Location-Aware, Flexible Task Management for Collaborating Unmanned Autonomous Vehicles

2009 ◽  
Author(s):  
Meng Wang ◽  
Yang Zhao ◽  
Alex Doboli
Keyword(s):  
Autonomous Vehicles ◽  
Task Management ◽  
Location Aware ◽  
Unmanned Autonomous Vehicles

scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

scholarly journals Feasibility of Location-Aware Handover for Autonomous Vehicles in Industrial Multi-Radio Environments

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6290
Author(s):  
Yi Lu ◽  
Mikhail Gerasimenko ◽  
Roman Kovalchukov ◽  
Martin Stusek ◽  
Jani Urama ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Low Frequency ◽  
Dead Reckoning ◽  
Location Awareness ◽  
Received Signal Strength Indicator ◽  
Self Organized ◽  
Location Aware ◽  
Industrial Internet ◽  
Handover Scheme ◽  
Achievable Accuracy

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.

Recent advances in remote sensing technologies for hydrocarbon exploration and environmental evaluation

2019 ◽  
Vol 38 (7) ◽  
pp. 554-555
Author(s):  
Yongyi Li ◽  
Roice Nelson ◽  
William Jeffery ◽  
Douglas Foster ◽  
Dominique Dubucq ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Sustainability ◽  
Autonomous Vehicles ◽  
Petroleum Industry ◽  
Hydrocarbon Exploration ◽  
Active Sensors ◽  
Environmental Evaluation ◽  
Satellite Systems ◽  
Unmanned Autonomous Vehicles ◽  
Surface Geology

Remote sensing detects and monitors the physical and spatial characteristics of the earth's oceans, surface, and atmosphere by measuring the reflected or scattered downwelling or emitted upwelling electromagnetic radiation or acoustic signal using passive or active sensors at a distance. It plays an important role in today's energy and environmental sustainability efforts. Remote sensing from spaceborne, airborne, terrestrial, and marine platforms has long been used in hydrocarbon exploration to map surface geology, topography, and hydrocarbon seepages, as well as to evaluate environments that relate to petroleum industry activities. Since the mid-2000s, remote sensing technologies have undergone substantial advances in data acquisition, processing, and interpretation. In the last decade, rapid advances in satellite systems, unmanned autonomous vehicles (UAVs), sensors, and scale of surveys have further expanded applications.

scholarly journals Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2566
Author(s):  
Daniel H. Stolfi ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Dynamic Systems ◽  
Autonomous Vehicles ◽  
Water Surface ◽  
Mobility Model ◽  
Collaborative Approach ◽  
Ground Vehicles ◽  
Area Of Interest ◽  
Covered Area ◽  
Different Types ◽  
Unmanned Autonomous Vehicles

In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories based on chaotic solutions of dynamic systems. Collaborations between vehicles are meant to cover some regions of the area which are unreachable by members of one swarm, e.g., unmanned ground vehicles on water surface, by using members of another swarm, e.g., unmanned aerial vehicles. Experimental results demonstrate that collaboration is not only possible but also emerges as part of the configurations calculated by a specially designed and parameterised evolutionary algorithm. Experiments were conducted on 12 different case studies including 30 scenarios each, observing an improvement in the total covered area up to 11%, when comparing ABISS with a non-collaborative approach.

Guest Editorial: Communications Challenges and Dynamics for Unmanned Autonomous Vehicles

2012 ◽  
Vol 30 (5) ◽  
pp. 849-851 ◽  
Author(s):  
Gerard Parr ◽  
Steve Hailes ◽  
Jonathan P. How ◽  
Joe McGeehan ◽  
Y. Jay Guo
Keyword(s):  
Autonomous Vehicles ◽  
Guest Editorial ◽  
Unmanned Autonomous Vehicles

scholarly journals Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

2020 ◽  
Vol 12 (20) ◽  
pp. 8541
Author(s):  
Massinissa Graba ◽  
Sousso Kelouwani ◽  
Lotfi Zeghmi ◽  
Ali Amamou ◽  
Kodjo Agbossou ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Industry 4.0 ◽  
True Path ◽  
Long Term Effect ◽  
Industrial Operations ◽  
Single Vehicle ◽  
Energy Consideration ◽  
Source Level ◽  
Unmanned Autonomous Vehicles ◽  
The Impact

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.

Exploring the Performance of Natural Search Strategies for the Control of Unmanned Autonomous Vehicles

2009 ◽  
Vol 62 (2) ◽  
pp. 283-301 ◽  
Author(s):  
Alec Banks ◽  
Jonathan Vincent
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
A Priori ◽  
Natural World ◽  
Search Strategies ◽  
Target Presence ◽  
Performance Improvements ◽  
Species Specific ◽  
Multi Phase ◽  
Unmanned Autonomous Vehicles

This paper builds on prior research into the application of particle swarm optimisation to autonomous vehicle control in search roles. It examines the use of naturally inspired search strategies to enhance the performance of groups of sensor-based vehicles in applications where there is no knowledge a priori regarding target presence, location, distribution or behaviour (movement). This paper first briefly reviews existing ethological research into search strategies in the natural world, identifying three types of random walk, two multi-phase strategies and two species-specific strategies for further investigation. Experiments are then performed within a simulation environment to compare the performance of naturally inspired strategies with deterministic patterns and random movement, when searching for both static and dynamic targets. Results indicate that performance improvements can be realised, provided that critical relationships within the application domain broadly match those existing in the underlying natural metaphor.

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