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A Mixed-Precision DNN Compiler for Robotic Computing on FPGA

A Mixed-Precision DNN Compiler for Robotic Computing on FPGA

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robotics, automation and Artificial intelligence (RAAI), IEEE international Conference on

作者： Qiufeng Li Hantao Huang Ao Shen Kai Li Sixiao Huang Hao Yu SUSTech School of Microelectronics Shenzhen China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Adopting deep learning models in robotic systems such as simultaneous localization and mapping (SLAM) attracts great interests from the community. The deployment of deep learning models on resource-constrained robotic edge system poses significant challenges due to their limited computational resources. Network architecture search(NAS)-based quantization is proven to achieve a great balance between network performance and hardware efficiency, which typically results in a mixed-precision network. Many mixed-precision accelerators have been developed as well to support computing mixed-precision networks manually. A software-hardware interface is thereby needed to au-tomatically map mixed-precision networks onto mixed-precision accelerators. In this paper, we have developed a custom compiler framework for maximized speed-up through optimal mapping to the 2–8 bit acceleration, better scheduling on the core and minimum runtime overhead. Experiments show that, through our automated compilation approach, the mixed-precision models deployed on the ZCU102 FPGA achieves maximumthroughput (524 GOPs for VGG16, 423GOPs for UndeepVo, 705 GOPs OpenPose), meeting the real-time computing requirements for robotic DNNs.

关键词： Deep learning Simultaneous localization and mapping Runtime Quantization (signal) Processor scheduling computational modeling Throughput

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computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search

Computationally-Efficient Roadmap-based Inspection Planning ...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Fu, Mengyu Salzman, Oren Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA Technion Israel Inst Technol Comp Sci Dept Haifa Israel

ISBN: (纸本)9781728190778

The inspection-planning problem calls for computing motions for a robot that allow it to inspect a set of points of interest (POIs) while considering plan quality (e.g., plan length). This problem has applications across many domains where robots can help with inspection, including infrastructure maintenance, construction, and surgery. Incremental Random Inspection-roadmap Search (IRIS) is an asymptotically-optimal inspection planner that was shown to compute higher-quality inspection plans orders of magnitudes faster than the prior state-of-the-art method. In this paper, we significantly accelerate the performance of IRIS to broaden its applicability to more challenging real-world applications. A key computational challenge that IRIS faces is effectively searching roadmaps for inspection plans-a procedure that dominates its running time. In this work, we show how to incorporate lazy edge-evaluation techniques into IRIS's search algorithm and how to reuse search efforts when a roadmap undergoes local changes. These enhancements, which do not compromise IRIS's asymptotic optimality, enable us to compute inspection plans much faster than the original IRIS. We apply IRIS with the enhancements to simulated bridge inspection and surgical inspection tasks and show that our new algorithm for some scenarios can compute similar-quality inspection plans 570x faster than prior work.

关键词： Iris Conferences Surgery Inspection Maintenance engineering computational efficiency Planning

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Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

Coupled Mobile Manipulation via Trajectory Optimization with...

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IEEE international Conference on robotics and automation (ICRA)

作者： Spahn, Max Brito, Bruno Alonso-Mora, Javier Delft Univ Technol Dept Cognit Robot NL-2628 CD Delft Netherlands

ISBN: (纸本)9781728190778

This paper presents a real-time method for whole-body trajectory optimization of mobile manipulators in simplified dynamic and unstructured environments. Current trajectory optimization methods typically use decoupling of the mobile base and the robotic arm, which reduces flexibility in motion, does not scale to unstructured environments, and does not consider the future evolution of the environment, which is crucial to avoid dynamic obstacles. Given a goal configuration, such as waypoints generated by a global path planner, we formulate a receding horizon trajectory optimization minimizing the distance-to-target while avoiding collisions with static and dynamic obstacles. The presented method unifies the control of a robotic arm and a non-holonomic base to allow coupled trajectory planning. For collision avoidance, we propose to compute three convex regions englobing the robot's major body parts (i.e., base, shoulder-link and wrist-link) and thus reducing and limiting the number of inequality constraints, regardless of the number of obstacles in the environment. Moreover, our approach incorporates predicted trajectory information to smoothly, and in advance, avoid dynamic obstacles. The presented results show that trajectory optimization for the coupled system can reduce the total execution time by 48% and that applying the convex region generation for individual links allows keeping the computational costs low, even for complex scenarios, enabling onboard implementation.

