Tele-operation of a Lunar rover from a control station on Earth involves a latency of several seconds due primarily to the finite speed (light-speed) of command and sensor signals, and this latency creates a difficult control task for the human operator. Two predictive displays, which seek to aid viewer perception of present events, were designed and evaluated for the specific task of driving a rover with multi-second latency. These displays provided visual information to the human operator on the rover's real-time locomotion, as predicted from control inputs executed by the operator. A human-subject experiment with 12 participants was conducted in which the participants navigated an actual rover through obstacle courses. There were four experimental conditions repeated by each participant: (1) delayed video feed only, (2, 3) two predictive displays based on delayed video feed, and (4) a reference condition of video feed with no delay. Inferential statistics show that both predictive displays significantly improved performance in terms of time taken to complete the courses, and one of the displays facilitated performance approaching that with no delay. No trends were observed in terms of collisions with or encroachments near obstacles.

In this paper we present a prototype system that aids the operator of a Personal Air Vehicle (PAV) by actively monitoring vehicle surroundings and providing autonomous control inputs for obstacle avoidance. The prototype is developed for a Personal Air Transportation System (PATS) that will enable human operators with low level of technical knowledge to use aerial vehicles for a day-to-day commute. While most collision avoidance systems used on human controlled vehicles override operator input, our proposed system allows the operator to be in control of the vehicle at all times. Our approach uses a dynamic potential field to generate pseudo repulsive forces that, when converted into control inputs, force the vehicle on a trajectory around the obstacle. By allowing the vehicle control input to be the sum of operator controls and collision avoidance controls, the system ensures that the operator is in control of the vehicle at all times. We first present a dynamic repulsive potential function and then provide a generic control architecture required to implement the collision avoidance system on a mobile platform. Further, extensive computer simulations of the proposed algorithm are performed on a quadcopter model, followed by hardware experiments on a stereo vision sensor. The proposed collision avoidance system is computationally inexpensive and can be used with any sensor that can produce a point cloud for obstacle detection.