1,720,977 research outputs found
Design and Control of a Cooperative System of an Autonomous Surface Vehicle and a Remotely Operated Vehicle (ASV-ROV)
No full textMarine robotics plays a fundamental role in executing various underwater complex missions. In this scenario tethered Remotely Operated Vehicles (ROVs) offer benefits such as efficient data transmission and strong physical connection for emergency cases, but their motion is confined by the length of the tether. This study addresses the issue of extending the range of the ROV by implementing a new control strategy cooperating with an Autonoumous Surface Vehicle (ASV). The proposed system ASV-ROV confronts two main challenges: the constrained movement of the ROV and the risk of cable entanglement. Therefore, a new control system is built to provide the ROV a smooth motion and avoid situations that are likely to generate cable entanglements. The new control system enables the ASV to align and to keep a certain distance from the ROV, regulating at the same time the released cable length in the water. The paper addresses the design of ASV-ROV cooperative system, the modelling of each vehicle, the control strategy, and simulation results. Future work research is also outlined at the end
DexROV project: Control Framework for Underwater Interaction Tasks
No full textAbstract:
In this work, the control framework of the DexROV Horizon 2020 project is presented. The framework is based on the task priority concept, extended by the authors to allow the activation and deactivation of tasks. The general concepts of control objectives, task and actions are given. The execution of a pipeline's weld inspection is used as study case to test the proposed framework in a simulation setting
Protocol-Driven A* Algorithm for Fast ASV Motion Planning in Dynamic Scenarios
No full textThis paper proposes a new local path planner for Autonomous Surface Vehicles that integrates navigation rules into the planning process. The planner operates within a three-level architecture designed for the whole motion planning process, from global path creation to real-time course adjustments. This integration aims to improve efficiency while complying with maritime collision avoidance regulations. Previous attempts to integrate the 'Convention on the International Regulations for Preventing Collisions at Sea' rules relied on reactive solutions, leading to frequent course changes and inefficiency. The planner incorporates these regulations within the A*-based algorithm of the architecture's second level. It receives obstacle data and plans a path to the next global waypoint. The correct maneuvers are determined based on the obstacle's approaching angle, and the A* algorithm's nodes are selected to ensure compliance while searching for the fastest route. Planned path safety is verified using a geometric ray-tracing algorithm that estimates future obstacle positions and checks for potential collisions. The planner was evaluated using simulations and field tests with the ULISSE autonomous catamaran. The experiments demonstrate the system's robustness in handling uncertainties and noise, while correctly avoiding dynamic obstacles
Autonomous Deep Sea Mining Exploration: The EU ROBUST Project Control Framework
No full textThis paper presents the control framework developed within the Horizon 2020 ROBUST project aimed at building an autonomous system for exploring deep sea mining sites. First, the Autonomous Underwater Vehicle analyzes the initial zone of interest with the aim of finding a sub area with the highest probability to contain a manganese nodule field. When such an area is found, a low altitude survey is performed. When a possible nodule is detected, the vehicle lands on the seafloor, allowing a dedicated sensor mounted on the manipulator's end-effector to perform the nodule analysis.This work presents the ROBUST control framework and the task priority based kinematic used for its implementation. In addition, software in the loop simulation, dry and pool test results are shown to validate the control framework addressed
ROBUST project: Control Framework for Deep Sea Mining Exploration
No full textThis paper presents the control framework under
development within the ROBUST Horizon 2020 project, whose
goal is the development of an autonomous robotic system for
the exploration of deep-sea mining sites. After a bathymetric
survey of the initial zone of interest, the robotized system selects
a subarea deemed to have the most chances of containing a
manganese nodule field and proceeds with a detailed low altitude
survey. Whenever a possible nodule is found, it performs an insitu
measurement through laser induced spectroscopy. To do so,
the underwater vehicle must first land on the seafloor, with a
certain precision to allow a subsequent fixed-based manipulation,
bringing its manipulator endowed with the laser system in
the position to carry out the measurement. The work reports
the developed control architecture and the simulation results
supporting it
UAV teams in emergency scenarios: A summary of the work within the project PRISMA
No full textIn recent years autonomous robots, and Unmanned Aerial Vehicles (UAVs) in particular, are becoming always more important in the context of emergency scenarios, being able to anticipate the actions of human operators and to support them during rescue operations. In this context, the investigation of strategies for the autonomous control of UAVs, for the development of Human-Swarm Interfaces and for the coverage of large areas is crucial. All these aspects have been analyzed within the Italian project PRISMA, and they will be here summarized
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