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Heterogeneous multi-robot systems offer the potential to support complex missions, such as those needed for persistent autonomy in underwater domains. Such systems enable each robot to be optimised for specific tasks to better manage dynamic situations. In this context, temporal planning can generate plans to support the execution of multi-robot missions. However, the task distribution quality in the generated plans is often poor due to the strategies that existing planners employ to search for suitable actions, which do not tend to optimise task allocation. In this paper, we propose a new algorithm called the Decentralised Heterogeneous Robot Task Allocator (DHRTA) which enhances goal distribution by considering task spatial distribution, execution time, and the capabilities of the available robots. DHRTA is the first phase of our decentralised planning strategy which supports individual robot plan generation using temporal planners. Experiments illustrate the robustness of the approach and indicate improvements in plan quality by reducing the planning time, mission time and the rate of mission failures.

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The oil and gas industry has increased its efforts in preventing and mitigating risks associated with loss of well control and loss of primary containment. Initiatives have primarily focused on increasing the awareness and education of field and operations personnel. The foundation of this strategy rests on the belief that increased awareness of threats, risks and enhanced training will lower well control risks and eliminate well control events. Despite this renewed focus, industry data show that well control events and high-potential near-misses have not diminished. In addition, findings from incident investigations point to human-factor-related causes, including lack of procedural discipline, non-compliance errors and cognitive errors. For organizations to deliver flawless execution at the wellsite while effectively preventing or mitigating well control or loss of primary containment events, a robust closed-loop methodology that leverages smart risk detection and mitigation systems must be employed. This paper analyzes the critical process safety requirements in the industry and provides solutions centered on a smart integrated digital platform. This platform, built on technologies such as precursor sensor and alarming technologies, barrier and equipment health monitoring, wellsite performance analysis, sophisticated workflow management and video and audio analytics, will effectively coordinate and manage these dynamic risks. A data management workflow that uses automated risk assessments, threat detection, structured and nonstructured contextual data will minimize the impacts of human factors and drive operational efficiency, process assurance, reduction in nonproductive time and project cost. These enhancements will enable global organizations to proactively drive effective risk management of well control and loss of primary containment events. This paper will explore the application, methodology and value of this smart, integrated digital platform through the presentation of case studies.

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