End-to-end deep learning for autonomous navigation of mobile robot

Kim, Ye-Hoon; Jang, Junik; Yun, Sojung

doi:10.1109/icce.2018.8326229

Cited by 34 publications

(22 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination of visual information and DRL mechanism can implicitly accomplish engineering projects such as localization, mapping, and path planning in end-to-end manner, with environment information embedded in network parameters. Ye-Hoon Kim [20] propose an end-to-end navigation method to extract visual features directly from images by the camera, which greatly reduces the power consumption and computational time. The experiment is performed in an office scene with a simplified DRL model, achieving satisfying results.…”

Section: Related Work a Drl Navigationmentioning

confidence: 99%

Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

Xue

2020

IEEE Access

View full text Add to dashboard Cite

As vision and language processing techniques have made great progress, mapless-visual navigation is occupying uppermost position in domestic robot field. However, most current end-to-end navigation models tend to be strictly trained and tested on identical datasets with stationary structure, which leads to great performance degradation when dealing with unseen targets and environments. Since the targets of same category could possess quite diverse features, generalization ability of these models is also limited by their visualized task description. In this paper we propose a model-agnostic metalearning based textdriven visual navigation model to achieve generalization to untrained tasks. Based on meta-reinforcement learning approach, the agent is capable of accumulating navigation experience from existing targets and environments. When applied to finding a new object or exploring in a new scene, the agent will quickly learn how to fulfill this unfamiliar task through relatively few recursive trials. To improve learning efficiency and accuracy, we introduce fully convolutional instance-aware semantic segmentation and Word2vec into our DRL network to respectively extract visual and semantic features according to object class, creating more direct and concise linkage between targets and their surroundings. Several experiments have been conducted on realistic dataset Matterport3D to evaluate its target-driven navigation performance and generalization ability. The results demonstrate that our adaptive navigation model could navigate to text-defined targets and achieve fast adaption to untrained tasks, outperforming other state-of-the-art navigation approaches.

show abstract

Section: Related Work a Drl Navigationmentioning

confidence: 99%

Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

Xue

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that some emerging studies [113][114][115] propose different driving algorithms that avoid the need for localization and mapping stages, and instead sense the environment and directly produce end-to-end driving decisions. This is known as the behavior reflex approach [113].…”

Section: Ego-localization and Mappingmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

256

130

View full text Add to dashboard Cite

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

show abstract

“…Several works have used a single perception sensor such as 2-D lidar or raw data from a depth camera to train collision avoidance behaviors in a robot using expert demonstrations [Pfeiffer et al, 2016] and imitation learning [Tai et al, 2018]. End-to-end methods [Kim et al, 2018], and methods using deep double-Q networks [Xie et al, 2017] [Schulman et al, 2017] using a 2-D lidar in [Long et al, 2017]. This approach was extended to a hybrid control architecture [Fan et al, 2018b], which switched between different policies to optimize the navigation.…”

Section: Learning-based Collision Avoidancementioning

confidence: 99%

“…In recent years, there has been significant work on learning-based collision avoidance for mobile robots operating in such dense scenarios. These include techniques based on end-to-end deep learning [Kim et al, 2018;Pfeiffer et al, 2017], generative adversarial imitation learning [Tai et * Authors contributed equally.…”

Section: Introductionmentioning

confidence: 99%

Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation

Liang

Patel

Sathyamoorthy

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning (DRL). Our simulator models realistic crowd and pedestrian behaviors, along with friction, sensor noise and delays in the simulated robot model. We also describe a technique to incrementally control the randomness and complexity of training scenarios to achieve better convergence and generalization capabilities. We demonstrate the effectiveness of our simulator by training a policy that fuses data from multiple perception sensors such as a 2-D lidar and a depth camera to detect pedestrians and computes smooth, collision-free velocities. Our novel reward function and multi-sensor formulation results in smooth and unobtrusive navigation. We have evaluated the learned policy on two differential drive robots and evaluate its performance in new dense crowd scenarios, narrow corridors, T and L-junctions, etc. We observe that our algorithm outperforms prior dynamic navigation techniques in terms of metrics such as success rate, trajectory length, mean time to goal, and smoothness.

show abstract

End-to-end deep learning for autonomous navigation of mobile robot

Cited by 34 publications

References 15 publications

Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation

Contact Info

Product

Resources

About