A major challenge with off-road autonomous navigation is the lack of maps or road markings that can be used to plan a path for autonomous robots. Classical path planning methods mostly assume a perfectly known environment without accounting for the inherent perception and sensing uncertainty from detecting terrain and obstacles in off-road environments. Recent work in computer vision and deep neural networks has advanced the capability of terrain traversability segmentation from raw images; however, the feasibility of using these noisy segmentation maps for navigation and path planning has not been adequately explored. To address this problem, this research proposes an uncertainty-aware path planning method, URA* using aerial images for autonomous navigation in off-road environments. An ensemble convolutional neural network (CNN) model is first used to perform pixel-level traversability estimation from aerial images of the region of interest. The traversability predictions are represented as a grid of traversal probability values. An uncertainty-aware planner is then applied to compute the best path from a start point to a goal point given these noisy traversal probability estimates. The proposed planner also incorporates replanning techniques to allow rapid replanning during online robot operation. The proposed method is evaluated on the Massachusetts Road Dataset, the DeepGlobe dataset, as well as a dataset of aerial images from off-road proving grounds at Mississippi State University. Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths.

Robots are being considered for applications where they serve as proxies for humans interacting with another human,such as emergency response, hostage negotiation, and healthcare. In these domains, the human (“dependent”) is connected to multiple other humans (“controllers”) via the robot proxy for long periods of time. The dependent may want to interact with humans but also to engage the robot as a medium to the World Wide Web. In the future, medical personnel may use the robot for victim assistance and comfort while the rescue team plans and monitors extrication. Other applications include healthcare, where the robot is the link between a patient and a medical provider for intermittent,routine interactions, and hostage negotiation, where police may use a bomb squad robot to talk with and build rapport with the suspect while the SWAT team uses the robot’s sensors to build and maintain situation awareness.Under funding from the National Science Foundation, we are finishing the first year of investigating verbal and nonverbal communication strategies for robots who are serving as proxies for multiple humans interact with the humans who are dependent on them. Our work posits that such a robot would occupy a novel social medium position according to the Computers as Social Actors (CASA) model [Nass,Steuer, and Tauber1994] [Reeves and Nass1996]. Given that teleoperated robots are treated socially, it is unlikely that a rescue robot would be treated as a pure medium even if playing music or videos. Likewise, the limitations of autonomy and the interactions of specialists with the dependent prevent the robot from being a true social actor. Instead, social actor and pure medium are two extremes on the agent identity spectrum, with a social medium occupying a middle position.A social medium would be perceived as a loyal, helpful “go between” who is an advocate for the dependent, rather than a device for accomplishing the goals of multiple controllers(medical specialist, structural engineer, rescue operations official, etc.). To explore the social medium identity,we have built a physical prototype of a Survivor Buddy and are creating autonomous affective behaviors and a social medium toolkit to explore human-robot interaction.