PANDORA - Persistent Autonomy Through Learning, Adaptation, Observation and Replanning★

Lane, David M.; Maurelli, Francesco; Kormushev, Petar; Carreras, Marc; Fox, Maria; Kyriakopoulos, Kostas J.

doi:10.1016/j.ifacol.2015.06.039

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…In the PANDORA framework [57], ontologies are used as a way for the robot to organise the knowledge about the world, not just in geometric concepts, but attaching a semantic label. In particular, a diagnostic module has been developed in order to detect a thruster failure, with a link between the degrees of freedom of the vehicle and the executable actions.…”

Section: Assessment Of Robotic Ontology Applied To Reliabilitymentioning

confidence: 99%

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

et al. 2021

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A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

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Section: Assessment Of Robotic Ontology Applied To Reliabilitymentioning

confidence: 99%

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

et al. 2021

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“…Semantic Web for Robots is an ontology implemented by [75] for robotic components and uses a collaborative knowledge acquisition system for encoding information about robotic devices. In the PANDORA framework [76], ontologies are used as a means for the robot to organize knowledge about the world, not just in geometric concepts, but by attaching a semantic label. The project aimed to demonstrate the challenges of integrating autonomous inspection of an underwater structure, autonomous location, cleaning and inspection of an anchor chain and autonomous manipulation of a valve from an undocked vehicle.…”

Section: Robotic System Modelling For Reliabilitymentioning

confidence: 99%

Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms

Mitchell¹,

Blanche

Zaki

et al. 2021

IEEE Access

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To reduce Operation and Maintenance (O&M) expenditure on offshore wind farms, wherein 80% of the cost relates to deploying personnel, the offshore wind sector looks to advances in Robotics and Artificial Intelligence (RAI) for solutions. Barriers to residential Beyond Visual Line of Sight (BVLOS) autonomy as a service, include operational challenges in run-time safety compliance, reliability and resilience, due to the complexities of dealing with known and unknown risk in dynamic environments. In this paper we incorporate a Symbiotic System Of Systems Approach (SSOSA) that uses a Symbiotic Digital Architecture (SDA) to provide a cyber physical orchestration of enabling technologies. Implementing a SSOSA enables Cooperation, Collaboration and Corroboration (C 3 ), as to address run-time verification of safety, reliability and resilience during autonomous missions. Our SDA provides a means to synchronize distributed digital models of the robot, environment and infrastructure. Through the coordinated bidirectional communication network of the SDA, the remote human operator has improved visibility and understanding of the mission profile. We evaluate our SSOSA in an asset inspection mission within a confined operating environment. Demonstrating the ability of our SSOSA to overcome safety, reliability and resilience challenges. The SDA supports lifecycle learning and co-evolution with knowledge sharing across the interconnected systems. Our results evaluate both sudden and gradual faults, as well as unknown events, that may jeopardize an autonomous mission. Using distributed and coordinated decision making, SSOSA enhances the analysis of the mission status, which includes diagnostics of critical sub-systems within the resident robot. This evaluation demonstrates that the SSOSA provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. SSOSA has the potential to be a highly transferable methodology to other mission scenarios and technologies, providing a pathway to implementing scalable autonomy as a service.

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“…Learning a model of the state transitions allows the planner to take advantage of previously unknown methods for accomplishing a goal. Lane et al () proposed teaching a hybrid ROV/AUV skills (i.e., a procedure of primitive actions for the onboard deliberative planner to use) by providing a supervised learning algorithm with vehicle data recorded during a piloted demonstration.…”

Section: Meeting the End User Requirementsmentioning

confidence: 99%

Review of mission planning for autonomous marine vehicle fleets

Thompson

Guihen

2018

Journal of Field Robotics

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The deployment of a fleet of autonomous marine vehicles (AMVs) allows for the parallelisation of missions, intervehicle support for longer deployment times, adaptability and redundancy to in situ mission changes, and effective use of the right vehicle for the right purpose. End users and operators of AMVs face challenges in planning complex missions due to the limitations of their vehicles, dynamic, operationally constrictive, and unstructured environments, and in minimising risks to equipment, the mission, and personnel. Automated mission planning for AMV fleets can be a tool to reduce the complexity of programming vehicle tasking, and to perform validity assessments for end user-specified goals, allowing the operator to focus on risk assessment. We present a critical review of the current advances in automated planning for AMV fleets, investigating the limitations of available state-ofthe-art tools and providing a road map of the goals and challenges based on analysis of field reports and end user initiatives.

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PANDORA - Persistent Autonomy Through Learning, Adaptation, Observation and Replanning★

Cited by 14 publications

References 12 publications

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms

Review of mission planning for autonomous marine vehicle fleets

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