The EMPOWER project creates opportunities for physically disabled individuals (namely quadriplegics) to operate robots through the web browser. Robotic technology has the ability to unlock productivity and grant greater purpose to mentally capable, but physically disabled, users. The goal of the EMPOWER project is to foster independence for the physically disabled by lowering barriers such as accessibility and cost. The potential of the EMPOWER project can best be illustrated through the prototyping projects between Mr. Evans and Brown University. Through our web-enabled AR.Drone (running ROS), Mr. Evans has been able to engage in activities in Providence, RI from his home in California, where he remotely pilots AR.Drones. The live video feed from the embedded cameras in the quadricopter provide Mr. Evans a vehicle to explore and interact with people and places far beyond the confines of his bed.

In this paper we present a formal approach to reciprocal collision avoidance for multiple mobile robots sharing a common 2-D or 3-D workspace whose dynamics are subject to linear differential constraints. Our approach defines a protocol for robots to select their control input independently (i.e. without coordination with other robots) while guaranteeing collision-free motion for all robots, assuming the robots can perfectly observe each other's state. To this end, we use the concept of LQR-Obstacles that define sets of forbidden control inputs that lead a robot to collision with obstacles, and extend it for reciprocal collision avoidance among multiple robots. We implemented and tested our approach in 3-D simulation environments for reciprocal collision avoidance of quadrotorhelicopters, which have complex dynamics in 16-D state spaces. Our results suggest that our approach avoids collisions among over a hundred quadrotors in tight workspaces at real-time computation rates.

That drone and its basic capabilities are summarized in Figure 1. The flexibility and controllability of quadrotor helicopters have made them a recent focus of interest among robotics and AI research groups. At the same time, their popularity has led to a wide range of commercially available platforms, some at prices accessible for undergraduate educational use. This project evaluates the ARDrone quadrotor helicopter as a basis for use in undergraduate classes such as robotics, computer vision, or embodied AI. We have encountered both successes and frustrations in using the ARDrone to date. Looking forward, the quadrotor's capabilities do seem a promising basis for future curricular offerings.