Proving that robotic forest harvesting is feasible and efficient using satellite positioning

What is it about?

In agriculture and forestry, errors in position and directional information provided by high accuracy global navigation satellite systems (GNSS) lead directly to economic, productivity and environmental losses. The paper focuses on GNSS inaccuracies in the context of kinematic robotic forest harvester operations once a tree has been located. This represents a significant challenge as the geographic location of individual points using GNSS receivers largely requires an unobstructed line of sight from the points to a minimum number of satellites. This is often difficult to achieve in forest environments, as trunks, foliage and humidity can attenuate the GNSS signals besides multipath effects. It is shown by measurements and modeling that the harvesting process trajectories mandate high priority to be given to 3-D location and directional information. Extensive measurements of GNSS inaccuracies on radial grids around a tree position inside forests, some of the very first, have been carried out with the assistance of the Polish Forest Administration Authority. Data acquisition took place in three different forest environments in Poland, characterized by differences in tree heights, species, terrain slope, and rain/humidity levels. Combined with a directional volumetric tree trunk and canopy harvesting model, the data collected allow estimating the yield and productivity direct effects of GNSS errors on precision forestry equipment. It is shown that acceptable yields on trunk wood and logs can be achieved in some types of forest with few robotic harvester moves, in that to some extent the GNSS errors can be compensated by the telescopic arm flexibility of the harvester. The additional use of assistive lidars and other sensors, not considered here, strengthen these conclusions.

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Why is it important?

•Location, range and directional GNSS derived information are degraded in forests; GNSS positioning in some categories of forests as identified in this paper may be sufficiently accurate for automatic forest harvesting, except under rain conditions. •Even assuming a very simple stem-to-log cutting algorithm, and no stem height sensor, recovered trunk volume is good enough, contrary to recovered canopy volume, to justify robotic harvesting, especially in view of increased time based productivity. Issues remain around the exploitation and pricing from logs with higher variability. The addition of different robotic harvester-born sensors, on purpose not taken into account in this study (e.g. lidars, real time imaging opacity measures), will strengthen these conclusions. It is also to be noted that if GNSS is the fall-back navigation system in case of the likely frequent failures of optoelectronic sensors on high vibration harvesters, the above conclusions stand.