A Stereo Camera Based Static and Moving Obstacles Detection on Autonomous Visual Navigation of Indoor Transportation Vehicle

2018 ◽  
Author(s):  
Shohei Nogami ◽  
Koichi Hidaka
Keyword(s):  
Visual Navigation ◽  
Stereo Camera ◽  
Moving Obstacles ◽  
Transportation Vehicle

scholarly journals Avoiding moving obstacles during visual navigation

2013 ◽  
Author(s):  
Andrea Cherubini ◽  
Boris Grechanichenko ◽  
Fabien Spindler ◽  
Francois Chaumette
Keyword(s):  
Visual Navigation ◽  
Moving Obstacles

A Visual Navigation Method Using a Hand-Drawn-Route-Map in Dynamic Environments

ROBOT ◽  
2011 ◽  
Vol 33 (4) ◽  
pp. 490-501 ◽  
Author(s):  
Xinde LI ◽  
Xuejian WU ◽  
Bo ZHU ◽  
Xianzhong DAI
Keyword(s):  
Dynamic Environments ◽  
Visual Navigation ◽  
Navigation Method

Visual Navigation With Multiple Goals Based on Deep Reinforcement Learning

2021 ◽  
pp. 1-11
Author(s):  
Zhenhuan Rao ◽  
Yuechen Wu ◽  
Zifei Yang ◽  
Wei Zhang ◽  
Shijian Lu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Visual Navigation ◽  
Multiple Goals

scholarly journals Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

2021 ◽  
Author(s):  
Srivatsan Krishnan ◽  
Behzad Boroujerdian ◽  
William Fu ◽  
Aleksandra Faust ◽  
Vijay Janapa Reddi
Keyword(s):  
Reinforcement Learning ◽  
Embedded System ◽  
Broad Class ◽  
Visual Navigation ◽  
Raspberry Pi ◽  
Latency Distribution ◽  
Hardware In The Loop ◽  
Resource Constrained ◽  
Aerial Robot ◽  
Policy Optimization

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

scholarly journals Analysis and Implementation of a Frequency Control DC–DC Converter for Light Electric Vehicle Applications

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1623
Author(s):  
Bor-Ren Lin
Keyword(s):  
Electric Vehicles ◽  
Output Voltage ◽  
Frequency Control ◽  
Input Voltage ◽  
Pulse Frequency ◽  
Switching Frequency ◽  
Battery Charger ◽  
Dc Microgrid ◽  
Load Voltage ◽  
Transportation Vehicle

In order to realize emission-free solutions and clean transportation alternatives, this paper presents a new DC converter with pulse frequency control for a battery charger in electric vehicles (EVs) or light electric vehicles (LEVs). The circuit configuration includes a resonant tank on the high-voltage side and two variable winding sets on the output side to achieve wide output voltage operation for a universal LEV battery charger. The input terminal of the presented converter is a from DC microgrid with voltage levels of 380, 760, or 1500 V for house, industry plant, or DC transportation vehicle demands, respectively. To reduce voltage stresses on active devices, a cascade circuit structure with less voltage rating on power semiconductors is used on the primary side. Two resonant capacitors were selected on the resonant tank, not only to achieve the two input voltage balance problem but also to realize the resonant operation to control load voltage. By using the variable switching frequency approach to regulate load voltage, active switches are turned on with soft switching operation to improve converter efficiency. In order to achieve wide output voltage capability for universal battery charger demands such as scooters, electric motorbikes, Li-ion e-trikes, golf carts, luxury golf cars, and quad applications, two variable winding sets were selected to have a wide voltage output (50~160 V). Finally, experiments with a 1 kW rated prototype were demonstrated to validate the performance and benefits of presented converter.

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