• Media type: E-Article
  • Title: K-Means Partitioned Space Path Planning (KPSPP) for Autonomous Robotic Harvesting
  • Contributor: Meaclem, Christopher Vincent; Chen, XiaoQi; Gutschmidt, Stefanie; Hann, Chris; Parker, Richard
  • imprint: SAGE Publications, 2015
  • Published in: International Journal of Advanced Robotic Systems
  • Language: English
  • DOI: 10.5772/61816
  • ISSN: 1729-8814
  • Keywords: Artificial Intelligence ; Computer Science Applications ; Software
  • Origination:
  • Footnote:
  • Description: <jats:p> A three-dimensional coverage path-planning algorithm is proposed for discrete harvesting machines. Although prior research has developed methods for coverage planning in continuous-crop fields, no such algorithm has been developed for discrete crops such as trees. The problem is formulated as a graph traversal problem and solved using graph techniques. Paths to facilitate autonomous operation are generated. A case study is formed around the novel tree-to-tree felling system developed by the University of Canterbury and Scion. This machine is being developed to manoeuvre through New Zealand's plantation forest to fell Pinus radiata trees on steep (≤ 45°) terrain. Algorithm performance is evaluated in 14 commercial plantation forests. Results indicate that a mean coverage of 84.43% was achieved. </jats:p>
  • Access State: Open Access