2,073 research outputs found
Simultaneous Trajectory Estimation and Mapping for Autonomous Underwater Proximity Operations
Due to the challenges regarding the limits of their endurance and autonomous
capabilities, underwater docking for autonomous underwater vehicles (AUVs) has
become a topic of interest for many academic and commercial applications.
Herein, we take on the problem of state estimation during an autonomous
underwater docking mission. Docking operations typically involve only two
actors, a chaser and a target. We leverage the similarities to proximity
operations (prox-ops) from spacecraft robotic missions to frame the diverse
docking scenarios with a set of phases the chaser undergoes on the way to its
target. We use factor graphs to generalize the underlying estimation problem
for arbitrary underwater prox-ops. To showcase our framework, we use this
factor graph approach to model an underwater homing scenario with an active
target as a Simultaneous Localization and Mapping problem. Using basic AUV
navigation sensors, relative Ultra-short Baseline measurements, and the
assumption of constant dynamics for the target, we derive factors that
constrain the chaser's state and the position and trajectory of the target. We
detail our front- and back-end software implementation using open-source
software and libraries, and verify its performance with both simulated and
field experiments. Obtained results show an overall increase in performance
against the unprocessed measurements, regardless of the presence of an
adversarial target whose dynamics void the modeled assumptions. However,
challenges with unmodeled noise parameters and stringent target motion
assumptions shed light on limitations that must be addressed to enhance the
accuracy and consistency of the proposed approach.Comment: 19 pages, 14 figures, submitted to the IEEE Journal of Oceanic
Engineerin
Topologic Maps for Robotic Exploration of Underground Flooded Mines
The mapping of confined environments in mobile robotics is traditionally tackled in dense occupancy maps, requiring large amounts of storage. For some use cases, such as the exploration of flooded mines, the use of dense maps in processing slow down processes like path generation. I introduce a method of generating topological maps in constrained spaces such as mines. By taking a structure with fewer points, traversal and storage of explored space can be made more efficient, avoiding com plex graphs generated by methods like RRT and it’s variants. It’s simpler structure also allows for more intuitive human-machine interactions with it’s fewer points. I also introduce an autonomous frontier-based exploration approach to generate the topological map during exploration, taking advantage of it’s traversal to navigate through known space. With this work, simulation tests show it is possible to success fully extract a simpler graph structure describing the topology during autonomous exploration and that this structure is robust through explored regionsO mapeamento de ambientes confinados em robótica móvel, é tradicionalmente abordado em mapas densos de ocupação, necessitando de grandes quantidades de armazenamento. Para certos casos, tal como a exploração de minas submersas, o uso de mapas densos no processamento, atrasa processos como geração de caminhos. Utilizando uma estrutura com menos pontos, a travessia e o armazenamento de espaço explorado tornam-se mais eficientes, evitando grafos complexos gerados por métodos como RRT e variantes. A sua estrutura mais simples permite também interações homem-máquina com o seu número reduzido de pontos. Introduzo também uma abordagem autónoma de exploração baseada em fronteiras, para gerar o mapa topo lógico durante a exploração, tirando vantagem da travessia do mesmo para navegar por espaço conhecido. Com este trabalho, testes em simulação mostram ser possÃvel extrair uma estrutura sob forma de grafo, descrevendo a topologia ao longo de explorações autónomas e que esta estrutura é robusta para a travessia em regiões explorada
Real-Time Passive Acoustic Tracking of Underwater Vehicles
Com o crescente interesse na exploração oceânica, sistemas de localização subaquática têm sido largamente usados pela industria e comunidade cientifica. Neste trabalho foi desenvolvido um sistema de localização acústica passiva em tempo real, com uma topologia idêntica ao do ultra-short baseline. Este sistema calcula a posição a duas dimensões de uma fonte acústica submersa conhecida, com base na integração de medições da direção do som ao longo do tempo. O ângulo de chegada da onda sonora é estimado pelo atraso de fase entre os sinais adquiridos por dois hidrofones colocados perto um do outro. Esta configuração permite atenuar as diferenças nos sinais recebidos devidas a perturbações do canal acústico subaquático. Este algoritmo foi implementado em tempo real numa plataforma SoC reconfigurável (CPU ARM + FPGA), e validado com ensaios de campo realizados no mar
Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves
Underwater caves are challenging environments that are crucial for water
resource management, and for our understanding of hydro-geology and history.
Mapping underwater caves is a time-consuming, labor-intensive, and hazardous
operation. For autonomous cave mapping by underwater robots, the major
challenge lies in vision-based estimation in the complete absence of ambient
light, which results in constantly moving shadows due to the motion of the
camera-light setup. Thus, detecting and following the caveline as navigation
guidance is paramount for robots in autonomous cave mapping missions. In this
paper, we present a computationally light caveline detection model based on a
novel Vision Transformer (ViT)-based learning pipeline. We address the problem
of scarce annotated training data by a weakly supervised formulation where the
learning is reinforced through a series of noisy predictions from intermediate
sub-optimal models. We validate the utility and effectiveness of such weak
supervision for caveline detection and tracking in three different cave
locations: USA, Mexico, and Spain. Experimental results demonstrate that our
proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring
good generalization performance while offering 10+ FPS on single-board (Jetson
TX2) devices
Rendezvous Planning for Multiple Autonomous Underwater Vehicles using a Markov Decision Process
Multiple Autonomous Underwater Vehicles (AUVs) are a potential alternative to conventional large manned vessels for mine countermeasure (MCM) operations. Online mission planning for cooperative multi-AUV network often relies or predefined contingency on reactive methods and do not deliver an optimal end-goal performance. Markov Decision Process (MDP) is a decision-making framework that allows an optimal solution, taking into account future decision estimates, rather than having a myopic view. However, most real-world problems are too complex to be represented by this framework. We deal with the complexity problem by abstracting the MCM scenario with a reduced state and action space, yet retaining the information that defines the goal and constraints coming from the application. Another critical part of the model is the ability of the vehicles to communicate and enable a cooperative mission. We use the Rendezvous Point (RP) method. The RP schedules meeting points for the vehicles throughput the mission. Our model provides an optimal action selection solution for the multi-AUV MCM problem. The computation of the mission plan is performed in the order of minutes. This quick execution demonstrates the model is feasible for real-time applications
Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images
We introduce a multi-sensor navigation system for autonomous surface vessels
(ASV) intended for water-quality monitoring in freshwater lakes. Our mission
planner uses satellite imagery as a prior map, formulating offline a
mission-level policy for global navigation of the ASV and enabling autonomous
online execution via local perception and local planning modules. A significant
challenge is posed by the inconsistencies in traversability estimation between
satellite images and real lakes, due to environmental effects such as wind,
aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we
specifically modelled these traversability uncertainties as stochastic edges in
a graph and optimized for a mission-level policy that minimizes the expected
total travel distance. To execute the policy, we propose a modern local planner
architecture that processes sensor inputs and plans paths to execute the
high-level policy under uncertain traversability conditions. Our system was
tested on three km-scale missions on a Northern Ontario lake, demonstrating
that our GPS-, vision-, and sonar-enabled ASV system can effectively execute
the mission-level policy and disambiguate the traversability of stochastic
edges. Finally, we provide insights gained from practical field experience and
offer several future directions to enhance the overall reliability of ASV
navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv
admin note: text overlap with arXiv:2209.1186
- …