| 0 | https://ppubs.uspto.gov/pubwebapp/external.html?q=(20180373248).pn | | NOKOMIS, INC | 2018-12-27T00:00:00.000Z |
| | Current embodiments of such systems are on board level or PC level scales with SWaP parameters of one order of magnitude to several orders of magnitude larger than the invention solution. The ...
| | | |
| 1 | https://ppubs.uspto.gov/pubwebapp/external.html?q=(20190138934).pn | | Saurav Prakash | 2019-05-09T00:00:00.000Z |
| | Example use cases for GD algorithms include localization in wireless sensor networks and distributed path-planning for drones. (2019)
| | | |
| 2 | https://ppubs.uspto.gov/pubwebapp/external.html?q=(20190138934).pn | | Saurav Prakash | 2019-05-09T00:00:00.000Z |
| | Linear regression is one such method that is used for many use cases including, for example, classification, stock market analysis, weather prediction, localization in wireless sensor networks ...
| | | |
| 6 | https://deepai.org/publication/cooperative-multi-agent-deep-reinforcement-learning-for-reliable-surveillance-via-autonomous-multi-uav-control | | deepai.org | 2022-06-26T02:38:36.000Z |
| | Recently, a considerable amount of work has been published on the optimization of deployment of UAVs for cellular services, including optimization-based coverage control, which is essential for ...
| | | |
| 7 | https://www.therobotreport.com/path-planning-algorithm-guides-penguing-counting-drones/ | | therobotreport.com | 2022-07-03T08:39:37.000Z |
| | Path-planning algorithm guides penguin-counting drones One of the autonomous drones used to survey penguins in Antartica. Stanford University researcher Mac Schwager entered the world of penguin ...
| | | |
| 8 | https://worldwidescience.org/topicpages/i/improved+obstacle+avoidance.html | | worldwidescience.org | 2022-08-09T11:42:14.000Z |
| | In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human-Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most ...
| | | |
| 9 | https://mynews.live-website.com/tag/robotics | | mynews.live-website.com | 2022-08-17T22:19:34.000Z |
| | One of the challenges in robotics is how to design robots that can navigate cluttered environments - something humans and other animals manage to do instinctively every day. Per the authors, many ...
| | | |
| 10 | https://doi.org/10.3390/s22239180 | | Pablo Flores Peña | 2022-11-25T00:00:00.000Z |
| | In addition, the benefits of using UAVs in different missions has led researchers to investigate how to overcome the missions' problems by dealing with different techniques such as the swarming ...
| | | |
| 11 | https://worldwidescience.org/topicpages/a/autonomous+underwater+modeling.html | | worldwidescience.org | 2022-11-28T01:25:51.000Z |
| | We conclude that AUVs are highly applicable for ice-monitoring, but further research is needed. Onboard assessment of XRF spectra using genetic algorithms for decision making on an autonomous ...
| | | |
| 12 | https://www.sciencegate.app/keyword/741969 | | sciencegate.app | 2022-12-01T17:20:58.000Z |
| | Then, by setting the coordinates of the initial moving obstacle and identifying safety distance, we can define the shape of the obstacle and the path planning of the approach segment in thunderstorm
| | | |
| 13 | https://doi.org/10.3389/fnbot.2022.1105177 | | Xiaohui Cui | 2022-12-22T00:00:00.000Z |
| | On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning ...
| | | |
| 14 | https://doi.org/10.3390/s23010087 | | Reza Ghabcheloo | 2022-12-22T00:00:00.000Z |
| | The proposed algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of ...
