Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skidsteered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.

Under the DARPA MARS 2020 program, Perceptek has developed a technical foundation for performing roadway operations in both structured and unstructured environments. Fully autonomous roadway operations require a large set of atomic functionalities that must seamlessly perform in concert in complex and dynamic environments. PercepTek has developed multiple atomic functionalities and implemented a robot control architecture called ARTEA (Autonomous Robotic Test and Evaluation Architecture) that blends these atomic functionalities into one cohesive system that can perform complex missions and reason about its environment. Some of the atomic functionalities that have been implemented and integrated onto our robotic test platform are visionbased road-following for both structured and unstructured roads, vision/radar-based vehiclefollowing, safety gap maintenance, road feature detection and response, road sign detection/ recognition, and pedestrian detection. In this paper, technical details of each of the individual robotic functionalities are presented along with their performance and limitations. We then discuss some of the critical components of the ARTEA architecture that are used for blending inputs from disparate functionalities and performing reasoning about the environment. Field testing was a critical aspect of our development process and we will discuss the test platform that was used to develop and test our robotic system. At the culmination of our MARS 2020 effort we performed a robotic test drive from Denver Colorado to New Orleans Louisiana in which we tested and evaluated various aspects of our system. We finally discuss our performance and the limitations of our system for this drive.

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