Dynamic path planning for traversing autonomous vehicle in off-road environment using MAVS

Fahmida Islam; John E. Ball; Chris Goodin

doi:10.1117/12.2618720

6 June 2022 Dynamic path planning for traversing autonomous vehicle in off-road environment using MAVS

Fahmida Islam, John E. Ball, Chris Goodin

Proceedings Volume 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022; 121150N (2022) https://doi.org/10.1117/12.2618720
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Navigating through an unknown environment is one of the key capabilities of the autonomous ground vehicle (AGV). It is relatively easier to traverse through an on-road environment, but moving through an off-road environment is challenging because of the inadequate driving path, obstacles, surface roughness, slope, dense vegetation, poor soil conditions, lack of road signs, etc. This complexity requires simulation of the AGV through many environments before facing an unknown situation in the real world. This paper proposes a dynamic path planning technique for AGV navigation in an off-road environment. First, a brief discussion on the traversability model and the factors mentioned in state of art such as vegetation density, soil condition, surface roughness, and slope individually. Secondly, we have proposed some modifications in the traversability model by introducing weights and exponents with each factor. Then, the A* algorithm has been analyzed by penalizing the weight and exponent values to get an optimal path. We used the Mississippi State University Autonomous Vehicular Simulator (MAVS) for simulation by creating an off-road scenario and using an AGV. We have generated a cost map based on the traversability score. The higher the score, the better the result. The optimal path is selected considering the traversability score. The novelty of this work is that we are exploring the linearity and non-linearity of the traversability model and applying the A* algorithm for path planning.

Conference Presentation

Citation Download Citation

Fahmida Islam, John E. Ball, and Chris Goodin "Dynamic path planning for traversing autonomous vehicle in off-road environment using MAVS", Proc. SPIE 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, 121150N (6 June 2022); https://doi.org/10.1117/12.2618720

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