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Vision-based fall detection and prevention for the elderly people: A review ongoing research 5

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Vision-based fall detection and prevention for the elderly p...

5th International Conference on Intelligent Computing in Data sciences, ICDS 2021

作者： Guendoul, Oumaima Hamd, Ait Abdelali Youness, Tabii Rachid, Oulad Haj Thami Omar, Bourja Mohammed v University ADMIR Lab National School of Computer Science and Systems Analysis ENSIAS Rabat10000 Morocco MAScIR Embedded Systems and Artificial Intelligence Department Rabat10000 Morocco

ISBN: (纸本)9781665442381

Falls are the most leading cause of accidental injury deaths worldwide, therefore, it poses a real challenge for the prevention of life-threatening conditions in geriatrics. The most damaged community is the ever-growing aging population. For this reason, there are considerable demands to distinguish a dangerous posture such as fall in real-time. Here we provide a literature review of conducted work on elderly fall detection and prediction mentioning the main methods to recognize human posture including computer vision-based and wearable sensor-based. We approached these perspectives: sensor fusion, Datasets, approaches proposition. In conclusion, our survey summarizes the progress achieved in the five past years to help the researchers in this field to spot areas where further effort would be beneficial and innovative. © 2021 IEEE.

关键词： computer vision

Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications

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arXiv 2024年

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab. Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © 2024, CC BY.

关键词： Costs

Vision-based indoor localization of nano drones in controlled environment with its applications

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arXiv 2024年

作者： Singh, Simranjeet Kumar, Amit Chemban, Fayyaz Pocker Fernandes, Vikrant Penubaku, Lohit Arya, Kavi Electrical Engineering Department Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due to their compact size, payload restrictions, and computational capabilities. This paper proposes an approach for localization using off-board computing, an off-board monocular camera, and modified open-source algorithms. The proposed method uses three parallel proportional-integral-derivative controllers on the off-board computer to provide velocity corrections via wireless communication, stabilizing the NAV in a custom-controlled environment. Featuring a 3.1cm localization error and a modest setup cost of 50 USD, this approach proves optimal for environments where cost considerations are paramount. It is especially well-suited for applications like teaching drone control in academic institutions, where the specified error margin is deemed acceptable. Various applications are designed to validate the proposed technique, such as landing the NAV on a moving ground vehicle, path planning in a 3D space, and localizing multi-NAVs. The created package is openly available at https://***/simmubhangu/eyantra drone to foster research in this field. © 2024, CC BY.

关键词： Drones

Graph-Based Local Planning with Spatiotemporal Risk Assessment for Risk-Bounded and Prediction-Aware Autonomous Driving

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Graph-Based Local Planning with Spatiotemporal Risk Assessme...

International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： Abdulrahman Ahmad Majid Khonji Ameena Al-Sumaiti Jorge Dias Khaled Elbassioni Department of Computer Science Khalifa University Abu Dhabi UAE Khalifa University Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE Department of Electrical Engineering Smart OR Lab Advanced Power Energy Center (APEC) Khalifa University Abu Dhabi UAE Department of Computer and Information Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Risk-bounded motion planning for autonomous driving in dynamic environments presents significant research challenges. Ensuring continuous navigation towards a destination while making real-time decisions is a nonconvex problem. This paper presents a graph-based local planning method constrained by user-specific driving preference, represented as a risk-bound criterion for motion planning. First, we propose a lattice graph construction method that adheres to the vehicle's curvature constraints. Then, we formulate the trajectory planning problem as an integer-linear programming task, addressed by our novel risk-bounded and prediction-aware constrained shortest path. Our solution accounts for both static and dynamic obstacles in urban settings, adhering to traffic regulations. At the core of our approach is a conservative spatiotemporal risk assessment mechanism, which evaluates collisions considering the uncertain delay from speed control of the ego vehicle and predicted trajectories of dynamic obstacles. We implemented our solution using the CARLA simulator and the ROS2 platform, within a comprehensive framework encompassing global planning, local planning, and vehicle control. The effectiveness of our approach is demonstrated through notable collision avoidance, improved path-tracking, and enhanced risk-bounded planning capabilities.

