Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot’s coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot.

可重构机器人被部署用于需要区域覆盖的应用，例如清洁和检查。除了一小组预定义形状之外，根据上下文进行重新配置对于区域覆盖性能至关重要。然而，现有的可重构机器人的区域覆盖方法并不总是有效，需要改进以确定预期目标。因此，本文提出了一种基于内部演练的新型覆盖策略，以提高可重构机器人的区域覆盖性能。在这方面，可重构机器人具有在执行动作之前预测其动作结果的认知能力。遗传算法使用内部演练的结果来确定一组机器人的覆盖参数，包括定位、航向和重新配置，以最大化机器人遇到的障碍物簇的覆盖范围。实验结果证实该方法能够显着提高可重构机器人的区域覆盖性能。