| | | |
| 15 | https://doi.org/10.1007/s00530-021-00833-2 | | Ahmed Barnawi | 2023-01-01T00:00:00.000Z |
| | With the availability of massive datasets and powerful computational power, CNN can extract high- and low-level features from the dataset, leading to the performance equal to humans in various applications. Furthermore, efficient CNN architecture gives an accurate prediction and is computationally efficient as well. Hence, researchers selected CNNs as an applied AI diagnosis algorithm for COVID-19. The advantages of using this approach are efficiency and learning through backpropagation. In the proposed work, we have created an autonomous CNN model that takes a digital chest X-ray image as an input and classifies the image into three categories, namely COVID-19 (positive), normal (negative), and pneumonia. Contributions The contributions are summarized as follows: A pre-processing step is introduced using various statistical image enhancement techniques (such as removing blur and noise, sharpness, contrast correction, etc.) to enhance the illumination in the digital X-ray images and improve overall image quality before feeding it to the neural network. A deep convolutional neural network using transfer learning is proposed, classifying a given chest X-ray input image into COVID-19, normal, or pneumonia class. Moreover, the proposed model is compared with the existing state-of-the-art models. An optimal path-planning scheme is designed to deliver emergency medical kits to the hospitals in need with lesser cost and following constraints regarding available fuel (battery life), available medicine, and specified time limit.Following, the related work is discussed in Sect.
| | | |
| 16 | https://doi.org/10.1007/s00530-021-00833-2 | | Ahmed Barnawi | 2023-01-01T00:00:00.000Z |
| | A deep convolutional neural network using transfer learning is proposed, classifying a given chest X-ray input image into COVID-19, normal, or pneumonia class. Moreover, the proposed model is ...
| | | |
| 17 | https://www.frontiersin.org/articles/10.3389/frobt.2022.1076897/full | | frontiersin.org | 2023-03-26T18:55:10.000Z |
| | The field of automated planning (sometimes called AI planning) focuses on finding a sequence of actions that allows an intelligent agent (for example, a robot) to reach a goal state (for example, a ...
| | | |
| 20 | https://www.city.ac.uk/about/people/academics/nabil-aouf | | city.ac.uk | 2023-05-30T05:37:46.000Z |
| | ... doi:10.1109/radar41533.2019.171310 M.L. and Merlet, T. (2019). Comparison of Descriptors for SAR ATR. 2019 IEEE Radar Conference (RadarConf19) 22-26 April. doi:10.1109/radar. 2019.8835804 Viana, I.B. and Aouf, N. (2018). Distributed Cooperative Path-Planning for Autonomous Vehicles Integrating Human Driver Trajectories. 2018 International Conference on Intelligent Systems (IS) 25-27 September. doi:10.1109/is. 2018.8710544 Rondao, D. and Aouf, N. (2018). Multi-View Monocular Pose Estimation for Spacecraft Relative Navigation.
| | | |
| 21 | https://profiles.stanford.edu/mac-schwager | | profiles.stanford.edu | 2023-05-31T05:08:40.000Z |
| | Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance ...
| | | |
| 23 | https://thesai.org/Publications/ViewIssue?volume=13&issue=12&code=IJACSA | | thesai.org | 2023-06-07T06:21:01.000Z |
| | The problem of partially knowing and dynamic environments has received little attention. This circumstance occurs when an exploratory robot or a robot without a floor plan or terrain map must move ...
| | | |
| 27 | https://doi.org/10.3390/s23187709 | | Annisa Anggun Puspitasari | 2023-09-06T00:00:00.000Z |
| | In addition, due to the implementation of massive MIMO (m-MIMO) systems, high overhead and computational complexity becomes a drawback for mathematical models in optimizing the functionality of the ...
| | | |
| 28 | https://doi.org/10.3390/s23187709 | | Annisa Anggun Puspitasari | 2023-09-06T00:00:00.000Z |
| | The proposed system has proven robust and effective in different kinds of trajectory tracking, while in , the authors proposed the asynchronous multithreading proximal policy optimization-based path planning (AMPPO-PP) and trajectory tracking (AMPPO-TT) algorithms for autonomous planning, tracking, and emergency obstacle avoidance in underwater vehicles. AMPPO-PP proved effective in planning paths around underwater communication by outperforming the classical path-planning algorithm and performing at the same level as the advanced sampling-based path-planning method. In contrast, AMPPO-TT is a trajectory-tracking algorithm that provides good tracking performance in three-dimensional coastline detection scenarios. Another study applied RL-based methods to control the underwater vehicle by redesigning the cost function, which allowed the vehicle to avoid obstacles smoothly .
