scholarly journals Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

2020 ◽  
Vol 10 (2) ◽  
pp. 2164
Author(s):  
Fredy Martinez ◽  
Edwar Jacinto ◽  
Fernando Martinez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Time Estimation ◽  
Obstacle Detection ◽  
Stereoscopic Vision ◽  
Human Interaction ◽  
Human Model ◽  
Service Robots ◽  
Indoor Environments ◽  
Time Operation

This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.

scholarly journals Using bacterial interaction and stereoscopic images for the location of obstacles on autonomous robots

2020 ◽  
Vol 9 (3) ◽  
pp. 906-913
Author(s):  
Fredy Martinez ◽  
Edwar Jacinto ◽  
Fernando Martínez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Computational Cost ◽  
Cost Effective ◽  
Service Robots ◽  
Depth Information ◽  
Human Beings ◽  
Distance Information ◽  
Time Operation ◽  
Bacterial Interaction

Service robots are characterized by autonomously performing indoor tasks in unstructured environments, this condition of the environment prevents the prior programming of the map, which requires reactive behavior. These robots require real-time and cost-effective identification of obstacles in the environment, which includes not only distance information, but also depth information. This paper shows a strategy to estimate the position of obstacles in unknown environments. This strategy is characterized by low computational cost and real-time operation. The environments are selected because they are those usual to human beings, and this also influences our design, since we look for functional and morphological equivalence with human beings. This equivalence corresponds to the installation of two cameras in our robotic platform to form a stereoscopic system equivalent to the human. The images captured simultaneously are analyzed by a bacterial interaction scheme to define points on the obstacles. Our strategy showed a high performance in controlled environments. The scheme was able to establish distances to different points of the obstacle with 95% accuracy for distances between 0.8 and 2 m.

Real-Time Operation and Characterization of a High-Performance Time-Based Accelerometer

2015 ◽  
Vol 24 (6) ◽  
pp. 1703-1711 ◽  
Author(s):  
Rosana Alves Dias ◽  
Filipe Serra Alves ◽  
Margaret Costa ◽  
Helder Fonseca ◽  
Jorge Cabral ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Performance Time ◽  
Time Operation ◽  
Real Time Operation

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.

scholarly journals A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

2020 ◽  
Vol 11 (4) ◽  
pp. 66
Author(s):  
Nassim Noura ◽  
Loïc Boulon ◽  
Samir Jemeï
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Performance ◽  
Health Management ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Methods ◽  
State Of Health ◽  
Hybrid Electric

To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

scholarly journals On the Use of Probabilistic Worst-Case Execution Time Estimation for Parallel Applications in High Performance Systems

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 314 ◽  
Author(s):  
Matteo Fusi ◽  
Fabio Mazzocchetti ◽  
Albert Farres ◽  
Leonidas Kosmidis ◽  
Ramon Canal ◽  
...  
Keyword(s):  
Real Time ◽  
Execution Time ◽  
High Performance ◽  
Effective Means ◽  
Time Estimation ◽  
Value Theory ◽  
Parallel Applications ◽  
Worst Case ◽  
Worst Case Execution Time ◽  
Execution Times

Some high performance computing (HPC) applications exhibit increasing real-time requirements, which call for effective means to predict their high execution times distribution. This is a new challenge for HPC applications but a well-known problem for real-time embedded applications where solutions already exist, although they target low-performance systems running single-threaded applications. In this paper, we show how some performance validation and measurement-based practices for real-time execution time prediction can be leveraged in the context of HPC applications on high-performance platforms, thus enabling reliable means to obtain real-time guarantees for those applications. In particular, the proposed methodology uses coordinately techniques that randomly explore potential timing behavior of the application together with Extreme Value Theory (EVT) to predict rare (and high) execution times to, eventually, derive probabilistic Worst-Case Execution Time (pWCET) curves. We demonstrate the effectiveness of this approach for an acoustic wave inversion application used for geophysical exploration.

scholarly journals Real Time Mapping and Dynamic Navigation for Mobile Robots

10.5772/5681 ◽  
2007 ◽  
Vol 4 (3) ◽  
pp. 35 ◽  
Author(s):  
Maki K. Habib
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Sensory Information ◽  
Obstacle Detection ◽  
Mapping Technique ◽  
Navigation Systems ◽  
Map Building ◽  
Time Operation ◽  
Dynamic Navigation

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.

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