propose a method to perceive a terrain's geometric and other surface properties for efficient motion planning in outdoor environments. Our method incorporates two perception branches to identify the terrain's elevation and roughness separately. The first branch uses an elevation map created using LiDAR point clouds to compute a cost map that represents critical elevation changes. The second branch uses a vision-based cost map trained using RGB images, IMU, and robot odometry. Then, least-cost waypoints are calculated on a combined cost map and are followed using the Dynamic Window Approach (DWA). Our planner navigates along the least-cost waypoints while adaptively varying the velocities to reduce vibration. We evaluate our method's performance on a Husky robot in real-world environments. We observe that our method leads to higher success rates, and lower vibrations compared to state-of-the-art methods.

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