ALPINE: a climbing robot for operations in mountain environments
Michele Focchi, Andrea Del Prete, Daniele Fontanelli, Marco Frego, Angelika Peer, Luigi Palopoli
TL;DR
ALPINE addresses the challenge of autonomous maintenance on steep mountain walls by presenting a dual-rope tethered climbing robot with a retractable leg and an auxiliary propeller. The authors develop a tractable reduced-order model for planning and a static wrench feasibility analysis to ensure safe wall contact and operation under friction and unilateral constraints. A two-tier control framework (offline nonlinear planning plus online MPC) enables multi-jump trajectories, disturbance rejection, obstacle avoidance, and precise landing, validated through extensive Gazebo simulations. The platform demonstrates strong potential for rapid deployment, payload handling, and autonomous maintenance tasks in harsh mountain environments, with comparative energy advantages over aerial and walking robots.
Abstract
Mountain slopes are perfect examples of harsh environments in which humans are required to perform difficult and dangerous operations such as removing unstable boulders, dangerous vegetation or deploying safety nets. A good replacement for human intervention can be offered by climbing robots. The different solutions existing in the literature are not up to the task for the difficulty of the requirements (navigation, heavy payloads, flexibility in the execution of the tasks). In this paper, we propose a robotic platform that can fill this gap. Our solution is based on a robot that hangs on ropes, and uses a retractable leg to jump away from the mountain walls. Our package of mechanical solutions, along with the algorithms developed for motion planning and control, delivers swift navigation on irregular and steep slopes, the possibility to overcome or travel around significant natural barriers, and the ability to carry heavy payloads and execute complex tasks. In the paper, we give a full account of our main design and algorithmic choices and show the feasibility of the solution through a large number of physically simulated scenarios.
