58 research outputs found
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
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Simulation Analysis of Exploration Strategies and UAV Planning for Search and Rescue
Aerial scans with unmanned aerial vehicles (UAVs) are becoming more widely
adopted across industries, from smart farming to urban mapping. An application
area that can leverage the strength of such systems is search and rescue (SAR)
operations. However, with a vast variability in strategies and topology of
application scenarios, as well as the difficulties in setting up real-world
UAV-aided SAR operations for testing, designing an optimal flight pattern to
search for and detect all victims can be a challenging problem. Specifically,
the deployed UAV should be able to scan the area in the shortest amount of time
while maintaining high victim detection recall rates. Therefore, low
probability of false negatives (i.e., high recall) is more important than
precision in this case. To address the issues mentioned above, we have
developed a simulation environment that emulates different SAR scenarios and
allows experimentation with flight missions to provide insight into their
efficiency. The solution was developed with the open-source ROS framework and
Gazebo simulator, with PX4 as the autopilot system for flight control, and YOLO
as the object detector
Vision-based Safe Autonomous UAV Docking with Panoramic Sensors
The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked
concerns about safety measures during their missions. To advance towards safer
autonomous aerial robots, this work presents a vision-based solution to
ensuring safe autonomous UAV landings with minimal infrastructure. During
docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper,
we propose the use of a single omnidirectional panoramic camera pointing
upwards from a landing pad to detect and estimate the position of people around
the landing area. The images are processed in real-time in an embedded
computer, which communicates with the onboard computer of approaching UAVs to
transition between landing, hovering or emergency landing states. While
landing, the ground camera also aids in finding an optimal position, which can
be required in case of low-battery or when hovering is no longer possible. We
use a YOLOv7-based object detection model and a XGBooxt model for localizing
nearby people, and the open-source ROS and PX4 frameworks for communication,
interfacing, and control of the UAV. We present both simulation and real-world
indoor experimental results to show the efficiency of our methods
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