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FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy

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arXiv 2025年

作者： Čapek, David Hrnčíř, Jan Báča, Tomáš Jirkal, Jakub Vonásek, Vojtěch Pěnička, Robert Saska, Martin Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV open-source simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments. © 2025, CC BY-NC-ND.

关键词： Flight simulators

Real-time Planning of Minimum-time Trajectories for Agile UAV Flight

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arXiv 2024年

作者： Teissing, Krystof Novosad, Matej Penicka, Robert Saska, Martin Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

We address the challenge of real-time planning of minimum-time trajectories over multiple waypoints, onboard multirotor UAVs. Previous works demonstrated that achieving a truly time-optimal trajectory is computationally too demanding to enable frequent replanning during agile flight, especially on less powerful flight computers. Our approach overcomes this stumbling block by utilizing a point-mass model with a novel iterative thrust decomposition algorithm, enabling the UAV to use all of its collective thrust, something previous point-mass approaches could not achieve. The approach enables gravity and drag modeling integration, significantly reducing tracking errors in high-speed trajectories, which is proven through an ablation study. When combined with a new multi-waypoint optimization algorithm, which uses a gradient-based method to converge to optimal velocities in waypoints, the proposed method generates minimum-time multi-waypoint trajectories within milliseconds. The proposed approach, which we provide as open-source package, is validated both in simulation and in real-world, using Nonlinear Model Predictive Control. With accelerations of up to 3.5g and speeds over 100 km/h, trajectories generated by the proposed method yield similar or even smaller tracking errors than the trajectories generated for a full multirotor model. © 2024, CC BY.

关键词： Open source software

Model Predictive Path Integral Control for Agile Unmanned Aerial Vehicles

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Model Predictive Path Integral Control for Agile Unmanned Ae...

IEEE/RSJ International Conference on Intelligent robots and systems (IROS)

作者： Michal Minařík Robert Pěnička Vojtěch Vonásek Martin Saska Multi-Robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km h −1 and acceleration close to 20 m s −2 , while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight.

关键词： Computational modeling Predictive models Autonomous aerial vehicles Cost function Real-time systems Trajectory Nonlinear dynamical systems Collision avoidance Vehicle dynamics Drones

Energy-aware multi-UAV Coverage Mission Planning with Optimal Speed of Flight

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arXiv 2024年

作者： Datsko, Denys Nekovar, Frantisek Penicka, Robert Saska, Martin The Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

This paper tackles the problem of planning minimum-energy coverage paths for multiple UAVs. The addressed multi-UAV Coverage Path Planning (mCPP) is a crucial problem for many UAV applications such as inspection and aerial survey. However, the typical path-length objective of existing approaches does not directly minimize the energy consumption, nor allows for constraining energy of individual paths by the battery capacity. To this end, we propose a novel mCPP method that uses the optimal flight speed for minimizing energy consumption per traveled distance and a simple yet precise energy consumption estimation algorithm that is utilized during the mCPP planning phase. The method decomposes a given area with boustrophedon decomposition and represents the mCPP as an instance of multiple Set Traveling Salesman Problem with a minimum energy objective and energy consumption constraint. The proposed method is shown to outperform state-of-the-art methods in terms of computational time and energy efficiency of produced paths. The experimental results show that the accuracy of the energy consumption estimation is on average 97% compared to real flight consumption. The feasibility of the proposed method was verified in a real-world coverage experiment with two UAVs. © 2024, CC BY.

关键词： Energy utilization

Model Predictive Path Integral Control for Agile Unmanned Aerial Vehicles

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arXiv 2024年

作者： Minařík, Michal Pěnička, Robert Vonásek, Vojtěch Saska, Martin The Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km h−1 and acceleration close to 20 m s−2, while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllers

MASPA: An efficient strategy for path planning with a tethered marsupial robotics system

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arXiv 2024年

作者： Capitán, Jesús Díaz-Báñez, José M. Pérez-Cutiño, Miguel A. Rodríguez, Fabio Ventura, Inmaculada Multi-robot & Control Systems group University of Seville Spain Department of Applied Mathematics II University of Seville Spain

