RobotDrlSim: A Real Time Robot Simulation Platform for Reinforcement Learning and Human Interactive Demonstration Learning

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

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Real Time Path Planning of Robot using Deep Reinforcement Learning

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2494 ◽

2020 ◽

Vol 53 (2) ◽

pp. 15602-15607

Author(s):

Jeevan Raajan ◽

P V Srihari ◽

Jayadev P Satya ◽

B Bhikkaji ◽

Ramkrishna Pasumarthy

Keyword(s):

Reinforcement Learning ◽

Path Planning ◽

Real Time ◽

Time Path

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The establishment of real time simulation platform of AC / DC system based on power electronic transformer

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/675/1/012069 ◽

2021 ◽

Vol 675 (1) ◽

pp. 012069

Author(s):

Xing Zhang ◽

Yang Li ◽

Guoqing He

Keyword(s):

Real Time ◽

Simulation Platform ◽

Power Electronic ◽

Real Time Simulation ◽

Dc System ◽

Electronic Transformer ◽

Power Electronic Transformer ◽

Time Simulation

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

Sensors ◽

10.3390/s21072534 ◽

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.

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Reinforcement Learning Based Framework for Real Time Fault Tolerance

2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) ◽

10.1109/iemcon51383.2020.9284929 ◽

2020 ◽

Author(s):

Yehia Kotb ◽

Mouhammad Alakkoummi ◽

Hassan Kanj

Keyword(s):

Reinforcement Learning ◽

Fault Tolerance ◽

Real Time

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Design and implementation of a virtual ARINC 653 simulation platform

SIMULATION ◽

10.1177/0037549721996018 ◽

2021 ◽

pp. 003754972199601

Author(s):

Jinchao Chen ◽

Keke Chen ◽

Chenglie Du ◽

Yifan Liu

Keyword(s):

Real Time ◽

System Development ◽

General Purpose ◽

Simulation Platform ◽

Communication Management ◽

Management Communication ◽

Real Time Simulation ◽

Testing And Debugging ◽

Time Simulation ◽

Efficiency And Effectiveness

The ARINC 653 operation system is currently widely adopted in the avionics industry, and has become the mainstream architecture in avionics applications because of its strong agility and reliability. Although ARINC 653 can efficiently reduce the weight and energy consumption, it results in a serious development and verification problem for avionics systems. As ARINC 653 is non-open source software and lacks effective support for software testing and debugging, it is of great significance to build a real-time simulation platform for ARINC 653 on general-purpose operating systems, improving the efficiency and effectiveness of system development and implementation. In this paper, a virtual ARINC 653 platform is designed and realized by using real-time simulation technology. The proposed platform is composed of partition management, communication management, and health monitoring management, provides the same operation interfaces as the ARINC 653 system, and allows dynamic debugging of avionics applications without requiring the actual presence of real devices. Experimental results show that the platform not only simulates the functionalities of ARINC 653, but also meets the real-time requirements of avionics applications.

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