| | | |
| 38 | https://www.frontiersin.org/articles/10.3389/fpls.2021.611940/full | | frontiersin.org | 2023-12-05T02:39:46.000Z |
| | Since the sunlight has infrared wavelengths and wind moves the targets, the location of the target in 3-dimensional space might not be accurately measured (Andujar et al., 2017; Narvaez et al., ...
| | | |
| 43 | https://doi.org/10.3390/biomimetics9020088 | | Lin Xie | 2024-02-01T00:00:00.000Z |
| | Qiu et al. proposed a distributed UAV swarm algorithm that uses bird flocking intelligence to guide a swarm of drones with multiple leading individuals to approach a target synchronously without ...
| | | |
| 45 | https://doi.org/10.3390/biomimetics9040238 | | Yixun Chao | 2024-04-16T00:00:00.000Z |
| | Although the above methods can solve the problems of the local minimum value of traditional path-planning algorithms to some extent, they are not suitable for fast path planning in the large-scale complex flight environments of drones. With the development of AI (artificial intelligence), many AI-based path-planning algorithms have been conducted, providing inspiration for solving the problems of traditional motion decision-making algorithms. Machmudah, A. et al., addressed the optimization of flight trajectories for a fixed-wing UAV (unmanned aerial vehicle) at a constant altitude by employing a Bezier curve and meta-heuristic optimizations, including PSO (particle swarm optimization), which minimized the path length while satisfying the maximum curvature and collision avoidance constraints .
| | | |
| 47 | https://doi.org/10.1038/s41598-024-60051-4 | | Yong He | 2024-04-22T00:00:00.000Z |
| | Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the ...
| | | |
| 48 | https://doi.org/10.3390/biomimetics9060351 | | Yong Xu | 2024-06-11T00:00:00.000Z |
| | Therefore, it is of great value to discuss the intelligent optimization algorithm of path planning from the perspective of the algorithm itself and its application category. Path-planning algorithms
| | | |
| 49 | https://doi.org/10.1371/journal.pone.0308264 | | Mohd Nadhir Ab Wahab | 2024-08-12T00:00:00.000Z |
| | Deep learning and reinforcement learning applications have aided advancements in autonomous systems, allowing them to navigate increasingly complicated and dynamic situations. Furthermore, ...
| | | |
| 50 | https://doi.org/10.1371/journal.pone.0308264 | | Mohd Nadhir Ab Wahab | 2024-08-12T00:00:00.000Z |
| | This highlights the need for research on path-planning algorithms to make the mobile robot move in the shortest collision-free path possible. This prompts the need for this research, as path planning brings many benefits to the area of the mobile robot when the path planned is optimised which include reduced energy consumption, reduced maintenance, and ensuring the safety of the mobile robot. Ideal path planning will help mobilise the robot effectively and efficiently and ensure the mobile robot's usability after each use, reducing the cost of repairs and maintenance. However, the majority of the solutions only provide the means to generate a path from the start point to the end point without optimising it. These paths can be optimised in terms of length, smoothness, and even safety of the path generated. However, solutions that are geared up to the area of optimisation can be found at times falling into the local optimal . Path planning algorithms have made significant advances in a variety of disciplines in recent years, owing to the growth of autonomous systems and robots. Deep learning-based techniques have gained traction, with convolutional neural networks (CNNs) and recurrent neural networks (RNNs) used to provide end-to-end navigation and map learning for improved path planning. Deep Q-Networks (DQN) and Proximal Policy Optimisation (PPO) are two reinforcement learning approaches that have been used to train agents to navigate complicated and dynamic environments. Multi-agent route planning has grown in importance, notably for coordinating the movements of autonomous vehicles and drones to avoid collisions and optimise trajectories.
| | | |
| 51 | https://doi.org/10.3390/s24237812 | | Kemeng Ran | 2024-12-06T00:00:00.000Z |
| | Path-planning algorithms enable study of high-dimensional spaces and non-holonomic constraint problems, applicable in scenarios like map navigation, robot operations, drone path planning, and ...
