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Posts
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portfolio
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publications
Towards Operating Wind Turbine Inspections using a LiDAR-equipped UAV
Published in arxiv, 2023
In this study, a novel technique for the autonomous visual inspection of rotating wind turbine rotor blades utilizing an unmanned aerial vehicle (UAV) was developed. This approach addresses the challenges presented by the dynamic environment at hand and the requirement of maintaining a safe distance from the moving rotor blades. The application of UAV-based inspection techniques mitigates these dangers and the expenses associated with traditional wind turbine inspection methods which involve halting normal wind farm operations. Our proposed system incorporates algorithms and sensor technologies. It relies on a light detection and ranging (LiDAR) sensor system, an inertial measurement unit, and a GPS to accurately identify the relative position of the rotating wind turbine with respect to the UAV " s own position. Once this position is determined, a non-destructive visual analysis of the rotating rotor blades is performed by generating a suitable trajectory and triggering a camera fitted on a gimbal system as the blades approach. This new technique, built upon the existing research on UAV inspection of rotating wind turbines, has been empirically validated using data collected from real-world wind farm applications. This article contributes to the ongoing trend of enhancing the safety and efficiency of infrastructure inspection. It also presents a good base for future research, with potential applications for other types of infrastructure, such as bridges or power lines.
Recommended citation: Sikora, T., Markovic, L., & Bogdan, S. (2023). Towards operating wind turbine inspections using a lidar-equipped uav. arXiv preprint arXiv:2306.14637.
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Development of a Dynamic Multi-object Planning Framework for Autonomous Mobile Robots
Published in MoStart 2024, 2023
Recent advancements in autonomous mobile robots (AMRs), such as drones, ground vehicles, and quadrupedal robots, have significantly impacted inspection, emergency response, and surveillance fields. While a large body of work covers topics addressing static scenarios, working with dynamic points of interest remains relatively problematic. The nature of the problem brings with it the real time adaptability challenges, efficient decision making, and uncertainty. Available literature mostly concentrates on research-oriented specialized tools running controlled experiments. However, there is a lack of comprehensive frameworks supporting coverage of scenarios dealing with non stationary objects. This paper introduces a multi-object planning framework for autonomous mobile robots operating with dynamic points of interest. The framework integrates open source software and low level frameworks to simulate multiple agents moving on unknown trajectories. Advanced planning algorithms can be deployed and tested in simulation, as well as the real world AMRs thanks to the software-in-the-loop (SITL) approach. The architecture uses ROS for high level communication, Ardupilot for interfacing with the vehicle hardware, and Gazebo for the physics simulation. Core modules allow configuring various dynamic agents and implementing various planning algorithms. As a proof of the system capabilities, use cases of ship inspection and agriculture monitoring using a UAV are presented. The resulting framework can serve as a basis for research, education, and deployment purposes on the topic of advanced planning algorithms for AMRs.
Recommended citation: Sikora, T., & Papic, V. (2024, April). Development of a Dynamic Multi-object Planning Framework for Autonomous Mobile Robots. In International Conference on Digital Transformation in Education and Artificial Intelligence Application (pp. 215-228). Cham: Springer Nature Switzerland.
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Learning Trajectory Tracking for an Autonomous Surface Vehicle in Urban Waterways
Published in MDPI Computation, 2023
Roboat is an autonomous surface vessel (ASV) for urban waterways, developed as a research project by the AMS Institute and MIT. The platform can provide numerous functions to a city, such as transport, dynamic infrastructure, and an autonomous waste management system. This paper presents the development of a learning-based controller for the Roboat platform with the goal of achieving robustness and generalization properties. Specifically, when subject to uncertainty in the model or external disturbances, the proposed controller should be able to track set trajectories with less tracking error than the current nonlinear model predictive controller (NMPC) used on the ASV. To achieve this, a simulation of the system dynamics was developed as part of this work, based on the research presented in the literature and on the previous research performed on the Roboat platform. The simulation process also included the modeling of the necessary uncertainties and disturbances. In this simulation, a trajectory tracking agent was trained using the proximal policy optimization (PPO) algorithm. The trajectory tracking of the trained agent was then validated and compared to the current control strategy both in simulations and in the real world.
Recommended citation: Sikora, T., Schiphorst, J. K., & Scattolini, R. (2023). Learning Trajectory Tracking For An Autonomous Surface Vehicle In Urban Waterways. Computation, 11(11), 216.
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Survey of Path Planning for Aerial Drone Inspection of Multiple Moving Objects
Published in MDPI Drones, 2024
Recent advancements in autonomous mobile robots (AMRs), such as aerial drones, ground vehicles, and quadrupedal robots, have significantly impacted the fields of infrastructure inspection, emergency response, and surveillance. Many of these settings contain multiple moving elements usually neglected in the planning process. While a large body of work covers topics addressing scenarios with stationary objects, promising work with dynamic points of interest has only recently gained traction due to computational complexity. The nature of the problem brings with it the challenges of motion prediction, real time adaptability, efficient decision-making, and uncertainty. Concerning aerial drones, while significantly constrained computationally, good understanding and the relative simplicity of their platform gives way to more complex prediction and planning algorithms needed to work with multiple moving objects. This paper presents a survey of the current state-of-the-art solutions to the path planning problem for multiple moving object inspection using aerial drones. The presented algorithms and approaches cover the challenges of motion and intention prediction, obstacle avoidance, planning in dynamic environments, as well as scenarios with multiple agents. Potential solutions and future trends were identified primarily in the form of heuristic and learning methods, state-of-the-art probabilistic prediction algorithms, and further specialization in regard to every scenario.
Recommended citation: Sikora, T.; Papić, V. Survey of Path Planning for Aerial Drone Inspection of Multiple Moving Objects. Drones 2024, 8, 705. https://doi.org/10.3390/drones8120705
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Databases: laboratory exercises
Undergraduate course, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023
This is a description of a teaching experience. You can use markdown like any other post.
Theory of Systems: laboratory exercises
Undergraduate course, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023
This is a description of a teaching experience. You can use markdown like any other post.
Computer Graphics: laboratory exercises
Graduate course, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023
This is a description of a teaching experience. You can use markdown like any other post.
Introduction into computers and programming: laboratory exercises
Undergraduate course, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2023
This is a description of a teaching experience. You can use markdown like any other post.