Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester

Abstract

This paper presents a system for autonomously conducting precision harvesting missions using a legged harvester. Precision tree harvesting removes some trees selectively, while leaving neighboring trees intact. Our robot performs the challenging task of navigation and tree grabbing in a confined, GPS-denied forest environment. We propose strategies for mapping, localization, planning, and control and integrate them into a fully autonomous system. The mission starts with a human mapping the area of interest using a detachable, custom sensor module. Subsequently, a human expert selects the specific trees for harvesting. The sensor module is then mounted on the machine and used for localization within the created map. A planning algorithm searches for both an approach-pose and a path in a single path planning problem. We design a path-following controller exploiting the legged harvester’s capabilities for negotiating rough terrain. Upon reaching the approach-pose, the machine grabs a tree with a general-purpose gripper. Our system has been tested in both emulated and natural forest settings. To the best of our knowledge, ours is the first robot to demonstrate such a level of autonomy on a full-size, hydraulic machine operating in a realistic environment.