Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics

Higa, Shoya; Iwashita, Yumi; Otsu, Kyohei; Ono, Masahiro; Lamarre, Olivier; Didier, Annie; Hoffmann, Mark

doi:10.1109/lra.2019.2928765

Cited by 58 publications

(27 citation statements)

References 22 publications

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“…In our study, the T2WI-FS contained 24 18 - 24 (Median [Min-Max]) slices per patient, in which 9 8 - 12 (Median [Min-Max]) slices containing prostate gland were included. The number of annotations per case was 5 4 - 10 (Median [Min-Max]).…”

Section: Methodsmentioning

confidence: 99%

“…First, we performed a pixel-wise analysis to obtain slice-level prediction and CNN features using tumor slices of T2WI-FS ( Figure 3 A, 3D ). A PNASNet-5-large 24 -based progressive search strategy was adopted as the structure for constructing a classification model (generator-net), which earned state-of-art performance for image classification with an accuracy of 1,000-category on the ImageNet 25 test set: 82.9% (top-1) and 96.2% (top-5). Model parameters of the model trained by ImageNet were used for the pre-training network and for transfer learning 26 , in which the filter parameters of the network were frozen, except for the last five layers.…”

Section: Methodsmentioning

confidence: 99%

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Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

et al. 2020

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Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment. Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format. Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% ( P < 0.001) and downgrading rate by 6.4% ( P = 0.029). Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

et al. 2020

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“…An obvious example is data-driven energy consumption modeling for navigation in planetary environments, as demonstrated by Otsu and Kubota (2016). Novel empirical approaches taking advantage of recent breakthroughs in deep learning for power modeling, such as the regression network proposed by Higa et al (2019), could benefit from our dataset. In addition, methods to better predict solar power generation would be useful for planetary navigation; historically, power generation prediction for the MERs tended to be more reactive rather than predictive (Stella et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset

Lamarre

Limoyo

Marić

et al. 2020

The International Journal of Robotics Research

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Future exploratory missions to the Moon and to Mars will involve solar-powered rovers; careful vehicle energy management is critical to the success of such missions. This article describes a unique dataset gathered by a small, four-wheeled rover at a planetary analog test facility in Canada. The rover was equipped with a suite of sensors designed to enable the study of energy-aware navigation and path planning algorithms. The sensors included a colour omnidirectional stereo camera, a monocular camera, an inertial measurement unit, a pyranometer, drive power consumption monitors, wheel encoders, and a GPS receiver. In total, the rover drove more than 1.2 km over varied terrain at the analog test site. All data is presented in human-readable text files and as standard-format images; additional Robot Operating System (ROS) parsing tools and several georeferenced aerial maps of the test environment are also included. A series of potential research use cases is described.

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“…In [28], a deep learning method has been proposed which exploits a 2D convolutional neural network to infer driving energy consumption from RGB and depth images. However, predicting energy consumption from images makes strong assumptions on the correlation between visual appearance and terrain properties.…”

Section: Related Workmentioning

confidence: 99%

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

Visca¹

2022

Preprint

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This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains.<div>Submitted for revision to IEEE Transaction on Cybernetics.</div>

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Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics

Cited by 58 publications

References 22 publications

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

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