| | | |
| 52 | https://doi.org/10.3390/s24248089 | | Nour AbuJabal | 2024-12-18T00:00:00.000Z |
| | Optimizing these factors simultaneously is challenging, as higher map resolutions improve path quality but also increase processing time. The previously described trade-off problem between execution
| | | |
| 56 | https://doi.org/10.3390/s25072005 | | Shuyue Liu | 2025-03-23T00:00:00.000Z |
| | Existing relay UAV path-planning methods are categorized into two categories: deep reinforcement learning (DRL) and bio-inspired optimization algorithms. 1.1. DRL DRL is a machine learning method based on artificial neural networks. By constructing multilayer neural networks, it simulates the learning process of the human brain and gradually achieves maximum reward through interaction with the environment . To address the challenge of UAV communication interruptions caused by frequent changes in the channel model between UAV and users in urban environments with many buildings, a neural network-based strategy has been proposed . This approach processes environmental information provided as input and signal strength to predict discrete environmental types, allowing the UAV to anticipate dynamic user mobility in the environment and plan its path, thereby improving communication performance. To address the slow response of traditional coverage path planning to dynamic environments, a DRL-based coverage path algorithm was previously proposed . First, the environmental information sensed by the SAR-UAV was modeled, and then the DRL algorithm was used to design the action space and reward function of the UAV based on the environmental model. Finally, deep learning methods were applied to control the UAV flight to complete coverage trajectory planning. 1.2. Bio-Inspired Optimization Algorithms Bio-inspired optimization algorithms are optimization methods based on biomimetics, such as genetic algorithms (GAs), ant colony optimization (ACO), and particle swarm optimization (PSO).
| | | |
| 57 | https://doi.org/10.3390/s25072005 | | Shuyue Liu | 2025-03-23T00:00:00.000Z |
| | The number and location of ground users change continuously, leading to dynamic variations in the service areas of relay UAVs . Effective path planning for relay UAVs necessitates obstacle and ...
| | | |
| 58 | https://ppubs.uspto.gov/pubwebapp/external.html?q=(20250117251).pn | | Eric COLTER | 2025-04-10T04:07:28.000Z |
| | The sign itself may be identified as a traffic sign by a first deployed neural network (e.g., a neural network that has been trained), the text "Flashing lights indicate icy conditions" may be ...
| | | |
| 59 | https://doi.org/10.1038/s41598-025-15345-6 | | K. Karthik | 2025-08-23T00:00:00.000Z |
| | Jackals encircling and consuming prey is represented mathematically as shown below ( ).21 22 In addition, Algorithm 1 presents the flow process of GJOA adopted for achieving dynamic UAV path ...
| | | |
| 61 | https://doi.org/10.1002/adma.202504796 | | Tuğba Akkaş | 2025-08-28T00:00:00.000Z |
| | It begins with an overview of the evolution of AI, biosensor technology, and their integration.
| | | |
| 63 | https://doi.org/10.1038/s41598-025-17313-6 | | Indra Kishor | 2025-09-01T00:00:00.000Z |
| | To evaluate the real-time performance improvements provided by Edge AI analytics in managing latency and enhancing operational efficiency. The proposed research introduces significant novelty by ...
| | | |
| 70 | https://doi.org/10.3390/biomimetics10090616 | | Xiaoxuan Liu | 2025-09-12T00:00:00.000Z |
| | Path planning can reduce transportation costs, improve the efficiency of goods delivery, and optimize the warehousing and distribution process. Path planning is the core technology in UAV flight and robot navigation to realize intelligent flight and navigation, ensuring that UAVs or robots can reach their destinations safely and efficiently. Therefore, there is an urgent need for each field to explore path-planning technology suitable for reducing expenditure costs and improving work efficiency. Consequently, studying path-planning algorithms has increasingly become a hot research topic. Path-planning algorithms are methods to find optimal paths in a graph or network abstracted from a realistic scenario . We can classify existing path-planning algorithms into two categories: traditional path-planning algorithms and intelligent optimization algorithms. Traditional path planning algorithms mainly include the artificial potential field method , the A* algorithm , Dijkstra's algorithm , the Probabilistic Roadmap (PRM) algorithm , the rapidly expanding random tree (RRT) algorithm , and so on. Although previous researchers have developed many path-planning algorithms, and their mathematical theory has matured significantly, when applied to real path-planning problems, they often need help to avoid falling into local optima and fail to find the optimal path quickly. In recent years, with the development of stochastic search theory, many emerging intelligent optimization algorithms have been developed to overcome this problem and have been successfully applied to path planning problems with promising results. The main ones are the traditional particle swarm algorithm , the genetic algorithm , the newly proposed alpha evolution , escape algorithm , and K-means optimizer .
