Pedro Patron

Path Planning for Autonomous Underwater Vehicles

Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process

Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

Semantic knowledge-based representation for improving situation awareness in service oriented agents of autonomous underwater vehicles

This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability, among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specificially impact om mission flexibility robustness and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different types must be proceed by (and run through) several different layers to be finally available in a suited format and at the right place to be acccesible by high-level decision making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the incipient fault diagnosis agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

The importance of trust between operator and AUV: Crossing the human/computer language barrier

AUVs have many advantages over traditional remote presence systems, but their autonomy introduces problems of its own. A deficit in trust may arise between the operator and an AUV which is able to modify its own plan and potentially behave in a not observably deterministic fashion. There have been some notable previous attempts to bring the plan used in an autonomous system closer to the user, but AUVs represent a relatively new technology which operates in a particular and limited domain for which this infrastructure does not yet exist. Here we present a framework to bring state and purpose of the mission as close to the user as possible. Natural language and high level graphics, which take influence from computer games in order to engender familiarity, are used as as key components of both the input and output of our suggested plan creation system. This allows the user to satisfy themselves of the validity of a plan and the competence of the system before it is run on a real AUV.

An Augmented Reality Architecture for the Creation of Hardware-in-the-Loop & Hybrid Simulation Test Scenarios for Unmanned Underwater Vehicles

System evaluation and testing of unmanned underwater vehicles (UUVs) in their destined environment can be tedious, error prone, time consuming and consequently, expensive. However, pre-real world testing facilities, such as Hard- ware-in-the-loop (HIL), are not always available. This is due to the time and expense required to create a specific test environment for the vehicle. Thus the system is not as fault tolerant as it could be since problems can remain undetected until the real- world testing phase. Debugging and fixing errors in the real- world testing phase is much more time consuming and expensive due to the nature of the harsh underwater environment. This paper introduces a novel framework for the rapid construction of virtual environment testing scenarios for remote platforms with embedded systems such as AUVs. The framework provides testing facilities across all stages of the reality continuum providing capabilities for pure simulation, HIL, Hybrid Simulation (HS) and Real world testing. The framework architecture is both very generic and flexible and allows mixing of real and simulated components. The framework is supported by a distributed communications protocol to provide location transparency of systems which is key to providing mixed reality testing facilities.

Adaptive mission plan diagnosis and repair for fault recovery in autonomous underwater vehicles

This paper proposes a novel approach for autonomous mission diagnosis and repair for maintaining operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach uses the potential of ontology reasoning in order to orient the planning algorithms adapting the mission plan of the vehicle. It can handle uncertainty and action scheduling in order to maximize mission efficiency and minimise mission failures due to external or unexpected factors. Its performance is presented in a set of simulated scenarios. The paper concludes by showing the results of a trial demonstration. Observations of different environmental and internal parameters are simulated in a REMUS 100 AUV while performing a mission. These trigger a knowledge exchange between the diagnosis monitor agent and the adaptive mission planner embedded agent. Based on the observed data and the original knowledge, the experiment shows how the adaptive planner system is able to identify the gaps in the mission and adapt the platforms mission plan accordingly.

Fault tolerant adaptive mission planning with semantic knowledge representation for autonomous underwater vehicles

This paper proposes a novel approach for autonomous mission plan recovery for maintaining operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach uses the potential of ontology reasoning in order to orient the planning algorithms adapting the mission plan of the vehicle. It can handle uncertainty and action scheduling in order to maximize mission efficiency and minimise mission failures due to external unexpected factors. Its performance is presented in a set of simulated scenarios for different concepts of operations for the underwater domain. The paper concludes by showing the results of a trial demonstration carried out on a real underwater platform. The results of this paper are readily applicable to land and air robotics.

Augmented Reality and Data Fusion techniques for Enhanced Situational Awareness of the Underwater Domain

System evaluation of AUV embedded technologies via the standard hardware-in-the-loop (HIL) method is based solely on the outcome of the actual embedded system. This limits the amount of testing that can be done before real world trials of the platform. This paper proposes a novel framework for rapid evaluation and integration of embedded technologies for AUVs. The proposed framework uses the concepts of Augmented Reality (AR) and data fusion providing functionality beyond HIL. Testing can be executed across all stages of the reality continuum providing capabilities for pure simulation, HIL, hybrid simulation (HS) and real world testing. This is achieved by a generic framework of components consisting of both real and simulated components. The framework is supported by a distributed communications protocol implemented by all components both on and off the embedded platform.

Interoperability of agent capabilities for autonomous knowledge acquisition and decision making in unmanned platforms

Robotic platforms are helping marine applications to gain routine and permanent access to the underwater environment. However, challenges related to this domain require the development and integration of more evolved embedded tools that can raise the platforms autonomy levels while maintaining the trust of the operator. This paper shows how knowledge representation, reasoning and planning tools can provide the required interoperability between embedded agents to achieve high-level mission goals described by the operator.

MIRIAM: a multimodal chat-based interface for autonomous systems

We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous systeMs), a multimodal interface to support situation awareness of autonomous vehicles through chat-based interaction. The user is able to chat about the vehicles plan, objectives, previous activities and mission progress. The system is mixed initiative in that it pro-actively sends messages about key events, such as fault warnings. We will demonstrate MIRIAM using SeeBytes SeeTrack command and control interface and Neptune autonomy simulator.