Task-Oriented Path Planning Towards Autonomous Sensor Network Deployment in Rainforest Canopies

Climate change is placing increasing pressure on rainforests and their ecosystems, creating an urgent need for technologies that enable large-scale biodiversity monitoring and conservation. In this work, we present an exploration path planner for the autonomous deployment of wireless sensor networks in rainforest canopies using a quadrotor platform. To address the challenge of placing sensors across large areas while constrained by the limited flight endurance of multirotor drones, our approach begins with a scouting mission that identifies feasible canopy locations for sensor placement while accounting for constraints such as communication range and network connectivity. In addition, we incorporate forest canopy morphological constraints by detecting flat, vegetation-covered areas that can support sensor nodes. Our informative exploration planner combines online detection of suitable canopy surfaces with pointcloud projection and a targeted sampling strategy to guide the drone towards candidate deployment sites. The planner relies on a heuristic gain function based on vegetation area, path length, and smoothness, rather than an explicit optimization of communication connectivity or coverage. Therefore, the proposed method should be interpreted as a task-oriented exploration strategy that identifies promising deployment regions, rather than a complete solution to the sensor network placement problem. We validate the approach through flight experiments conducted in environments designed to replicate key characteristics of rainforest canopies. The results demonstrate that the proposed method can identify viable candidate deployment locations within complex vegetation structures, providing a foundation for future work on fully integrated autonomous biodiversity monitoring missions.