| | | |
| 122 | http://www.srilankaguardian.org/2024/07/relevance-of-art-of-war-to-drones-and.html | | Sri Lanka Guardian | 2024-07-08T08:11:51.000Z |
| | The daunting threat of swarm warfare is manifested in the following forms. "Know thyself, know thy enemy. A thousand battles, a thousand victories." ~ Sun Tzu, The Art of War Ancient Art of War and Modern Intelligence In the modern context of warfare, what is seemingly evident is the devastation that can be brought about by the use of sophisticated and lethal weaponry. However different these modern catastrophic developments of warfare are from Twentieth Century technology used in the two World Wars, ancient wisdom could still be relevant and applicable in principle. The ancient text "The Art of War" by Sun Tzu has been celebrated for centuries as an exceptional guide to military strategy. Its principles, originally conceived for traditional combat, are equally applicable to contemporary conflict scenarios involving drone warfare and artificial intelligence (AI). Despite originating in an era of swords and spears, Sun Tzu's insights have a timeless relevance that continues to inform strategies in today's high-tech battlefields. A foundational element of Sun Tzu's philosophy is the critical role of information and intelligence. In "The Art of War," he emphasizes the importance of understanding both oneself and the enemy. In the realm of drone warfare and AI, this translates into data collection and analysis. Modern drones, equipped with advanced sensors and cameras, collect real-time data on enemy movements, terrain, and other crucial factors.
| | | |
| 170 | https://www.mdpi.com/2571-5577/1/1/5 | | mdpi.com | 2023-05-29T23:41:13.000Z |
| | Lee, C.K.M.; Chan, H.K.; Choy, K.L.; Zhang, W. Swarm intelligence applied in green logistics: A literature review. 2014, 37, 154 - 169. [ Klumpp, M. Automation and Artificial Intelligence in Business Logistics Systems: Human Reactions and Collaboration Requirements. Int. J. Logist. 2017, 1 - 19. [ Armstrong, S.; Bostrom, N.; Shulman, C. Racing to the precipice: A model of artificial intelligence development. AI Soc. 2016, 31, 201 - 206. [
| | | |
| 172 | https://lifeboat.com/blog/author/shubham-ghosh-roy | | lifeboat.com | 2023-06-05T00:20:26.000Z |
| | How the Human Brain Project Built a Mind of its Own - May 7, 2023 AI plus MRI yields the ability to recognize what the mind is hearing - May 7, 2023 NeaChat Uses OpenAI ChatGPT Version 4 Offering Chinese Users a Cutting-Edge AI Technology Experience - May 6, 2023 Turns out Uranus might be swarmed by deep ocean worlds - May 6, 2023 AI Face Identification Puts Innocent Man In Jail - May 6, 2023 Google "We Have No Moat, And Neither Does OpenAI" - May 6, 2023 How menopause reshapes the brain - May 6, 2023
| | | |
| 209 | https://hashdork.com/swarm-ai/ | | hashdork.com | 2023-12-09T17:23:50.000Z |
| | Consider the swarming behavior of bees to create hives, the formation of bait balls by tiny fish to frighten off larger predator fish, the group hunting behavior of wolves, or the movement of birds ...
| | | |
| 211 | https://doi.org/10.1021/acsami.4c04486 | | Ece Tezsezen | 2024-05-29T00:00:00.000Z |
| | AI models used to design metamaterials can be categorized as supervised, unsupervised, or hybrid models.52 A general subdivision can be generative models and optimization algorithms. Generative ...
| | | |
| 442 | https://doi.org/10.1038/s41598-025-10831-3 | | Udit Mamodiya | 2025-07-10T00:00:00.000Z |
| | By integrating these enhancements, the COMLAT can become an extremely scalable, smart, and industry-grade AI solution for adaptive solar tracking. The addition of multi-agent reinforcement learning,
| | | |