关键词： Limiting Trajectory planning Navigation Conferences Dynamics Real-time systems computational efficiency

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Multiple Objects Detection and Localization using Data Fusion

Multiple Objects Detection and Localization using Data Fusio...

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automation, robotics and Computer Engineering (ICARCE), international Conference on

作者： Ayman Khaled Omar Shalash Ossama Ismaeil Information and Documentation Center Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Arab Academy for Science Technology and Maritime Transport College of Artificial Intelligence Al Alamein Egypt Regional Informatics Center Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

Real-time object detection is one of the most fundamental computer vision processes, it is of great importance for many applications, such as video surveillance and autonomous driving. Object detection with multiple modalities of data is of great importance as it provides more information about the surrounding objects. Most 3D object detection approaches are based on utilizing expensive sensors and/or high computational requirements. Therefore, in this paper, we propose a low-cost real-time multiple objects detection platform using a low-cost 2D-LiDAR and an RGB camera. YOLOv3 has been adopted as the object detection algorithm, moreover, a novel calibration technique has been proposed to achieve mapping between the camera and LiDAR. Experiments were made on real-world scenarios; the performance of the system was validated by manually measuring the distances of detected objects. The system outputs surrounding objects along with their classification and their 3D location in the scene.

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DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

DeepQ Stepper: A framework for reactive dynamic walking on u...

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IEEE international Conference on robotics and automation (ICRA)

作者： Meduri, Avadesh Khadiv, Majid Righetti, Ludovic NYU Tandon Sch Engn New York NY 10003 USA Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781728190778

Reactive stepping and push recovery for biped robots is often restricted to flat terrains because of the difficulty in computing capture regions for nonlinear dynamic models. In this paper, we address this limitation by proposing a novel 3D reactive stepper, the DeepQ stepper, that can approximately learn the 3D capture regions of both simplified and full robot dynamic models using reinforcement learning, which can then be used to find optimal steps. The stepper can take into account the entire dynamics of the robot, ignored in most reactive steppers, leading to a significant improvement in performance. The DeepQ stepper can handle nonconvex terrain with obstacles, walk on restricted surfaces like stepping stones while tracking different velocities, and recover from external disturbances for a constant low computational cost.

关键词： Legged locomotion Solid modeling Three-dimensional displays Costs computational modeling Friction Reinforcement learning

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Multidisciplinary and Multiphysical Model Represents Complex Research in Applied Informatics

Multidisciplinary and Multiphysical Model Represents Complex...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

This paper is about integration of model representations to serve integrated research in applied informatics and applied mathematics. Integration was one of main areas in the engineering purposed software development in a comprehensive effort to serve rushing development of high level automation technologies in aircraft and other leading industries during the past decades. By now, integration of formerly separated model representations provides wide and comprehensive collection of modeling, simulation, organizing, and communication means in in high level multidisciplinary and multiphysical scenario of engineering modeling platform (EMP). This paper focuses on the Virtual Research Laboratory (VRL) where a suitable EMP configuration serves as an all-in-one laboratory for scientific-industrial research. It introduces some of the recent research results in this unusual application of EMP among others the experimental model of research (EMR) which is an all-in-one representation of a research and utilizes services available at a world level EMP. The multidisciplinary and multiphysical EMR is central issue in PhD research program at VRL. Active knowledge background empowers the reactive and autonomous characteristics of EMR. Actual issues at EMP based implementation of EMR at VRL are also discussed in this paper.

关键词： Surveys computational modeling Atmospheric modeling Collaboration Organizations Medical services Mathematical models

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Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information

Deep Learning-based Feature Fusion for Action Recognition Us...

