3 research outputs found
Optimal Routing Schedules for Robots Operating in Aisle-Structures
In this paper, we consider the Constant-cost Orienteering Problem (COP) where
a robot, constrained by a limited travel budget, aims at selecting a path with
the largest reward in an aisle-graph. The aisle-graph consists of a set of
loosely connected rows where the robot can change lane only at either end, but
not in the middle. Even when considering this special type of graphs, the
orienteering problem is known to be NP-hard. We optimally solve in polynomial
time two special cases, COP-FR where the robot can only traverse full rows, and
COP-SC where the robot can access the rows only from one side. To solve the
general COP, we then apply our special case algorithms as well as a new
heuristic that suitably combines them. Despite its light computational
complexity and being confined into a very limited class of paths, the optimal
solutions for COP-FR turn out to be competitive even for COP in both real and
synthetic scenarios. Furthermore, our new heuristic for the general case
outperforms state-of-art algorithms, especially for input with highly
unbalanced rewards
Task Planning on Stochastic Aisle Graphs for Precision Agriculture
This work addresses task planning under uncertainty for precision agriculture
applications whereby task costs are uncertain and the gain of completing a task
is proportional to resource consumption (such as water consumption in precision
irrigation). The goal is to complete all tasks while prioritizing those that
are more urgent, and subject to diverse budget thresholds and stochastic costs
for tasks. To describe agriculture-related environments that incorporate
stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph
(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action
Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled
by SAG, and tackles the task planning problem by simultaneously determining the
optimal tasks to perform and an optimal time to exit (i.e. return to a base
station), at run-time. The proposed approach is tested with both simulated data
and real-world experimental datasets collected in a commercial vineyard, in
both single- and multi-robot scenarios. In all cases, NBA-P outperforms other
evaluated methods in terms of return per visited vertex, wasted resources
resulting from aborted tasks (i.e. when a budget threshold is exceeded), and
total visited vertices.Comment: To appear in Robotics and Automation Letter
Robot-assisted Soil Apparent Electrical Conductivity Measurements in Orchards
Soil apparent electrical conductivity (ECa) is a vital metric in Precision
Agriculture and Smart Farming, as it is used for optimal water content
management, geological mapping, and yield prediction. Several existing methods
seeking to estimate soil electrical conductivity are available, including
physical soil sampling, ground sensor installation and monitoring, and the use
of sensors that can obtain proximal ECa estimates. However, such methods can be
either very laborious and/or too costly for practical use over larger field
canopies. Robot-assisted ECa measurements, in contrast, may offer a scalable
and cost-effective solution. In this work, we present one such solution that
involves a ground mobile robot equipped with a customized and adjustable
platform to hold an Electromagnetic Induction (EMI) sensor to perform
semi-autonomous and on-demand ECa measurements under various field conditions.
The platform is designed to be easily re-configurable in terms of sensor
placement; results from testing for traversability and robot-to-sensor
interference across multiple case studies help establish appropriate tradeoffs
for sensor placement. Further, a developed simulation software package enables
rapid and accessible estimation of terrain traversability in relation to
desired EMI sensor placement. Extensive experimental evaluation across
different fields demonstrates that the obtained robot-assisted ECa measurements
are of high linearity compared with the ground truth (data collected manually
by a handheld EMI sensor) by scoring more than in Pearson correlation
coefficient in both plot measurements and estimated ECa maps generated by
kriging interpolation. The proposed robotic solution supports autonomous
behavior development in the field since it utilizes the ROS navigation stack
along with the RTK GNSS positioning data and features various ranging sensors.Comment: 15 pages, 16 figure