scholarly journals FFAU—Framework for Fully Autonomous UAVs

2020 ◽  
Vol 12 (21) ◽  
pp. 3533
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
Fábio Azevedo ◽  
Ricardo Sacoto-Martins ◽  
Luís Bernardo ◽  
...  
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
New Technology ◽  
Experimental Tests ◽  
Urban Environments ◽  
Novel Approach ◽  
Safety Risks ◽  
Stationary Objects ◽  
Safety And Reliability ◽  
Remote Clients

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.

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 A Kinematic Model to Compensate the Structural Deformations in Machine Tools Using Fibre Bragg Grating (FBG) Sensors

2016 ◽  
Author(s):  
Francesco Aggogeri ◽  
Alberto Borboni ◽  
Rodolfo Faglia ◽  
Angelo Merlo ◽  
Nicola Pellegrini
Keyword(s):  
Machine Tool ◽  
Real Time ◽  
Kinematic Model ◽  
Experimental Tests ◽  
Bragg Grating ◽  
Fibre Bragg Grating ◽  
Machining Performance ◽  
Novel Approach ◽  
Structural Deformations ◽  
Fbg Sensors

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

2021 ◽  
Author(s):  
Brij B. Gupta ◽  
Krishna Yadav ◽  
Imran Razzak ◽  
Konstantinos Psannis ◽  
Arcangelo Castiglione ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Novel Approach

scholarly journals A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models

2021 ◽  
Author(s):  
Rakesh Kumar ◽  
Gaurav Dhiman ◽  
Neeraj Kumar ◽  
Rajesh Kumar Chandrawat ◽  
Varun Joshi ◽  
...  
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Production Cost ◽  
Upper Bounds ◽  
Production Costs ◽  
Cost Data ◽  
Novel Approach ◽  
Membership Grade ◽  
Minimum Costs

AbstractThis article offers a comparative study of maximizing and modelling production costs by means of composite triangular fuzzy and trapezoidal FLPP. It also outlines five different scenarios of instability and has developed realistic models to minimize production costs. Herein, the first attempt is made to examine the credibility of optimized cost via two different composite FLP models, and the results were compared with its extension, i.e., the trapezoidal FLP model. To validate the models with real-time phenomena, the Production cost data of Rail Coach Factory (RCF) Kapurthala has been taken. The lower, static, and upper bounds have been computed for each situation, and then systems of optimized FLP are constructed. The credibility of each model of composite-triangular and trapezoidal FLP concerning all situations has been obtained, and using this membership grade, the minimum and the greatest minimum costs have been illustrated. The performance of each composite-triangular FLP model was compared to trapezoidal FLP models, and the intense effects of trapezoidal on composite fuzzy LPP models are investigated.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

Sign in / Sign up

Export Citation Format

Share Document