Unique Approaches to Utilization of Proprioceptive and Exteroceptive Sensor Systems for Autonomous Agile Mobility

Project began mid-2020 and ended Q1-2022.

This project seeks to develop and define new fundamental approaches and provide recommendations for the utilization of proprioceptive and exteroceptive sensor systems for autonomous agile mobility.

The maximization of vehicle mobility in off-road environments requires high speed iterative intervention by a vehicle’s mobility control systems during the evolution of a critical motion situation. This stands in contrast to existing modern electronic vehicle control systems such as traction control, wheel torque vectoring systems, and anti-lock braking which are largely designed to take affect after the vehicle has begun to experience loss in mobility due to a critical motion situation.

To make the paradigm shift from reaction control to agile control of mobility, fundamental research will be done to define, characterize, and evaluate various configurations of both available and theoretical sensors, implementation details, cabling, data acquisition, and post-processing solutions. Modeling, simulation, evaluation, and exploration of these solutions (along with their realistic error sources) will provide a unique contribution by providing a common reference point and practical evaluation methodology for vehicle system and sensor designers of the future.

The recommendations and guidelines that will result from this multi-year effort will seek to establish performance requirements, recommendations, and performance thresholds for each critical component in the sensor system, as well as a methodology for estimating their combined effect on wheel speed and mobility estimation. As each commercial designer of the future will have their own constraints and access to commercially available components and solutions, it is important that fundamental research such as this is done so as to best demonstrate the various design trade-offs that will be inevitably encountered when selecting and/or designing sensor system components and algorithms for this purpose.

Publications:

1. Gardner, S., Haider, M. R., Smereka, J., Jayakumar, P., Gorsich, D., Moradi, L., & Vantsevich, V. (2021). Rapid High-Dimensional Semantic Segmentation with Echo State Networks. Ground Vehicle Systems Engineering Technology Symposium (GVSETS) - Autonomy, Artificial Intelligence, Robotics (AAIR).
2. Gardner, S., Haider, M. R., Moradi, L., & Vantsevich, V. (2021). A Modified Echo State Network for Time Independent Image Classification. 64th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS).

Select references:

1. Vantsevich, V. V., Demkiv, L. I., & Klos, S. R. (2018). Analysis of Tire Relaxation Constants for Modeling Vehicle Traction Performance and Handling. In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers Digital Collection.
2. Vantsevich, V. V., Demkiv, L. I., Klos, S. R., Misko, S. R., & Moradi, L. (2019). An Experimental Study of Longitudinal Tire Relaxation Constants for Vehicle Traction Dynamics Modeling. In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers Digital Collection.

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