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MPCC++: Model Predictive Contouring control for Time-Optimal Flight with Safety Constraints

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arXiv 2024年

作者： Krinner, Maria Romero, Angel Bauersfeld, Leonard Zeilinger, Melanie Carron, Andrea Scaramuzza, Davide Institute for Dynamics Systems and Control ETH Zurich Switzerland Robotics and Perception Group University of Zurich Switzerland

Quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring control (MPCC) has emerged as a promising model-based approach for time optimization problems such as drone racing. However, the standard MPCC formulation used in quadrotor racing introduces the notion of the gates directly in the cost function, creating a multi-objective optimization that continuously trades off between maximizing progress and tracking the path accurately. This paper introduces three key components that enhance the state-of-the-art MPCC approach for drone racing. First and foremost, we provide safety guarantees in the form of a track constraint and terminal set. The track constraint is designed as a spatial constraint which prevents gate collisions while allowing for time optimization only in the cost function. Second, we augment the existing first principles dynamics with a residual term that captures complex aerodynamic effects and thrust forces learned directly from real-world data. Third, we use Trust Region Bayesian Optimization (TuRBO), a state-of-the-art global Bayesian Optimization algorithm, to tune the hyperparameters of the MPCC controller given a sparse reward based on lap time minimization. The proposed approach achieves similar lap times to the best-performing RL policy and outperforms the best model-based controller while satisfying constraints. In both simulation and real world, our approach consistently prevents gate crashes with 100% success rate, while pushing the quadrotor to its physical limits reaching speeds of more than 80km/h. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

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arXiv 2025年

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © 2025, CC BY.

关键词： System-on-chip

Equivariant IMU Preintegration with Biases: a Galilean group Approach

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arXiv 2024年

作者： Delama, Giulio Fornasier, Alessandro Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discrete-time formulation of the IMU preintegration on Gal(3) ⋈ gal(3), the left-trivialization of the tangent group of the Galilean group Gal(3). We define a novel preintegration error that geometrically couples the navigation states and the bias leading to lower linearization error. Our method improves in consistency compared to existing preintegration approaches which treat IMU biases as a separate state-space. Extensive validation against state-of-the-art methods, both in simulation and with real-world IMU data, implementation in the Lie++ library, and open-source code are provided. Copyright © 2024, The Authors. All rights reserved.

关键词： Inertial navigation systems

An Equivariant Approach to Robust State Estimation for the ArduPilot Autopilot System

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An Equivariant Approach to Robust State Estimation for the A...

IEEE International Conference on robotics and Automation (ICRA)

作者： Alessandro Fornasier Yixiao Ge Pieter van Goor Martin Scheiber Andrew Tridgell Robert Mahony Stephan Weiss Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The majority of commercial and open-source autopilot software for uncrewed aerial vehicles rely on the tried and tested extended Kalman filter (EKF) to provide the state estimation solution for the inertial navigation system (INS). While modern implementations achieve remarkable robustness, it is often due to the careful implementation of exception code for a multitude of corner cases along with significant skilled tuning effort. In this paper, we use the data wealth of the ArduPilot community to identify and highlight the most common real-world challenges in INS state estimation, including sensor self-calibration, robustness in static conditions, global navigation satellite system (GNSS) outliers and shifts, and robustness to faulty inertial measurement units (IMUs). We propose a novel equivariant filter (EqF) formulation for the INS solution that exploits a Semi-Direct-Bias symmetry group for multi-sensor fusion with self-calibration capabilities and incorporates equivariant velocity-type measurements. We augment the filter with a simple innovation-covariance inflation strategy that seamlessly handles GNSS outliers and shifts without requiring coding of a whole set of exception cases. We use real-world data from the Ardupilot community to demonstrate the performance of the proposed filter on known cases where existing filters fail without careful exception handling or case-specific tuning and benchmark against the ArduPilot’s EKF3, the most sophisticated EKF implementation currently available.

关键词： Global navigation satellite system Low-pass filters Inertial navigation Robot sensing systems Autonomous aerial vehicles Robustness Software

Output-feedback-based affine formation manoeuvre control of multi-agent systems applying negative imaginary systems theory

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Output-feedback-based affine formation manoeuvre control of ...

IEEE Conference on Decision and control

作者： Yu-Hsiang Su Parijat Bhowmick Alexander Lanzon Department of EEE IIT Guwahati Assam India Department of EEE Control Systems and Robotics Group University of Manchester UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper exploits the Negative Imaginary systems theory to develop a novel affine formation manoeuvre control framework for multi-agent systems using dynamic output feedback. The framework begins by deriving affine transformation matrices for leader agents, enabling dynamic adjustments from the nominal formation to the target formation. An output-feedback distributed Strictly Negative Imaginary control law is then proposed for follower agents to achieve affine formation manoeuvres. Unlike existing affine formation manoeuvre control schemes, which typically rely on full-state feedback (including both position and velocity measurements), the proposed approach requires only relative position measurements. In addition, it offers more freedom in choosing a dynamic controller transfer function, thereby improving formation tracking performance. A comprehensive simulation case study is provided to test the effectiveness of the proposed output-feedback-based affine formation manoeuvre control framework.

关键词： Transfer functions Position measurement control systems Velocity measurement Multi-robot systems Output feedback Multi-agent systems

Novel Disturbance Observer Relevant Parametric System Identification Based on Robust Stability Criterion

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Novel Disturbance Observer Relevant Parametric System Identi...

2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Jung, Hanul Tacx, Paul Oomen, Tom Oh, Sehoon Dgist Department of Robotics Engineering Daegu42988 Korea Republic of Eindhoven University of Technology Control Systems Technology Group Department of Mechanical Engineering P.O. Box 513 Eindhoven5600 MB Netherlands

ISBN: (纸本)9798350335446

In this paper, novel parametric identification is addressed by the disturbance observer (DOB) relevant algorithm. To fully utilize the disturbance observer, a proper nominal model, which is a parametric model, is essential. The control performance of the DOB can be checked based on the robust stability criterion, which is related to the modeling error and the desired bandwidth of the DOB. Consequently, it is beneficial to evaluate the parametric model in the system identification procedure. Therefore, this paper proposes a novel disturbance observer-relevant system identification algorithm based on the robust stability criterion of the disturbance observer. The effectiveness of the proposed method is validated by showing the numerical simulations under specific conditions. © 2023 IEEE.

关键词： Stability criteria

Detection-segmentation convolutional neural network for autonomous vehicle perception

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arXiv 2023年

作者： Baczmanski, Maciej Synoczek, Robert Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor – the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://***/vision-agh/MMAR_2023. © 2023, CC BY.

关键词： Autonomous vehicles

Detection-segmentation convolutional neural network for autonomous vehicle perception

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TechRxiv

TechRxiv 2023年

作者： Baczmanski, Maciej Synoczek, Robert Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： Autonomous vehicles

High-definition event frame generation using SoC FPGA devices

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arXiv 2023年

作者： Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD – 1280 × 720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods. © 2023, CC BY.

关键词： System-on-chip