Prioritized Sensor Detection for Environmental Mapping: Theory and Experiments
This paper presents a decentralized coordination algorithm that allows a team of sensor-enabled robots to navigate a region containing non-convex obstacles and take measurements within the region that contain the highest probability of having "good" information first. This approach is motivated by scenarios where prior knowledge of the search space is known or when time constraints are present that limit the amount of area that can be searched by a robot team. Our cooperative control algorithm combines Voronoi partitioning, a global optimization technique, and a modified navigation function to prioritize sensor detection. Also, we present a technique for fusing multi-sensing objectives which is accomplished through linear regression. Practical applications include search and rescue, target detection, and hazardous contaminations. The issues we address such as non-convex obstacles as well as global search are not extensively addressed in the current literature. Simulation and experimental results of the control algorithm are given, and validate the prioritized sensing behavior as well as the collision avoidance property.
cooperative control sensor networks motion planning environmental sensing