Should fully autonomous vehicles (FAVs) be designed inclusively and accessibly, independence will be transformed for millions of people experiencing transportation-limiting disabilities worldwide. Although FAVs hold promise to improve efficient transportation without intervention, a truly accessible experience must enable user input, for all people, in many driving scenarios (e.g., to alter a route or pull over during an emergency). Therefore, this paper explores desires for control in FAVs among (n=23) people who are blind and visually impaired. Results indicate strong support for control across a battery of driving tasks, as well as the need for multimodal information. These findings inspired the design and evaluation of a novel multisensory interface leveraging mid-air gestures, audio, and haptics. All participants successfully navigated driving scenarios using our gestural-audio interface, reporting high ease-of-use. Contributions include the first inclusively designed gesture set for FAV control and insight regarding supplemental haptic and audio cues.

This work presents a novel ultrasonic haptic interface to improve nonvisual perception and situational awareness in applications such as fully autonomous vehicles. User study results (n=14) suggest comparable performance with the dynamic ultrasonic stimuli versus a control using static embossed stimuli. The utility of the ultrasonic interface is demonstrated with a prototype autonomous small-scale robot vehicle using intersection abstractions. These efforts support the application of ultrasonic haptics for improving nonvisual information access in autonomous transportation with strong implications for people who are blind and visually impaired, accessibility, and human-in-the-loop decision making.

Game theoretic methods have become popular for planning and prediction in situations involving rich multi-agent interactions. However, these methods often assume the existence of a single local Nash equilibria and are hence unable to handle uncertainty in the intentions of different agents. While maximum entropy (MaxEnt) dynamic games try to address this issue, practical approaches solve for MaxEnt Nash equilibria using linear-quadratic approximations which are restricted to unimodal responses and unsuitable for scenarios with multiple local Nash equilibria. By reformulating the problem as a POMDP, we propose MPOGames, a method for efficiently solving MaxEnt dynamic games that captures the interactions between local Nash equilibria. We show the importance of uncertainty-aware game theoretic methods via a two-agent merge case study. Finally, we prove the real-time capabilities of our approach with hardware experiments on a 1/10th scale car platform.

This paper introduces an anytime synthesized motion planning algorithm for humanoid robots unifying locomotion and manipulation planning. It generates an entire set of motions to finish specific tasks in an environment containing obstacles by exploiting a powerful inverse kinematics (IK) engine. The IK engine can compute solutions allowing the robot to reposition its feet for meeting the task requirements. The presented planning algorithm has two primary beneficial capabilities. First, it is capable of generating a motion plan to complete a task handling multiple ordered or unordered actions. Second, it produces an initial solution very quickly, and then searches for the opportunity to improve the the solution during execution. The performance of the proposed algorithm is evaluated on the NASA-JSC Valkyrie humanoid robot by demonstrating an object pick up task in simulation and a box pick-and-place task in the real world.

We propose a shared control architecture to enable the modeling of human-in-the-loop cyber physical systems (HiLCPS) in robotics applications. We identify challenges that currently hinder ideas and concepts from cross-domain applications to be shared among different implementation of HiLCPS. The presented architecture is developed with the intent to help bridge the gap between different communities developing HiLCPS by providing a common framework, associated metrics, and associated language to describe individual elements. We provide examples from two different domains, disaster robotics and assistive robotics, to demonstrate the structure of the architecture.

Passive averaging suspensions have been proven highly effective on rovers for improving mobility by providing ground compliance. However, the passive degree of freedom poses an added challenge to the controls problem. This paper presents a controller design to increase the accuracy of straight line trajectories for rovers with passive suspension on rough terrain. The chosen approach uses only proprioceptive sensors, a 3D kinematic model, and a trivial ground plane estimator algorithm to adjust individual wheel velocities based on estimates of terrain slope. This has distinct advantages to other techniques that use global position sensors and dynamic models which inevitably lead to more complex and computationally intensive solutions. The proposed controller is simulated in Matlab and found to be successful through experiments conducted with ORYX 2.0, a planetary rover research platform. This paper presents the feed forward velocity controller design, simulations, and experimental results for validation.

This work presents a hierarchical navigation architecture and cascade classifier for sample search and identification on a space exploration rover. A three tier navigation architecture and inverse Jacobian based robot arm controller are presented. The algorithms are implemented on AERO, the Autonomous Exploration Rover, participating in the NASA Sample Return Robot Centennial Challenge in 2013 and initial results are demonstrated.