Environment Exploration in Sensing Automation for Habitat Monitoring
We present algorithms for environment exploration in the context of a habitat monitoring task, where the goal is to track radio-tagged invasive fish with autonomous surface or ground robots. The first task is navigation around an unknown obstacle using an input from a front-facing sonar. This capability is important for navigation on inland lakes, because plants and shallow shorelines are hard to map in advance. The second task involves energy harvesting for long-term operation. We address the problem of exploring the solar map of the environment which is used for energy-efficient navigation. For both problems, we present online algorithms and examine their performance using competitive analysis. In competitive analysis, the performance of an online algorithm is compared against the optimal offline algorithm. For obstacle avoidance, the offline algorithm knows the shape of the obstacle. For solar exploration, the offline algorithm knows the geometry of the shadow-casting objects. We obtain an $O(1)$ competitive ratio for obstacle avoidance and an $O(\log n)$ competitive ratio for solar exploration, where $n$ is the number of critical points to observe. The strategies for obstacle avoidance are validated through extensive field experiments, and the strategies for exploration are validated with simulations.