TAPE algorithm lets tethered drones explore 3D cavities without tangling
First method to autonomously explore unknown cavities while preventing tether snags, tested in field.
Louis Petit and Alexis Lussier Desbiens from the Université de Sherbrooke present TAPE (Tether-Aware Path Planning for Autonomous Exploration), the first method designed specifically for tethered aerial robots to explore unknown 3D cavities while preventing tether entanglement. Traditional exploration planners ignore tether constraints, leading to snags or limited reach. TAPE uses a hierarchical approach: a global planner solves a Traveling Salesman Problem to minimize total path length across frontier points, while a local planner dynamically adjusts the robot's trajectory to keep the tether free, weighing path cost against tether length via an adjustable decision function.
Simulation results show TAPE achieves only a 4.1% increase in distance traveled compared to an optimal TSP solution without tether constraints, yet it guarantees tether length remains under the maximum allowed in 100% of cases—versus just 53% for the baseline. The method also passed field tests, demonstrating real-world applicability for inspecting caves, mines, infrastructure voids, and disaster rubble. Published in IEEE Robotics and Automation Letters, TAPE opens the door to autonomous, entanglement-free exploration in confined subterranean environments.
- Two-level hierarchical planner: global TSP for distance minimization, local planner using adjustable tether cost function.
- Achieves 100% tether constraint satisfaction (vs 53% baseline) with only 4.1% extra distance on average.
- Validated in both simulation and field tests for real 3D cavity exploration (caves, tunnels, rubble).
Why It Matters
Enables tethered drones to autonomously map caves and disaster zones without tether tangles, improving safety and range.