Reinforcement Learning Strategy for Automatic Control of Real-time Obstacle Avoidance based on Vehicle Dynamics

2017 ◽  
Vol 12 (3) ◽  
pp. 297-305 ◽  
Author(s):  
Dong-Hoon Kang ◽  
◽  
Jae Hwan Bong ◽  
Jooyoung Park ◽  
Shinsuk Park
Keyword(s):  
Reinforcement Learning ◽  
Automatic Control ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Learning Strategy

Safe reinforcement learning for real-time automatic control in a smart energy-hub

2022 ◽  
Vol 309 ◽  
pp. 118403
Author(s):  
Dawei Qiu ◽  
Zihang Dong ◽  
Xi Zhang ◽  
Yi Wang ◽  
Goran Strbac
Keyword(s):  
Reinforcement Learning ◽  
Automatic Control ◽  
Real Time ◽  
Energy Hub

scholarly journals Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Minghui Wang ◽  
Bi Zeng ◽  
Qiujie Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Learning Strategy ◽  
Planning Method ◽  
Unknown Environment ◽  
Robot Systems ◽  
Avoidance Control ◽  
Multi Robot

Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

scholarly journals “Real-time” obstacle avoidance in the absence of primary visual cortex

2009 ◽  
Vol 106 (37) ◽  
pp. 15996-16001 ◽  
Author(s):  
Christopher L. Striemer ◽  
Craig S. Chapman ◽  
Melvyn A. Goodale
Keyword(s):  
Visual Cortex ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Primary Visual Cortex ◽  
Posterior Parietal Cortex ◽  
Dorsal Stream ◽  
Visual Pathways ◽  
Visual Stream ◽  
Reaching Task ◽  
Visual Inputs

When we reach toward objects, we easily avoid potential obstacles located in the workspace. Previous studies suggest that obstacle avoidance relies on mechanisms in the dorsal visual stream in the posterior parietal cortex. One fundamental question that remains unanswered is where the visual inputs to these dorsal-stream mechanisms are coming from. Here, we provide compelling evidence that these mechanisms can operate in “real-time” without direct input from primary visual cortex (V1). In our first experiment, we used a reaching task to demonstrate that an individual with a dense left visual field hemianopia after damage to V1 remained strikingly sensitive to the position of unseen static obstacles placed in his blind field. Importantly, in a second experiment, we showed that his sensitivity to the same obstacles in his blind field was abolished when a short 2-s delay (without vision) was introduced before reach onset. These findings have far-reaching implications, not only for our understanding of the time constraints under which different visual pathways operate, but also in relation to how these seemingly “primitive” subcortical visual pathways can control complex everyday behavior without recourse to conscious vision.

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

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.

scholarly journals Real Time Path Planning of Robot using Deep Reinforcement Learning

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

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.

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