A multi-vehicle testbed for multi-modal, decentralized sensing of the environment
This video presents experiments conducted on our multi-vehicle environmental testbed. The first experiment is the prioritized multi-sensing behavior, which uses a magnetic and light source for the robots to take sensor measurements. During the experiment robots create a Voronoi partitioning based on their current positions, then optimize the probability of detection map (POD) to determine a point in their own Voronoi partition that has the highest probability of obtaining good sensor readings from a magnetic or light source. When a point by each robot is identified, the robots navigate to that goal point while avoiding collisions with obstacles in the environment as well as with other robots. As the robots navigate to the goal point sensor measurements are taken. When each robot has reached its destination, sensor measurement information as well as position information is exchanged. This is then repeated for 30 iterations and the resulting magnetic and light intensity maps are shown. The second experiment conducted is the adaptive decentralized nonholonomic sensor network algorithm. Initially the robot at the right corner is exposed to the greatest concentration of light. Light measurements are taken using four precision light sensors mounted on the aluminum plate. Every robot is moving to their estimated center of mass of its respective Voronoi cell. As they are using a difference equation approximation of the adaptive law we notice back and forth motion. This is due to the centers of mass variation at every calculation time. In another top view, we see the actual distribution of the robots and their Voronoi partitions related to the light concentration. This is approximately a centroidal Voronoi tesselation based on the estimate distribution function which is illustrated as well. The light distribution is changed by switching the light source concentration. At this point the robots change their estimate parameter vectors in order to excite the adaptation law by increasing the difference between what they are measuring and what they are estimating. The robots then start moving towards the new light concentration. Finally, a top view of the robots with respect to the light distribution and the estimated density function are shown. This experiment verifies the behavior of the adaptive decentralized nonholonomic sensor network controller.