Mission Planning for Long-Term Deployment using Probabilistic Environment Models

使用概率环境模型进行长期部署的任务规划

• 批准号: 2420751
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2420751
• 关键词: Mission; Planning; Long; Term; Deployment

1 Brief description of the content of the research including potential impactAs mobile robots become more advanced and affordable, they are being deployed in increasingly complex monitoring tasks in a variety of environments, from agricultural croplands to oceans and rainforests. In many cases, robots offer enhanced opportunities for environment modelling compared with fixed sensors, as they can cover more ground and remove the need for permanent installation of sensors in the environment. However, this leads to the question of how the robot should decide where to go to acquire the most useful information. My research will aim to develop algorithms that improve the capabilities of robotic monitoring systems, with potential applications in agriculture, ecology, renewable energy and many other areas. 2 Aims and objectivesThe aim of the proposed work is broadly to improve the efficiency of robotic monitoring systems, enabling them to provide more useful information within their operating constraints. Particular objectives to be addressed will include: 1. Designing a framework for spatiotemporal modelling that can be used by robots to capture a rich representation of their environments. A robot deployed in a monitoring context must be able to model its environment in a way that can usefully inform its planning system. 2. Demonstrating efficient informative planning with a rich underlying environment model. Existing work on persistent modelling and informative planning has typically used fairly simple environment models. We look to extend these capabilities to the richer models described above. 3. Incorporating practical constraints such as battery life in an integrated manner, to allow the system to proactively plan around its need to charge. 3 Novelty of the research methodologyRobotic technologies have developed dramatically in recent years and continue to do so, and as suchresearch into robotic monitoring still presents many avenues for further exploration. Much work has been done on the algorithmic foundations of this work, but we hope to both extend these foundations and - importantly - validate them through deployment on real robots, as well as through simulation testing. 4 Alignment to EPSRC strategies and research areasThe proposed work aligns strongly with the Robotics research area described by the EPSRC. Thisresearch area acknowledges the potential for robotics research to contribute in the near future to disruptive technologies in many major industrial sectors, including automotive, aerospace, nuclear,oil and gas, agriculture, space, manufacturing, defence and construction. This research area, and theproposed work, are also strongly connected with the Artificial Intelligence Technologies area,within which one of the focuses is toward development of autonomous and intelligent technologies.5 Companies or collaborators involvedThe proposed work is supported by Amazon Web Services (AWS)

随着移动的机器人变得越来越先进和负担得起，他们正在部署在各种环境中越来越复杂的监测任务，从农田到海洋和热带雨林。在许多情况下，与固定传感器相比，机器人为环境建模提供了更多的机会，因为它们可以覆盖更多的地面，并且不需要在环境中永久安装传感器。然而，这导致了机器人应该如何决定去哪里获取最有用的信息的问题。我的研究将旨在开发提高机器人监测系统能力的算法，在农业，生态，可再生能源和许多其他领域具有潜在的应用。2目的和目标拟议工作的目的是广泛地提高机器人监测系统的效率，使它们能够在其操作限制范围内提供更多有用的信息。具体目标将包括：1.设计一个时空建模的框架，可以被机器人用来捕捉其环境的丰富表示。部署在监控环境中的机器人必须能够以一种可以有效地通知其规划系统的方式对其环境进行建模。 2.利用丰富的底层环境模型演示高效的信息规划。现有的持久建模和信息规划工作通常使用相当简单的环境模型。我们希望将这些功能扩展到上述更丰富的模型。3.以集成的方式消除实际限制，例如电池寿命，以允许系统主动规划其充电需求。 3研究方法的新奇机器人技术近年来有了巨大的发展，并将继续发展，因此，对机器人监测的研究仍然为进一步探索提供了许多途径。在这项工作的算法基础上已经做了很多工作，但我们希望扩展这些基础，并且-重要的是-通过部署在真实的机器人上以及通过模拟测试来验证它们。4与EPSRC的战略和研究领域保持一致拟议的工作与EPSRC描述的机器人研究领域保持一致。该研究领域承认机器人研究在不久的将来为许多主要工业部门的颠覆性技术做出贡献的潜力，包括汽车，航空航天，核能，石油和天然气，农业，太空，制造业，国防和建筑。该研究领域和拟议的工作也与人工智能技术领域密切相关，其中一个重点是自主和智能技术的开发。5参与的公司或合作者拟议的工作由亚马逊网络服务（AWS）支持。