Mind the gap: Robotic Mission Planning Meets Software Engineering

M. Askarpour, C. Menghi, Gabriele Belli, M. Bersani, Patrizio Pelliccione
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引用次数: 5

Abstract

In the context of robotic software, the selection of an appropriate planner is one of the most crucial software engineering decisions. Robot planners aim at computing plans (i.e., blueprint of actions) to accomplish a complex mission. While many planners have been proposed in the robotics literature, they are usually evaluated on showcase examples, making hard to understand whether they can be effectively (re)used for realising complex missions, with heterogeneous robots, and in real-world scenarios. In this paper we propose ENFORCE, a framework which allows wrapping FM-based planners into comprehensive software engineering tools, and considers complex robotic missions. ENFORCE relies on (i) realistic maps (e.g, fire escape maps) that describe the environment in which the robots are deployed; (ii) temporal logic for mission specification; and (iii) Uppaal model checker to compute plans that satisfy mission specifications. We evaluated ENFORCE by analyzing how it supports computing plans in real case scenarios, and by evaluating the generated plans in simulated and real environments. The results show that while ENFORCE is adequate for handling single-robot applications, the state explosion still represents a major barrier for reusing existing planners in multi-robot applications.
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注意差距:机器人任务规划与软件工程
在机器人软件环境中,选择合适的规划器是最关键的软件工程决策之一。机器人规划者的目标是计算计划(即行动蓝图)来完成复杂的任务。虽然机器人文献中提出了许多规划器,但它们通常是在展示示例中进行评估的,这使得很难理解它们是否可以有效地(重新)用于实现复杂的任务,使用异构机器人,以及在现实世界的场景中。在本文中,我们提出了一个框架,它允许将基于fm的计划器包装到综合的软件工程工具中,并考虑复杂的机器人任务。enforcement依赖于(i)描述机器人部署环境的逼真地图(例如,消防逃生地图);任务规格的时间逻辑;(三)乌帕尔模型检查器,以计算满足任务规格的计划。我们通过分析它在真实场景中如何支持计算计划,以及在模拟和真实环境中评估生成的计划来评估ENFORCE。结果表明,虽然强制执行足以处理单机器人应用,但状态爆炸仍然是在多机器人应用中重用现有规划器的主要障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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