Payload Optimization in Drone Operations

The most important criterion in the design of unmanned air vehicles is to successfully complete given task and consume minimum energy meantime. This paper presents a comparison performances metaheuristic methods such as Particle Swarm Optimization (PSO) Grey Wolf (GWO) controllers DC/DC buck converters for optimizing consumption path following error PEM fuel cell-powered quadrotor system. Hence, system consists two PSO- GWO-based optimizers. Optimizer I used determining parameters PD controller, which minimizing route-tracking error. On other hand, controller values converters’ components are determined by II minimize voltage-tracking errors converters. Both optimizers work together try tracking while also power using suitable objective functions. Simulation results demonstrate effectiveness enhancing efficiency improving quadrotor’s flight stability. For step inputs, optimized shows better performance according time domain criteria rise settling time. However, PSO-based 24.707% overshoot. 10.8866% less observed efficient increases 18% complex route involving ramp inputs. Then, 3... Read More

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A maneuverable flight apparatus for tracking aerial targets using fixed cameras instead of gimbaled cameras. The apparatus has a housing with fixed, non-moving cameras at two locations. One camera faces the flight direction, the other is offset. Rolling the apparatus shifts the target view between them. This enables wide field-of-view tracking without gimbals or cooling. It saves weight, improves aerodynamics, and reduces launch prep time compared to gimbaled cameras.

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A device and method for accurately and efficiently determining the center of gravity (CG) of a remote controlled aircraft to balance it for better flight performance. The device has a base with slots for movable platforms to hold digital CG sensors. The aircraft wheels are placed on the sensors to measure mass at each location. The CG is calculated using the measured masses, wheel distances, and subtracted manufacturer CG. This allows adjusting the aircraft's mass to match the calculated CG for optimal balance. The base is leveled and wings aligned before measurement.

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Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation path planning critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel approach that integrates management into rolling horizon framework UAV planning. We introduce an enhanced Particle Swarm Optimization (PSO) algorithm, incorporating adaptive perturbation strategies local search mechanism based on simulated annealing, optimize assignments routes. The enables the system adapt evolving demands over time. Energy consumption is explicitly modeled, accounting flight, computation, recharging at designated stations, ensuring practical applicability. Extensive simulations demonstrate proposed method reduces makespan significantly compared conventional static approaches, effectively balancing usage requirements. These results highlight potential of our real-world operations settings.

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Applying AI rules during an inspection mission to reduce the size of collected inspection data before uploading it to the cloud. Edge devices collect inspection data using AI models for defect detection. To avoid sending large datasets with variations and noise, the edge devices apply AI rules to evaluate samples. If a sample meets all rules, it's included in the final set. If not, it's excluded. This refined set is uploaded, preserving cloud and edge resources.

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Packages adapted for use with drones to enable safe transport and retrieval of goods using aerial vehicles. The packages have collapsible or nestable configurations to reduce size for storage and transport by drones. They also have handles for attaching to drones to secure and lower the packages. This allows drones to carry and deliver the packages by lowering them instead of landing, improving safety and efficiency compared to landing and unloading. The packages can be collapsible sheets, nested containers, or pouches that can be compactly stored and transported on drones, then expanded and secured to the drone for delivery.

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A moving body like a drone that can transport objects without affecting their stability during flight. The drone has a holding mechanism with a rotation unit that allows the object to be held horizontally from the side near or above the center of gravity of the transport unit. This configuration stabilizes the object's orientation during flight even if the drone tilts or turns, preventing it from tipping over or spilling contents. The rotation unit allows the object to stay level as the drone moves.

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Flight control system for electric aircraft that optimizes flight configurations to alleviate loads and improve performance. The system calculates loads based on flight conditions, determines optimized configurations to distribute loads, and generates commands to actuate aircraft effectors like propellers. The aim is to balance loads across the aircraft structure, reduce gyroscopic moments, and prevent backdriving of electric actuators.

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Optimizing drone delivery efficiency by dynamically routing drones to intermediate locations to reduce delivery distances and times. The technique involves determining an optimal intermediate location based on historical payload delivery data. The drone is sent to this location instead of directly to the final destination. Notifications are sent to nearby recipients requesting the same payload. If a request is received, the drone navigates to the final location. This reduces total distance traveled compared to direct delivery. The technique improves efficiency by leveraging payload clustering to minimize last mile delivery.

