Simple mathematical models of physical systems give us tremendous insight into the nature of their underlying dynamics and the control challenges they present. Stripping away unnecessary details can illuminate fundamental dependencies and focus engineering efforts on the most critical problems. The challenge comes when models become so familiar that they are no longer taken as simplifications but mistaken for reality itself. Opportunities and possibilities that lie outside the bounds of those simple models are subsequently missed.
Ground vehicle dynamics represent an ideal illustration of these principles. While vehicles are complex multi-body systems with uncertain and nonlinear dynamic properties due to tire mechanics, many simplifications of these dynamics exist. By choosing the right level of abstraction for a model, anything from parallel parking to a race car driver’s choice of trajectory to drifting with smoking rear tires can be explained clearly and concisely. The choice of model is important, however, since what one model illuminates, another may obscure. This talk demonstrates through mathematics and video from experiments how simple models can be used to accurately control automated vehicles through even the most extreme maneuvers on the race track. Lap times comparable to expert drivers and drifting maneuvers beyond the precision of a human are possible with models consisting of only a few state variables.
Just as models can guide or limit us in our work as researchers and engineers, our models of what it means to be a researcher or academic can sometimes artificially limit our impact in the world. The talk concludes with some simple models of how the robotics community can provide necessary leadership and technical guidance as society wrestles with the changes arising from our technologies.