Towards Autonomous Robotic Precision Harvesting: Mapping, Localization,\n Planning and Control for a Legged Tree Harvester

摘要

This paper presents an integrated system for performing precision harvesting\nmissions using a legged harvester. Our harvester performs a challenging task of\nautonomous navigation and tree grabbing in a confined, GPS denied forest\nenvironment. Strategies for mapping, localization, planning, and control are\nproposed and integrated into a fully autonomous system. The mission starts with\na human mapping the area of interest using a custom-made sensor module.\nSubsequently, a human expert selects the trees for harvesting. The sensor\nmodule is then mounted on the machine and used for localization within the\ngiven map. A planning algorithm searches for both an approach pose and a path\nin a single path planning problem. We design a path following controller\nleveraging the legged harvester's capabilities for negotiating rough terrain.\nUpon reaching the approach pose, the machine grabs a tree with a\ngeneral-purpose gripper. This process repeats for all the trees selected by the\noperator. Our system has been tested on a testing field with tree trunks and in\na natural forest. To the best of our knowledge, this is the first time this\nlevel of autonomy has been shown on a full-size hydraulic machine operating in\na realistic environment.\n