1,091 research outputs found
An Effective Multi-Cue Positioning System for Agricultural Robotics
The self-localization capability is a crucial component for Unmanned Ground
Vehicles (UGV) in farming applications. Approaches based solely on visual cues
or on low-cost GPS are easily prone to fail in such scenarios. In this paper,
we present a robust and accurate 3D global pose estimation framework, designed
to take full advantage of heterogeneous sensory data. By modeling the pose
estimation problem as a pose graph optimization, our approach simultaneously
mitigates the cumulative drift introduced by motion estimation systems (wheel
odometry, visual odometry, ...), and the noise introduced by raw GPS readings.
Along with a suitable motion model, our system also integrates two additional
types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random
Field assumption. We demonstrate how using these additional cues substantially
reduces the error along the altitude axis and, moreover, how this benefit
spreads to the other components of the state. We report exhaustive experiments
combining several sensor setups, showing accuracy improvements ranging from 37%
to 76% with respect to the exclusive use of a GPS sensor. We show that our
approach provides accurate results even if the GPS unexpectedly changes
positioning mode. The code of our system along with the acquired datasets are
released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters,
201
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
The application of autonomous robots in agriculture is gaining increasing
popularity thanks to the high impact it may have on food security,
sustainability, resource use efficiency, reduction of chemical treatments, and
the optimization of human effort and yield. With this vision, the Flourish
research project aimed to develop an adaptable robotic solution for precision
farming that combines the aerial survey capabilities of small autonomous
unmanned aerial vehicles (UAVs) with targeted intervention performed by
multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview
of the scientific and technological advances and outcomes obtained in the
project. We introduce multi-spectral perception algorithms and aerial and
ground-based systems developed for monitoring crop density, weed pressure, crop
nitrogen nutrition status, and to accurately classify and locate weeds. We then
introduce the navigation and mapping systems tailored to our robots in the
agricultural environment, as well as the modules for collaborative mapping. We
finally present the ground intervention hardware, software solutions, and
interfaces we implemented and tested in different field conditions and with
different crops. We describe a real use case in which a UAV collaborates with a
UGV to monitor the field and to perform selective spraying without human
intervention.Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp.
29-49, Sept. 202
Accurate Crop Spraying with RTK and Machine Learning on an Autonomous Field Robot
The agriculture sector requires a lot of labor and resources. Hence, farmers
are constantly being pressed for technology and automation to be
cost-effective. In this context, autonomous robots can play a very important
role in carrying out agricultural tasks such as spraying, sowing, inspection,
and even harvesting. This paper presents one such autonomous robot that is able
to identify plants and spray agro-chemicals precisely. The robot uses machine
vision technologies to find plants and RTK-GPS technology to navigate the robot
along a predetermined path. The experiments were conducted in a field of potted
plants in which successful results have been obtained.Comment: 7 pages, 12 figures, Journa
Determining position and orientation of a 3-wheel robot on a pipe using an accelerometer
Accurate positioning of robots on pipes is a challenge in automated industrial inspection. It is typically achieved using expensive and cumbersome external measurement equipment. This paper presents an Inverse Model method for determining the orientation angle (α ) and circumferential position angle (ω) of a 3 point of contact robot on a pipe where measurements are taken from a 3-axis accelerometer sensor. The advantage of this system is that it provides absolute positional measurements using only a robot mounted sensor. Two methods are presented which follow an analytical approximation to correct the estimated values. First, a correction factor found though a parametric study between the robot geometry and a given pipe radius, followed by an optimization solution which calculates the desired angles based on the system configuration, robot geometry and the output of a 3-axis accelerometer. The method is experimentally validated using photogrammetry measurements from a Vicon T160 positioning system to record the position of a three point of contact test rig in relation to a test pipe in a global reference frame. An accelerometer is attached to the 3 point of contact test rig which is placed at different orientation (α ) and circumferential position (ω) angles. This work uses a new method of processing data from an accelerometer sensor to obtain the α and ω angles. The experimental results show a maximum error of 3.40° in α and 4.17° in ω , where the ω circumferential positional error corresponds to ±18mm for the test pipe radius of 253mm
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Mitigating ground effect on mini quadcopters with model reference adaptive control
Mitigating ground effect becomes a big challenge for autonomous aerial vehicles when they are flying in close proximity to the ground. This paper aims to develop a precise model of ground effect on mini quadcopters, provide an advanced control algorithm to counter the model uncertainty and, as a result, improves the command tracking performance when the vehicle is in the ground effect region. The mathematical model of ground effect has been established through a series of experiments and validated by a flight test. The experiments show that the total thrust generated by rotors increases linearly as the vehicle gets closer to the ground, which is different from the commonly-used ground effect model for a single rotor vehicle. In addition, the model switches from a piecewise linear to a quadratic function when the rotor to rotor distance is increased. A control architecture that utilizes the model reference adaptive controller (MRAC) has also been designed, where MRAC is added to the altitude loop. The performance of the proposed control algorithm has been evaluated through a set of flight tests on a mini quadcopter platform and compared with a traditional proportional–integral–derivative (PID) controller. The results demonstrate that MRAC dramatically improves the tracking performance of altitude command and can reduce the rise time by 80 % under the ground effect
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