Workload management is a critical concern in shared control of unmanned ground vehicles. In response to this challenge, prior studies have developed methods to estimate human operators’ workload by analyzing their physiological data. However, these studies have primarily adopted a single-model-single-feature or a single-model-multiple-feature approach. The present study proposes a Bayesian inference model to estimate workload, which leverages different machine learning models for different features. We conducted a human subject experiment with 24 participants, in which a human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) with the help from an autonomy while performing a surveillance task simultaneously. Participants’ eye-related features, including gaze trajectory and pupil size change, were used as the physiological input to the proposed Bayesian inference model. Results show that the Bayesian inference model achieves a 0.823 F1 score, 0.824 precision, and 0.821 recall, outperforming the single models.

Multi-objective or multi-destination path planning is crucial for mobile robotics applications such as mobility as a service, robotics inspection, and electric vehicle charging for long trips. This work proposes an anytime iterative system to concurrently solve the multi-objective path planning problem and determine the visiting order of destinations. The system is comprised of an anytime informable multi-objective and multi-directional RRT*algorithm to form a simple connected graph, and a solver that consists of an enhanced cheapest insertion algorithm and a genetic algorithm to solve approximately the relaxed traveling salesman problem in polynomial time. Moreover, a list of waypoints is often provided for robotics inspection and vehicle routing so that the robot can preferentially visit certain equipment or areas of interest. We show that the proposed system can inherently incorporate such knowledge to navigate challenging topology. The proposed anytime system is evaluated on large and complex graphs built for real-world driving applications. C++ implementations are available at: https://github.com/UMich-BipedLab/IMOMD-RRTStar.

This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control (NMPC) problems with uncertain system dynamics. EPC leverages an affine dynamics model that is updated online via Locally Weighted Projection Regression (LWPR) to capture nonlinearities, uncertainty, and changes in the system dynamics. This model enables the NMPC problem to be re-cast as a quadratic program (QP). The QP can then be solved via multi-parametric techniques to generate a mapping from state, reference, and dynamics model to a locally optimal, affine feedback control law. These mappings, in conjunction with the basis functions learned via LWPR, define a notion of experience for the controller as they capture the full input-output relationship for previous actions the controller has taken. The resulting experience database allows EPC to avoid solving redundant optimization problems, and as it is constructed online, enables the system to operate more efficiently over time. We demonstrate the performance of EPC through a set of hardware-in-the-loop simulation studies of a quadrotor micro air vehicle that is subjected to unmodeled exogenous perturbations.

In this work, we consider the problem of fast, accurate control of a robot with constrained dynamics. We present a new nonlinear model predictive control (MPC) technique, Nonlinear Partial Enumeration (NPE), that combines online and offline computation in a nonlinear version of the partial enumeration method for MPC, thereby dramatically decreasing the compute time per control iteration. We apply NPE to the problem of MAV flight and demonstrate through a set of simulation trials that NPE outperforms other fast control methodologies during aggressive motion and enables the system to learn a reusable set of local feedback controllers that enable more efficient operation over time.

This paper presents a hierarchical planning architecture that generates vehicle trajectories that adapt to uncertain, spatially-varying disturbance forces toward enhanced tracking performance. The disturbance force is modeled as a discrete conditional probability distribution that is updated online by local measurements as the vehicle navigates. A global planner identifies the optimal route to the goal and adapts this route according to a cost metric derived from the belief distribution on the disturbance force. A local planner embeds the belief distribution in the trajectory generation process to compute dynamically feasible trajectories along the global plan that evolve with the belief. Simulation studies analyze and demonstrate the increased trajectory tracking accuracy via the proposed methodology with a single vehicle and the impact of the approach to multiple agents performing collaborative inference toward enhanced collective performance.