关键词： Trajectory planning Dynamics Velocity control Planning Spatiotemporal phenomena Trajectory Risk management Vehicle dynamics Collision avoidance Autonomous vehicles

The Controllability, Observability, and Stability Analysis of a Class of Composite systems with Fractional Degree Generalized Frequency Variables

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 859-864页

作者： Cuihong Wang Yafei Zhao YangQuan Chen IEEE the Department of Mathematics and Computer Science Shanxi Normal University the Mechatronics Embedded Systems and Automation(MESA) Lab

This paper is concerned with fundamental properties of a class of composite systems with fractional degree generalized frequency variables, including controllability, observability and stability. Firstly, some necessary and sufficient conditions are given to guarantee controllability and observability of such composite systems. Then we prove that the stability problem of such composite systems can be reduced to judging whether a fractional degree polynomial is stable. Finally, the stability analysis result is applied in the supervisory control of fractional-order multi-agent systems, and an example is provided to illustrate the effectiveness of the proposed methods.

关键词： Composite system controllability fractionalorder system observability stability

Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

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arXiv 2022年

作者： Schmid, Robin Atha, Deegan Schöller, Frederik Dey, Sharmita Fakoorian, Seyed Otsu, Kyohei Ridge, Barry Bjelonic, Marko Wellhausen, Lorenz Hutter, Marco Agha-Mohammadi, Ali-Akbar PasadenaCA United States Swiss Federal Institute of Technology ETH Zürich Robotic Systems Lab Switzerland University of Goettingen Department of Computer Science Germany Technical University of Denmark Department of Electrical Engineering and Photonics Denmark

Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a significant investment in human time, assumes correct expert classification, and small details can lead to misclassification. To address these challenges, we propose a method for predicting high- and low-risk terrains from only past vehicle experience in a self-supervised fashion. First, we develop a tool that projects the vehicle trajectory into the front camera image. Second, occlusions in the 3D representation of the terrain are filtered out. Third, an autoencoder trained on masked vehicle trajectory regions identifies low- and high-risk terrains based on the reconstruction error. We evaluated our approach with two models and different bottleneck sizes with two different training and testing sites with a four-wheeled off-road vehicle. Comparison with two independent test sets of semantic labels from similar terrain as training sites demonstrates the ability to separate the ground as low-risk and the vegetation as high-risk with 81.1% and 85.1% accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Off road vehicles

End-to-End Design for Self-Reconfigurable Heterogeneous robotic Swarms

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End-to-End Design for Self-Reconfigurable Heterogeneous Robo...

International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Jorge Peña Queralta Li Qingqing Tuan Nguyen Gia Hong-Linh Truong Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Turku Finland Department of Computer Science Aalto University Finland

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile robots; autonomous aerial vehicles; learning (artificial intelligence); control engineering computing; mobile computing; protoc... 详细信息

ISBN: (数字)9781728143514

ISBN: (纸本)9781728198040

关键词： Robot sensing systems Drones Robot kinematics Collaboration Cloud computing

Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

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arXiv 2024年

作者： Mattamala, Matias Frey, Jonas Libera, Piotr Chebrolu, Nived Martius, Georg Cadena, Cesar Hutter, Marco Fallon, Maurice Dynamic Robot Systems Group University of Oxford 23 Banbury Road Oxford OxfordshireOX2 6NN United Kingdom Robotic Systems Lab ETH Zurich Leonhardstrasse 21 Zurich Zurich8092 Switzerland Autonomous Learning Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Baden-Württemberg72076 Germany Computer Science Department University of Tüüingen Maria-von-Linden-Strasse 6 Tübingen Baden-Württemberg72076 Germany

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. © 2024, CC BY.

关键词： Parks

Motion planner augmented reinforcement learning for robot manipulation in obstructed environments

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arXiv 2020年

作者： Yamada, Jun Lee, Youngwoon Salhotra, Gautam Pertsch, Karl Pflueger, Max Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Cognitive Learning for Vision and Robotics Lab United States Robotic Embedded Systems Laboratory Department of Computer Science University of Southern California United States

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://***/mopa-rl. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning