Project leader: Chung Hsieh
Each vehicle moves in a circular path that is either clockwise or counter clockwise depending on whether the vehicle measures itself to be inside or outside of the sensing area. We use a virtual sensor determined by feedback from the lab positioning system. The composite motion results in the vehicle traversing the boundary and collecting a sample of boundary point data. For multi-agent implementation, the vehicles slow down or speed up depending on whether another vehicle is close by or far away, in order to maintain some spacing between vehicles.
Link to more details-under construction! (see ACC paper below)
Project leader David Tung
All vehicles have a list of targets to visit. Each vehicle has the same list. The vehicle chooses an initial target from the list, based on distance, and moves to visit that target. If another vehicle has also chosen the same target, the two vehicles discuss who will get there first and the farther one chooses another target on the list. This is called the ``greedy'' search algorithm. David Tung implemented this on the testbed using the Steelebots. As first order control vehicles, their motion can be fairly precisely specified and thus they serve as a good choice for this method. David wrote a point-to-point controller for the Steelebots that allows a given vehicle to start at point point and travel to the next point, making sure to break and slow down so that it does not overshoot the target point. In order to implement the algorithm on the Kelly, a second order dynamic vehicle, the point to point controller from Project 3 (below) was used. The Kelly implementation also made use of their peer-to-peer wireless communication capabilities. Their trajectories were computed onboard and information was shared directly from vehicle to vehicle for a more decentralized implementation of the algorithm.
Link to more details (also see ACC paper below)
Project leader Bao Nguyen
This control algorithm is specially designed for second order vehicles for which accelerations can be well controlled but positions less so. The algorithm has two components of the motion. The first is self-propulsion in the direction of motion, the second is attraction and repulsion to other targets or vehicles using pairwise potentials. With the help of Yao-Li Chuang, Bao coded up the controller for the fans on the Kelly vehicle to mimic the accelerations resulting from virtual potentials placed at specified points. They use this method as a point-to-point controller for the greedy algorithm (above) for second order vehicles.
Link to more details-under construction! (see ACC paper below)