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A Virtual Spring-Damper Approach for UAV Swarm Formation and Decentralised Collision Avoidance

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A Virtual Spring-Damper Approach for UAV Swarm Formation and...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Luis Mejias Pedro Arias-Perez Javier Melero-Deza Pascual Campoy The School of Electrical Engineering and Robotics Queens-land University of Technology (QUT) Brisbane QLD Australia The Computer Vision and Aerial Robotics Group Centre for Automation and Robotics (C.A.R.) Universidad Politecnica de Madrid (UPM-CSIC) Madrid Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper introduces a method for controlling UAV swarm formations and avoiding collisions using the Virtual Spring-Damper (VSD) approach. This approach draws upon classical mechanical spring-damper systems, employing displacement and velocities to generate attractive and repulsive forces. These forces help sustain swarm formation and prevent collisions in a decentralized manner. Extensive simulations provided a systematic approach to obtain the parameters involved in the spring-damper system. Flight tests were carried out to verify the proposed method. The experimental outcomes align with our simulations, demonstrating effectiveness in collision avoidance while preserving a specified flight formation.

关键词： Systematics Autonomous aerial vehicles Trajectory Collision avoidance Aircraft Drones Testing

E-NeRF: Neural Radiance Fields from a Moving Event Camera

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arXiv 2022年

作者： Klenk, Simon Koestler, Lukas Scaramuzza, Davide Cremers, Daniel Computer Vision Group Technical University of Munich Germany Robotics and Perception Group University of Zurich Switzerland

Estimating neural radiance fields (NeRFs) from "ideal" images has been extensively studied in the computer vision community. Most approaches assume optimal illumination and slow camera motion. These assumptions are often violated in robotic applications, where images may contain motion blur, and the scene may not have suitable illumination. This can cause significant problems for downstream tasks such as navigation, inspection, or visualization of the scene. To alleviate these problems, we present E-NeRF, the first method which estimates a volumetric scene representation in the form of a NeRF from a fast-moving event camera. Our method can recover NeRFs during very fast motion and in high-dynamic-range conditions where frame-based approaches fail. We show that rendering high-quality frames is possible by only providing an event stream as input. Furthermore, by combining events and frames, we can estimate NeRFs of higher quality than state-of-the-art approaches under severe motion blur. We also show that combining events and frames can overcome failure cases of NeRF estimation in scenarios where only a few input views are available without requiring additional regularization. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA

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arXiv 2023年

作者： Baczmanski, MacIej Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics Agh University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by both high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 watts, compared to the minimum of 55 watts for weaker CPUs, and the small size (119mm x 140mm x 36mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than 97% of mAP (mean average precision) for object detection and above 90% of mIoU (mean intersection over union) score for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

Lo-Fri: Lidar Odometry Based on Filtered Range Images

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SSRN

SSRN 2023年

作者： Velasco-Sánchez, Edison P. Muñoz-Bañón, Miguel Á. Candelas-Herias, Francisco A. Puente-Mendez, Santiago T. Torres-Medina, Fernando AUROVA Group of Automation Robotics and Computer Vision University of Alicante San Vicente del Raspeig S/N Alicante03690 Spain

Robotic localization in unstructured outdoor environments is increasingly in need of low-bias and computationally efficient odometry methods. In this paper, we propose a LiDAR odometry process based on filtered range images with point-to-edge and point-to-plane ICP matching. This method separates edges, surfaces and ground features from a point cloud with gradient image filtering methods. Besides, we present a filter based on a Fourier mask that separates the fundamental frequencies of the image that correspond to the ground plane. The evaluation results generate a translation and rotation error of 0.86% and 0.0036°/m on the KITTI training data set and a runtime of 78 ms. In addition, the system was tested on a UGV with a Velodyne VLP-16 sensor, yielding an average closed-loop error of 0.8 m and a runtime of 27 ms over eight loops covering 3.5 km. © 2023, The Authors. All rights reserved.

关键词： Optical radar

LiDAR-based drone navigation with reinforcement learning

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TechRxiv

TechRxiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Drones

LiDAR-based drone navigation with reinforcement learning

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arXiv 2023年

关键词： Robot Operating System

Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA

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arXiv 2022年

作者： Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

This paper presents a system for hardware-in-the-loop (HiL) simulation of unmanned aerial vehicle (UAV) control algorithms implemented on a heterogeneous SoC FPGA computing platforms. The AirSim simulator running on a PC and an Arty Z7 development board with a Zynq SoC chip from AMD Xilinx were used. Communication was carried out via a serial USB link. An application for autonomous landing on a specially marked landing strip was selected as a case study. A landing site detection algorithm was implemented on the Zynq SoC platform. This allowed processing a 1280 × 720 @ 60 fps video stream in real time. Performed tests showed that the system works correctly and there are no delays that could negatively affect the stability of the control. The proposed concept is characterised by relative simplicity and low implementation cost. At the same time, it can be applied to test various types of high-level perception and control algorithms for UAV implemented on embedded platforms. We provide the code developed on GitHub, which includes both Python scripts running on the PC and C code running on Arty Z7. © 2022, CC BY.

关键词： System-on-chip

DualQuat-LOAM: LiDAR Odometry and Mapping parametrized on Dual Quaternions

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arXiv 2024年

作者： Velasco-Sánchez, Edison P. Recalde, Luis F. Li, Guanrui Candelas-Herias, Francisco A. Puente-Mendez, Santiago T. Torres-Medina, Fernando AUROVA Group of Automation Robotics and Computer Vision University of Alicante San Vicente del Raspeig S/N Alicante03690 Spain Worcester Polytechnic Institute Worcester01609 United States

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039°/m, with an average run time of 53 ms. © 2024, CC BY-NC-ND.

关键词： robotics

Towards real-time and energy efficient Siamese tracking - a hardware-software approach

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TechRxiv

TechRxiv 2022年

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is energy consuming, and thus cannot be used in many low-power applications. To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA). In this work, we propose a hardware-software implementation of the well-known fully connected Siamese tracker (SiamFC). We have developed a quantised Siamese network for the FINN accelerator, using algorithm-accelerator co-design, and performed design space exploration to achieve the best efficiency-to-energy ratio (determined by FPS and used resources). For our network, running in the programmable logic part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost 50 frames-per-second with tracker accuracy on par with its floating point counterpart, as well as the original SiamFC network. The complete tracking system, implemented in ARM with the network accelerated on FPGA, achieves up to 17 fps. These results bring us towards bridging the gap between the highly accurate but energy-demanding algorithms and energy-efficient solutions ready to be used in low-power, edge systems. © 2022, CC BY.

关键词： Field programmable gate arrays (FPGA)