Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.

The behavior of nature’s predators is considered for designing a high speed tracking controller for nonholonomic vehicles, whose dynamics are represented using a unicycle model. To ensure that the vehicle behaves intuitively and mimics the biologically inspired predator-prey interaction, saturation constraints based on Ackermann steering kinematics are added. A new strategy for mapping commands back into a viable envelope is introduced, and the restrictions are accounted for using Lyapunov stability criteria. Following verification of the saturation constraints, the proposed algorithm was implemented on a testing platform. Stable trajectories of up to 9 m/s were achieved. The results presented show that the algorithm demonstrates significant promise in high speed trajectory tracking with obstacle avoidance.

Functional Electrical Stimulation is a promising approach to treat patients by stimulating the peripheral nerves and their corresponding motor neurons using electrical current. This technique helps maintain muscle mass and promote blood flow in the absence of a functioning nervous system. The goal of this work is to control muscle contractions from FES via three different algorithms and assess the most appropriate controller providing effective stimulation of the muscle. An open-loop system and a closed-loop system with three types of model-free feedback controllers were assessed for tracking control of skeletal muscle contractions: a Proportional-Integral (PI) controller, a Model Reference Adaptive Control algorithm, and an Adaptive Augmented PI system. Furthermore, a mathematical model of a muscle-mass-spring system was implemented in simulation to test the open-loop case and closed-loop controllers. These simulations were carried out and then validated through experiments ex vivo. The experiments included muscle contractions following four distinct trajectories: a step, sine, ramp, and square wave. Overall, the closed-loop controllers followed the stimulation trajectories set for all the simulated and tested muscles. When comparing the experimental outcomes of each controller, we concluded that the Adaptive Augmented PI algorithm provided the best closed-loop performance for speed of convergence and disturbance rejection.

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