29 Nov 2023

Vehicle Modeling in Unreal Engine 4

Abstract: Vehicle modeling software has presented considerable challenges in properly representing vehicle mobility in extreme conditions. We have recently been developing new vehicle models and scenes in Unreal Engine. Unreal Engine is best known as a video game creation platform focused on graphics and has relatively few options for real world accurate physics modeling. UE4 allows for lots of customization internally or via supplemental C++ code, so this can be mitigated by the addition of various functions to account for different situations a vehicle might be in. We have successfully implemented the following: accurately functioning wheeled vehicles, tracked vehicles, and created simulated and real world environments, downloaded through Geowatch heightmaps. Each environment can have various terrain conditions including soil, rock, snow, and sand applied across its surface. Modeling snow in these environments is of particular interest and recent motion resistance and sinkage models have been integrated into the software to affect graphics and vehicle performance. This new model for vehicle mobility offers an opportunity to improve the physics and graphics of differing terrains especially for winter conditions. The new model also allows for features to be updated and added with ease in the future.

7 Feb 2022

Dynamics Modeling and Robotic-Assist, Leader-Follower Control of Tractor Convoys

Abstract: This paper proposes a generalized dynamics model and a leader-follower control architecture for skid-steered tracked vehicles towing polar sleds. The model couples existing formulations in the literature for the powertrain components with the vehicle-terrain interaction to capture the salient features of terrain trafficability and predict the vehicles response. This coupling is essential for making realistic predictions of the vehicles traversing capabilities due to the power-load relationship at the engine output. The objective of the model is to capture adequate fidelity of the powertrain and off-road vehicle dynamics while minimizing the computational cost for model based design of leader-follower control algorithms. The leader-follower control architecture presented proposes maintaining a flexible formation by using a look-ahead technique along with a way point following strategy. Results simulate one leader-follower tractor pair where the leader is forced to take an abrupt turn and experiences large oscillations of its drawbar arm indicating potential payload instability. However, the follower tractor maintains the flexible formation but keeps its payload stable. This highlights the robustness of the proposed approach where the follower vehicle can reject errors in human leader driving.