Software Porting of a 3D Reconstruction Algorithm to Razorcam Embedded System on Chip

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a UAV with tight constraints of size, weight, and power. The potential impacts will be seen through the continuation of this research in the Smart ES lab at University of Arkansas

Key technologies for safe and autonomous drones

Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems

The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Embedded Vision Systems: A Review of the Literature

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems

Bio-Inspired Optic Flow Sensors for Artificial Compound Eyes.

Compound eyes in flying insects have been studied to reveal the mysterious cues of vision-based flying mechanisms inside the smallest flying creatures in nature. Especially, researchers in the robotic area have made efforts to transfer the findings into their less than palm-sized unmanned air vehicles, micro-air-vehicles (MAVs). The miniaturized artificial compound eye is one of the key components in this system to provide visual information for navigation. Multi-directional sensing and motion estimation capabilities can give wide field-of-view (FoV) optic flows up to 360 solid angle. By deciphering the wide FoV optic flows, relevant information on the self-status of flight is parsed and utilized for flight command generation. In this work, we realize the wide-field optic flow sensing in a pseudo-hemispherical configuration realized by mounting a number of 2D array optic flow sensors on a flexible PCB module. The flexible PCBs can be bent to form a compound eye shape by origami packaging. From this scheme, the multiple 2D optic flow sensors can provide a modular, expandable configuration to meet low power constraints. The 2D optic flow sensors satisfy the low power constraint by employing a novel bio-inspired algorithm. We have modified the conventional elementary motion detector (EMD), which is known to be a basic operational unit in the insect’s visual pathways. We have implemented a bio-inspired time-stamp-based algorithm in mixed-mode circuits for robust operation. By optimal partitioning of analog to digital signal domains, we can realize the algorithm mostly in digital domain in a column-parallel circuits. Only the feature extraction algorithm is incorporated inside a pixel in analog circuits. In addition, the sensors integrate digital peripheral circuits to provide modular expandability. The on-chip data compressor can reduce the data rate by a factor of 8, so that it can connect a total of 25 optic flow sensors in a 4-wired Serial Peripheral Interface (SPI) bus. The packaged compound eye can transmit full-resolution optic flow data through the single 3MB/sec SPI bus. The fabricated 2D optic flow prototype sensor has achieved the power consumption of 243.3pJ/pixel and the maximum detectable optic flow of 1.96rad/sec at 120fps and 60 FoV.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108841/1/sssjpark_1.pd

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Navigation of Unmanned Aerial Vehicles in GPS-denied Environments

Ph.DDOCTOR OF PHILOSOPH