MUONS path planning performance for a vehicle with complex suspension in Unreal

Zach Jeffries; Casey Majhor; Jake Carter; Sam Kysar; Jeremy P. Bos

doi:10.1117/12.2585773

12 April 2021 MUONS path planning performance for a vehicle with complex suspension in Unreal

Zach Jeffries, Casey Majhor, Jake Carter, Sam Kysar, Jeremy P. Bos

Proceedings Volume 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021; 117480G (2021) https://doi.org/10.1117/12.2585773
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

We present an overview of MUONS: the Michigan tech Unstructured and Off-road Navigation Stack. MUONS is a ROS-based point-cloud-based navigation stack designed to enable traversal of complex terrain that may exceed vehicle kinematic limits. We originally developed MUONS for small to mid-size skid-steer autonomous ground vehicles with no suspension. In this work we examine how MUONS performs on a simulated full-scale vehicle with complex suspension elements. By comparing the performance of the full-scale and mid-size vehicle we aim to identify the critical vehicle linkages that must be included in our simulation model. We also aim to understand the necessary changes and modifications required to adapt MUONS to full-scale Ackerman steering autonomous ground vehicles with complex suspensions.

Conference Presentation

Citation Download Citation

Zach Jeffries, Casey Majhor, Jake Carter, Sam Kysar, and Jeremy P. Bos "MUONS path planning performance for a vehicle with complex suspension in Unreal", Proc. SPIE 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021, 117480G (12 April 2021); https://doi.org/10.1117/12.2585773

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