scholarly journals Parking Pose Generation for Autonomous Freight Collection by Pallet Handling Car-like Robot

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4677
Author(s):  
Khayyam Masood ◽  
David Pérez Morales ◽  
Vincent Fremont ◽  
Matteo Zoppi ◽  
Rezia Molfino
Keyword(s):  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Vehicle Navigation ◽  
Mathematical Equations ◽  
Autonomous Vehicle Navigation

This paper focuses on autonomous navigation for an electric freight vehicle designed to collect freight autonomously using pallet handling robots installed in the vehicle. Apart from autonomous vehicle navigation, the primary hurdle for vehicle autonomy is the autonomous collection of freight irrespective of freight orientation/location. This research focuses on generating parking pose for the vehicle irrespective of the orientation of freight for its autonomous collection. Freight orientation is calculated by capturing the freight through onboard sensors. Afterward, this information creates a parking pose using mathematical equations and knowledge of the vehicle and freight collection limitations. Separate parking spots are generated for separate loading bays of the vehicle depending on the availability of the loading bay. Finally, results are captured and verified for different orientations of freight to conclude the research.

scholarly journals Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1468
Author(s):  
Razin Bin Issa ◽  
Modhumonty Das ◽  
Md. Saferi Rahman ◽  
Monika Barua ◽  
Md. Khalilur Rhaman ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Vehicle Navigation ◽  
Autonomous Agent ◽  
Learning Approach ◽  
Q Learning ◽  
Land Vehicles ◽  
Autonomous Vehicle Navigation

Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

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