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Towards a hybrid software architecture and multi-agent approach for autonomous robot software

机译：朝着自主机器人软件的混合软件架构和多智能辅导方法

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摘要

To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.
机译：为了支持动态环境中的自主机器人的强大计划，自主机器人软件应包括应对现实世界环境不变状态的适应性和无功功能。然而，传统的软件架构，如感觉模型计划行为和基于行为的范例，不足以满足要求。在感应模型计划范式中行动过程中缺乏感知使得软件对运行时常规的反应缓慢，而基于行为的架构通常在规划远程步骤并进行优化的计划适应时缩短。本文提出了一种混合软件架构，可在动态环境中保持机器人行为的适应性和反应性。为了实现这种架构，我们进一步提出了称为autorobot的多代理开发框架，该框架将机器人软件视为一个多代理系统，其中各种代理角色协作以实现软件功能。为了展示我们具体框架和软件架构的适用性和有效性，我们进行实验以实施典型的案例，例如，一个自主拾取和射击偏远客人菜肴的机器人，这需要机器人计划和导航一种高度动态的环境，可以使其行为适应意外情况。

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