Player tracking is a useful tool for tactical analysis and performance evaluation in soccer, providing valuable insights into player movements and team dynamics. This project investigates the feasibility of tracking p...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
Player tracking is a useful tool for tactical analysis and performance evaluation in soccer, providing valuable insights into player movements and team dynamics. This project investigates the feasibility of tracking players using UAVcaptured imagery, employing both YOLO and traditional image processing algorithms (TIPA). Initial validation focuses on robot soccer players due to their predictable and controllable movements. The comparative analysis considers processing time, computational cost, adaptability to environmental changes, sensitivity to lighting variations, ability to handle dynamic conditions, tracking accuracy, and real-time performance. Results indicate that, under equivalent hardware and preparation time conditions, YOLO achieves performance comparable to traditional techniques. Nonetheless, the selection of the most suitable approach should be guided by task-specific demands, available computational resources, and the time allocated for system development and deployment.
This paper presents an adaptive control strategy for cooperative cargo transportation using quadcopters in a tan-dem configuration. By modeling the payload and the unmanned aerial vehicles (UAVs) as a unified rigid-bo...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
This paper presents an adaptive control strategy for cooperative cargo transportation using quadcopters in a tan-dem configuration. By modeling the payload and the unmanned aerial vehicles (UAVs) as a unified rigid-body system, the proposed framework addresses the dynamic interactions among them, ensuring robust performance in tasks such as cargo transportation. The system uses an underactuated adaptive control approach, capable of dealing with variations in payload weight while maintaining stability and agility during flight. The proposed strategy is validated through numerical experiments, demonstrating its effectiveness in trajectory tracking tasks. The results show the system's ability to adapt the parameters of the system modeling to the observed and measured values, guaranteeing the tracking of the proposed trajectory and the robust payload handling. This work contributes to the development of cooperative aerial cargo transportation systems, with applications in transportation missions that exceed the individual capacity of each UAV.
Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires ...
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In conjunction with the growth of automated warehouses, a logistical problem also increases. The automated inventory counting problem emerges from the difficulty of managing the products of these large distribution ce...
In conjunction with the growth of automated warehouses, a logistical problem also increases. The automated inventory counting problem emerges from the difficulty of managing the products of these large distribution centers. Usually, these centers have long corridors and high shelves with many different products. To solve this problem, this work proposes an approach of a highly-scalable low-cost plug-and-play multirobot system for inventory management. Our approach is composed of a set that includes a micro-drone, an embedded camera module, and a ground mobile robot. In our tests, two situations are analyzed: first with one heterogeneous multirobot system set and a second situation with two heterogeneous multi-robot system sets. The results demonstrate the advantage of having the interconnected multi-robot system to reduce the time of the inventory management task.
Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires ...
Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.
Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geomet...
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ISBN:
(数字)9781665405935
ISBN:
(纸本)9781665405942
Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.
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