A tethered marsupial robotics system comprises three components: an Unmanned Ground Vehicle (UGV), an Unmanned Aerial Vehicle (UAV), and a tether connecting both robots. Marsupial systems are highly beneficial in industry as they extend the UAV’s battery life during flight. This paper introduces a novel strategy for a specific path planning problem in marsupial systems, where each of the three components must avoid collisions with ground and aerial obstacles modeled as 3D cuboids. Given an initial configuration in which the UAV is positioned atop the UGV, the goal is to reach an aerial target with the UAV. We assume that the UGV first moves to a position from which the UAV can take off and fly through a vertical plane to reach an aerial target. We propose an approach that discretizes the space to approximate an optimal solution, minimizing the sum of the lengths of the ground and air paths. First, we assume a taut tether and use a novel algorithm that leverages the convexity of the tether and the geometry of obstacles to efficiently determine the locus of feasible take-off points for the UAV. We then apply this result to scenarios that involve loose tethers. The efficiency of our method enables real-time decision-making, making it suitable for use in emergency situations where quick responses are crucial. The simulation test results show that our approach can solve complex situations in seconds, outperforming a baseline planning algorithm based on RRT* (Rapidly exploring Random Trees). © 2024, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

Collaborative Object Manipulation on the Water Surface by a UAV-USV Team Using Tethers

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arXiv 2024年

作者： Novák, Filip Báča, Tomáš Saska, Martin Multi-robot Systems Group Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

This paper introduces an innovative methodology for object manipulation on the surface of water through the collaboration of an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) connected to the object by tethers. We propose a novel mathematical model of a robotic system that combines the UAV, USV, and the tethered floating object. A novel Model Predictive Control (MPC) framework is designed for using this model to achieve precise control and guidance for this collaborative robotic system. Extensive simulations in the realistic robotic simulator Gazebo demonstrate the system’s readiness for real-world deployment, highlighting its versatility and effectiveness. Our multi-robot system overcomes the state-of-the-art single-robot approach, exhibiting smaller control errors during the tracking of the floating object’s reference. Additionally, our multi-robot system demonstrates a shorter recovery time from a disturbance compared to the single-robot approach. Copyright © 2024, The Authors. All rights reserved.

关键词： multipurpose robots

Collaborative Object Manipulation on the Water Surface by a UAV-USV Team Using Tethers

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Collaborative Object Manipulation on the Water Surface by a ...

IEEE/RSJ International Conference on Intelligent robots and systems (IROS)

作者： Filip Novák Tomáš Báča Martin Saska Multi-Robot Systems Group Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

关键词： Collaboration Predictive models Autonomous aerial vehicles Mathematical models multi-robot systems Vehicle dynamics Intelligent robots Predictive control

Drones Guiding Drones: Cooperative Navigation of a Less-Equipped Micro Aerial Vehicle in Cluttered Environments

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Drones Guiding Drones: Cooperative Navigation of a Less-Equi...

IEEE/RSJ International Conference on Intelligent robots and systems (IROS)

作者： Václav Pritzl Matouš Vrba Yurii Stasinchuk Vít Krátký Jiří Horyna Petr Štěpán Martin Saska Department of Cybernetics Faculty of Electrical Engineering Multi-Robot Systems Group Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reliable deployment of Unmanned Aerial Vehicles (UAVs) in cluttered unknown environments requires accurate sensors for Global Navigation Satellite System (GNSS)-denied localization and obstacle avoidance. Such a requirement limits the usage of cheap and micro-scale vehicles with constrained payload capacity if industrial-grade reliability and precision are required. This paper investigates the possibility of offloading the necessity to carry heavy sensors to another member of the UAV team while preserving the desired capability of the smaller robot intended for exploring narrow passages. A novel cooperative guidance framework offloading the sensing requirements from a minimalistic secondary UAV to a superior primary UAV is proposed. The primary UAV constructs a dense occupancy map of the environment and plans collision-free paths for both UAVs to ensure reaching the desired secondary UAV’s goals even in areas not accessible by the bigger robot. The primary UAV guides the secondary UAV to follow the planned path while tracking the UAV using Light Detection and Ranging (LiDAR)-based relative localization. The proposed approach was verified in real-world experiments with a heterogeneous team of a 3D LiDAR-equipped primary UAV and a micro-scale camera-equipped secondary UAV moving autonomously through unknown cluttered GNSS-denied environments with the proposed framework running fully on board the UAVs.

关键词： Location awareness Accuracy Three-dimensional displays Navigation Service robots Autonomous aerial vehicles robot sensing systems Sensors Reliability Collision avoidance