Sarkar et al., 2020 - Google Patents
Real-time object processing and routing for intelligent drones: A novel approachSarkar et al., 2020
View PDF- Document ID
- 11157472936485213391
- Author
- Sarkar S
- Totaro M
- Kumar A
- Elgazzar K
- Publication year
- Publication venue
- Computer
External Links
Snippet
We propose a real-time object-processing and smart-routing system using an unmanned aerial vehicle-mounted camera and an object-recognition algorithm to determine the image coordinates of object locations to calculate the shortest route between coordinates. This …
- 238000004422 calculation algorithm 0 abstract description 24
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Choi et al. | Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art | |
Aggarwal et al. | Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges | |
Wei et al. | An improved method based on deep reinforcement learning for target searching | |
Devo et al. | Autonomous single-image drone exploration with deep reinforcement learning and mixed reality | |
Park et al. | Vision-based obstacle avoidance for UAVs via imitation learning with sequential neural networks | |
Bouvry et al. | Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios | |
Hentati et al. | Mobile target tracking mechanisms using unmanned aerial vehicle: Investigations and future directions | |
Lei et al. | A bio-inspired neural network approach to robot navigation and mapping with nature-inspired algorithms | |
Huang et al. | Multi-uav collision avoidance using multi-agent reinforcement learning with counterfactual credit assignment | |
Sarkar et al. | Real-time object processing and routing for intelligent drones: A novel approach | |
Hassan et al. | Applications of Machine Learning in UAV Networks | |
Khamis et al. | Deep learning for unmanned autonomous vehicles: A comprehensive review | |
Yang et al. | Smart autonomous moving platforms | |
Cosar | Artificial Intelligence Technologies and Applications Used in Unmanned Aerial Vehicle Systems | |
Pisarenko et al. | The Structure of the Information Storage “CONTROL_TEA” for UAV Applications | |
Chen et al. | Integrated air-ground vehicles for uav emergency landing based on graph convolution network | |
Zhao et al. | Deep-learning based autonomous-exploration for UAV navigation | |
Palácios et al. | Evaluation of mobile autonomous robot in trajectory optimization | |
Xia et al. | Intelligent Method for UAV Navigation and De-confliction--Powered by Multi-Agent Reinforcement Learning | |
Xiao et al. | Vision-based learning for drones: A survey | |
Lebedev et al. | Analysis of «Leader–Followers» Algorithms in Problem of Trajectory Planning for a Group of Multi-rotor UAVs | |
Boulares et al. | UAV path planning algorithm based on Deep Q-Learning to search for a floating lost target in the ocean | |
Pouya et al. | Performing active search to locate indication of ancient water on mars: An online, probabilistic approach | |
Pradhan et al. | Artificial intelligence empowered models for UAV communications | |
Kumar | On Maximising the Total Information Gain in a Vehicle Routing Problem |