Percorrer por autor "Nowakowski, Marek"
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- A Comparison of PID Controller Architectures Applied in Autonomous UAV Follow up of UGVPublication . Bonzatto Junior, Luciano; Berger, Guido S.; Braun, João A.; Pinto, Milena F.; Santos, Murillo Ferreira dos; Oliveira Júnior, Alexandre de; Nowakowski, Marek; Costa, Paulo; Wehrmeister, Marco A.; Lima, JoséThe cooperation between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has brought new perspectives and e!ectiveness to production and monitoring processes. In this sense, tracking moving targets in heterogeneous systems involves coordination, formation, and positioning systems between UGVs and UAVs. This article presents a Proportional-Integral-Derivative (PID) control strategy for tracking moving target operations, considering an operating environment between a multirotor UAV and an indoor UGV. Di!erent PID architectures are developed and compared to each other in the Gazebo simulator, whose objective is to analyze the control performance of the UAV when used to track the ground robot based on the identification of the ArUco fiducial marker. Computer vision techniques based on the Robot Operating System (ROS) are integrated into the UAV’s tracking system to provide a visual reference for the aircraft’s navigation system. The results of this study indicate that the PD, Cascade, and Parallel controllers showed similar performance in both trajectories tested, with the Parallel controller showing a slight advantage in terms of mean error and standard deviation, suggesting its suitability for applications that prioritize precision and stability.
- Advance Reconnaissance of UGV Path Planning Using Unmanned Aerial Vehicle to Carry Our Mission in Unknown EnvironmentPublication . Nowakowski, Marek; Berger, Guido S.; Braun, João A.; Mendes, João; Bonzatto Junior, Luciano; Lima, JoséThe utilization of unmanned vehicles for specialized tasks has gained significant attention in both military and civilian domains. This article explores the application of commercial unmanned aerial vehicles (UAVs) for reconnaissance purposes, specifically to verify autonomous driving missions assigned to the developed TAERO manned-unmanned vehicle in field operations. The paper introduces the TAERO vehicle, highlighting its functionality and capabilities for unmanned missions. The architecture of the unmanned ground vehicle (UGV) system is discussed taking into consideration the autonomy subsystem and used location data. The limitations associated with terrain and potential obstacles are addressed as well as importance of acquiring accurate terrain information for successful autonomous operation. The solution proposed in our study involves the use of a commercially available UAV applied to the visual tracking of potential targets in an engagement scenario. Details related to flight route planning system, geolocation, target tracking, and data transmission between robotic platforms are discussed and presented in this work. The acquired real-time data plays a crucial role in confirming the mission, making necessary adjustments, or altering the planned route. The UAV platform, known for its maneuverability and operational capabilities, can operate ahead as a reconnaissance element, improving the overall reconnaissance capabilities of the system. Upon completion of the mission, the UAV can return to the base or land on a moving vehicle platform. The authors proposed integration of a UAV that significantly enhances the autonomous mode capabilities of unmanned ground platform, improving operation in unknown environment during special mission.
- Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational EnvironmentPublication . Nowakowski, Marek; Kurylo, Jakub; Braun, João; Berger, Guido; Mendes, João; Lima, JoséNowadays, there has been a growing interest in the use of mobile robots for various applications,where the analysis of the operational environment is a crucial component to conduct our special tasks ormissions. Themain aimof thiswork was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm(based on YOLO network) to detect objects and estimate their distances using theMiDaS depth model. Initially, the accuracy of distance estimationwas evaluated through video streamtesting in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.