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international Conference on robotics and Artificial intelligence (ICRAI)

作者： Fahad Ul Hassan Asif Mattoo Umar Shahbaz Khan Tahir Nawaz Nasir Rashid Department of Mechatronics Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan

Various action recognition systems have been proposed, but most of them are not feasible to be used in real-time applications. Skeleton-based action recognition has a low computational cost and is not affected by background changes. As the pose estimation models are becoming faster (almost real-time), a model was created with only 1.8M parameters named DD-net, which uses the skeleton information to predict the action. Recently an improved version of the model came out and was named TD-net. The model is very rich with geometric-based features but lacks motion-based features. To overcome this we added two motion features in the model named acceleration and velocity. These features were created using second order Taylor's approximation, in a window around the current frame. The model accuracy was compared with DD-net, TD-net, and state-of-the-art algorithms using three different datasets. An increase in accuracy is observed for all three datasets (i.e 1.1% for SHERC, 1.7% for FPHAB and 2% for JHMDB) when compared with TD-net.

关键词： Industries computational modeling Pose estimation Predictive models Streaming media Skeleton Real-time systems

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Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors

Learning-Based Dimensionality Reduction for Computing Compac...

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IEEE international Conference on robotics and automation (ICRA)

作者： Hao Dong Xieyuanli Chen Mihai Dusmanu Viktor Larsson Marc Pollefeys Cyrill Stachniss University of Bonn Germany ETH Zurich Switzerland Lund University Sweden Microsoft Research Department of Engineering Science University of Oxford UK Lamarr Institute for Machine Learning and Artificial Intelligence Germany

A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high-dimensional; 128 dimensions or even more. The higher the dimensionality, the larger the memory consumption and computational time for approaches using such descriptors. In this paper, we investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We thoroughly analyze our method in unsuper-vised, self-supervised, and supervised settings, and evaluate the dimensionality reduction results on four representative descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval. The experiments show that our lightweight MLPs trained using supervised method achieve better dimensionality reduction than PCA. The lower-dimensional descriptors generated by our approach outperform the original higher-dimensional descriptors in downstream tasks, especially for the hand-crafted ones. The code is available at https://***/PRBonn/descriptor-dr.

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Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

Active Model Learning using Informative Trajectories for Imp...

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IEEE international Conference on robotics and automation (ICRA)

作者： Zhang, Weixuan Tognon, Marco Ott, Lionel Siegwart, Roland Nieto, Juan Swiss Fed Inst Technol Autonomous Syst Lab Leonhard Str 21 CH-8092 Zurich Switzerland

ISBN: (纸本)9781728190778

Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. We experimentally demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from noninformative trajectory by 13.3% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.

关键词： Uncertainty computational modeling Training data Data collection Data models Unmanned aerial vehicles Trajectory

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Geometry-aware Compensation Scheme for Morphing Drones

Geometry-aware Compensation Scheme for Morphing Drones

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IEEE international Conference on robotics and automation (ICRA)

作者： Fabris, Amedeo Kleber, Kevin Falanga, Davide Scaramuzza, Davide Univ Zurich Robot & Percept Grp Dept Informat CH-8050 Zurich Switzerland Univ Zurich Dept Neuroinformat CH-8050 Zurich Switzerland Swiss Fed Inst Technol CH-8050 Zurich Switzerland

ISBN: (纸本)9781728190778

Recent studies have shown that enabling drones to change their morphology in flight can significantly increase their versatility in different tasks. In this paper, we investigate the aerodynamic effects caused by the partial overlap between the propellers and the main body of a morphing quadrotor during flight. We experimentally characterize such effects and design a morphology-aware control scheme to compensate them. We demonstrate the effectiveness of our approach by deploying the compensation scheme on a quadrotor that can fold its arms around the main body, comparing it against the same controller without the compensation scheme. Experimental results show that our compensation scheme can address the loss of thrust due to the overlap between the main body and the propellers, guaranteeing higher tracking accuracy, without requiring complex and computationally expensive aerodynamical models. To the best of our knowledge, this is the first work counteracting the aerodynamic effects of a morphing quadrotor during flight and showing the effects of partial overlap between a propeller and the central body of the drone.

关键词： Power demand Propellers Conferences computational modeling Morphology Aerodynamics Reliability engineering

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