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Advanced package delivery system for unmanned aerial vehicles (UAVs) that enables efficient, precise, and user-friendly delivery of multiple packages of varying sizes and weights to different destinations in a single flight operation. The system uses transformable frame systems, automated software controls, and smart devices to facilitate storage, transport, and release of packages from UAVs. It allows UAVs to deliver multiple packages to different locations in a single flight by utilizing internal frames that connect to an external frame on the UAV. Packages are stored and released from the internal frames. A drop container with a selectively openable top scans package identifiers and automatically opens to receive the package. The UAV calculates delivery trajectories and releases packages once verified proximate to targets. Users can track packages and interact with system components via a smart device app.

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Autonomous agricultural treatment delivery system using drones and robots to precisely identify and treat individual plants in a farm. The system uses computer vision, AI, and robotics to navigate through crops, identify specific plants, and apply treatments like fertilizer or pesticide directly to them instead of spraying entire areas. The drones have cameras to detect plants, calculate trajectories, and move autonomously. They carry cartridges of treatments that can be refilled in-situ. This enables targeted and optimized farming with reduced waste, chemical, and water usage compared to broad spraying.

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Drone system for optimized delivery of payloads like soil amendments based on ground conditions. The drone has separate bays for different payloads like oolitic aragonite and fertilizer. The drone controller determines ground attributes at the delivery location, derives a payload ratio based on that, and releases the appropriate amounts of each payload from the bays. This allows flexible and optimized deployment of complex interacting payloads like soil amendments based on specific ground conditions.

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Multi-purpose drone for cargo loading that can transport cargo and generate information about the cargo at the same time. The drone has a detachable winch at the bottom to load cargo and a weight sensor to measure the weight. It also has sensors to detect obstacles and a controller to stop the drone if it gets too close. The cargo container inside the drone has partition walls to prevent shifting during flight. The drone body has sensors to measure distance and compare against a reference to prevent collisions. This allows safe cargo transport while monitoring weight.

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Intelligent cargo loading and transportation system for large unmanned aerial vehicles (UAVs) that allows automated, efficient, and safe cargo handling. The system involves installing a storage compartment near the nose of the UAV cabin and a delivery port below the belly. Cargo components are moved using side and bottom guide rails with electric locks. Two-way locks slide on horizontal rails to move components left or right. One-way locks slide on inclined rails to move cargo forward. This allows components to be fixed, locked, and transported autonomously within the UAV belly.

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Adjusting the center of gravity of a transport unmanned helicopter to improve endurance and load capacity without sacrificing weight like traditional counterweights. The system uses a movable pod below the helicopter, fuel storage inside the helicopter, and a center of gravity adjustment device between the frame and landing gear. The pod can be moved to balance cargo weight. An auxiliary fuel tank inside replaces external counterweights. This allows adjusting center of gravity using cargo and fuel instead of fixed weights.

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Enabling safe and efficient drone operations with payloads that vary in weight and characteristics. The drone's controller receives payload data, predicts flight response based on payload mass, and modifies commands to prevent unsafe maneuvers. It also calibrates flight parameters with the payload attached. This improves drone performance and prevents crashes when carrying different payloads. The drone also exchanges identification data with the payload for verification.

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An automated system for a drone to pick up payloads without human assistance. The system uses a ground-based apparatus with a funnel and channel. The drone lowers a retriever mechanism that slides down the funnel into the channel and grabs the payload. The drone can then lift the payload, disengaging it from the apparatus.

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Method for unmanned aerial vehicles (drones) to precisely unload goods into an autonomous vehicle's storage box. The drone analyzes an image of the vehicle to recognize a marker inside the box. It adjusts its position based on the marker's location until it is within a threshold distance from the box center. Then, if the goods have a suitable handling attribute, the drone lowers them on a wire into the open box and releases the grip. If not, it releases the grip in mid-air above the box. The marker is normally hidden under the closed lid, but the drone can request the vehicle to open it via wireless communication to expose the marker.

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An attachment mechanism secures packages to drones for delivery. The mechanism has a support plate that attaches to the top of a package and an extended handle. The handle has an opening and bridge that can be grasped by a drone's payload retriever. This allows the drone to lift and transport the package while keeping the payload enclosed. The package attachment avoids the need for external dangling straps or hooks.

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Using multiple drones in a coordinated fashion to lift, maneuver, and transport payloads. The drones communicate with each other to dynamically adjust their movements in response to changes in the payload. This allows more efficient and scalable lifting compared to using a single drone or multiple drones with separate operators. The drones can autonomously cooperate or be remotely controlled by a command center. The modular system allows using drones with varying lifting capabilities to match payload size instead of overprovisioning with a single heavy-lifting drone.

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