6,774 research outputs found
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Distributed allocation of mobile sensing swarms in gyre flows
We address the synthesis of distributed control policies to enable a swarm of
homogeneous mobile sensors to maintain a desired spatial distribution in a
geophysical flow environment, or workspace. In this article, we assume the
mobile sensors (or robots) have a "map" of the environment denoting the
locations of the Lagrangian coherent structures or LCS boundaries. Based on
this information, we design agent-level hybrid control policies that leverage
the surrounding fluid dynamics and inherent environmental noise to enable the
team to maintain a desired distribution in the workspace. We establish the
stability properties of the ensemble dynamics of the distributed control
policies. Since realistic quasi-geostrophic ocean models predict double-gyre
flow solutions, we use a wind-driven multi-gyre flow model to verify the
feasibility of the proposed distributed control strategy and compare the
proposed control strategy with a baseline deterministic allocation strategy.
Lastly, we validate the control strategy using actual flow data obtained by our
coherent structure experimental testbed.Comment: 10 pages, 14 Figures, added reference
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
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