scholarly journals Real-time Trajectory Planning for Automated Vehicle Safety and Performance in Dynamic Environments

2021 ◽  
pp. 1-22
Author(s):  
Huckleberry Febbo ◽  
Paramsothy Jayakumar ◽  
Jeffrey L. Stein ◽  
Tulga Ersal
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
High Speed ◽  
High Performance ◽  
Simultaneous Optimization ◽  
Problem Solver ◽  
Automated Vehicles ◽  
Control Effort ◽  
Moving Obstacle

Abstract Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. The ability to solve this formulation in real-time is evaluated using NLOptControl, an open-source, direct-collocation based, optimal control problem solver in conjunction with the KNITRO nonlinear programming problem solver. The formulation is tested with various sets of the specifications. A parametric study relating execution horizon and obstacle speed indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon and the obstacles are moving slowly. However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety without, in most cases, increasing the solve-times. The results indicate that (i) safe trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above, unless a static or low-speed environment permits a less comprehensive planner; and (ii) the resulting formulation can be solved in real-time.

scholarly journals Monitoring and characterization of vibration and shock conditions aboard high-performance marine craft

2018 ◽  
Vol 233 (4) ◽  
pp. 1068-1081
Author(s):  
Manudul Pahansen de Alwis ◽  
Karl Garme
Keyword(s):  
Real Time ◽  
High Speed ◽  
High Performance ◽  
Severity Index ◽  
Irregular Waves ◽  
Vibration Exposure ◽  
Data Set ◽  
Real Time Analysis ◽  
Impact Acceleration ◽  
The Impact

The stochastic environmental conditions together with craft design and operational characteristics make it difficult to predict the vibration environments aboard high-performance marine craft, particularly the risk of impact acceleration events and the shock component of the exposure often being associated with structural failure and human injuries. The different timescales and the magnitudes involved complicate the real-time analysis of vibration and shock conditions aboard these craft. The article introduces a new measure, severity index, indicating the risk of severe impact acceleration, and proposes a method for real-time feedback on the severity of impact exposure together with accumulated vibration exposure. The method analyzes the immediate 60 s of vibration exposure history and computes the severity of impact exposure as for the present state based on severity index. The severity index probes the characteristic of the present acceleration stochastic process, that is, the risk of an upcoming heavy impact, and serves as an alert to the crew. The accumulated vibration exposure, important for mapping and logging the crew exposure, is determined by the ISO 2631:1997 vibration dose value. The severity due to the impact and accumulated vibration exposure is communicated to the crew every second as a color-coded indicator: green, yellow and red, representing low, medium and high, based on defined impact and dose limits. The severity index and feedback method are developed and validated by a data set of 27 three-hour simulations of a planning craft in irregular waves and verified for its feasibility in real-world applications by full-scale acceleration data recorded aboard high-speed planing craft in operation.

scholarly journals High-Speed, High-Performance Real-Time Imaging of Physiological Sarcomere Dynamics in the Beating Mouse Heart in Vivo

2016 ◽  
Vol 110 (3) ◽  
pp. 463a
Author(s):  
Fuyu Kobirumaki-Shimozawa ◽  
Kotaro Oyama ◽  
Togo Shimozawa ◽  
Takashi Ohki ◽  
Takako Terui ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
High Performance ◽  
Mouse Heart ◽  
Real Time Imaging ◽  
Sarcomere Dynamics

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

2019 ◽  
Vol 16 (8) ◽  
pp. 3419-3427
Author(s):  
Shishir K. Shandilya ◽  
S. Sountharrajan ◽  
Smita Shandilya ◽  
E. Suganya
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Big Data Analytics ◽  
Good Precision ◽  
Entire Genome ◽  
Big Data Technologies ◽  
Sequencing Platforms

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

ODE-based obstacle avoidance and trajectory planning for unmanned surface vessels

Robotica ◽  
2010 ◽  
Vol 29 (5) ◽  
pp. 691-703 ◽  
Author(s):  
Reza A. Soltan ◽  
Hashem Ashrafiuon ◽  
Kenneth R. Muske
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Sliding Mode ◽  
Control Law ◽  
Target Trajectory ◽  
Surface Vessels ◽  
Modeling Uncertainties

SUMMARYA new method for real-time obstacle avoidance and trajectory planning of underactuated unmanned surface vessels is presented. In this method, ordinary differential equations (ODEs) are used to define transitional trajectories that can avoid obstacles and reach a final desired target trajectory using a robust tracking control law. The obstacles are approximated and enclosed by elliptical shapes. A transitional trajectory is then defined by a set of ordinary differential equations whose solution is a stable elliptical limit cycle defining the nearest obstacle on the vessel's path to the target. When no obstacle blocks the vessel's path to its target, the transitional trajectory is defined by exponentially stable ODE whose solution is the target trajectory. The planned trajectories are tracked by the vessel through a sliding mode control law that is robust to environmental disturbances and modeling uncertainties and can be computed in real time. The method is illustrated using a complex simulation example with a moving target and multiple moving and rotating obstacles and a simpler experimental example with stationary obstacles.

scholarly journals Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Pablo Martinez ◽  
Hovagim Bakardjian ◽  
Andrzej Cichocki
Keyword(s):  
Real Time ◽  
Healthy Subjects ◽  
High Speed ◽  
High Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Optimal Frequency ◽  
Computer Screen ◽  
Near Future ◽  
Bci System

We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

Computationally Efficient Trajectory Planning for High Speed Obstacle Avoidance of a Quadrotor With Active Sensing

2021 ◽  
Vol 6 (2) ◽  
pp. 3365-3372
Author(s):  
Gang Chen ◽  
Dongxiao Sun ◽  
Wei Dong ◽  
Xinjun Sheng ◽  
Xiangyang Zhu ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
High Speed ◽  
Active Sensing ◽  
Computationally Efficient

scholarly journals Processor-in-the-Loop Architecture Design and Experimental Validation for an Autonomous Racing Vehicle

2021 ◽  
Vol 11 (16) ◽  
pp. 7225
Author(s):  
Eugenio Tramacere ◽  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Real Time ◽  
Personal Computer ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Experimental Validation ◽  
Low Cost ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
System Level

Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation.

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