Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.
Sensors (Basel). 2021 Dec 13;21(24):8331. doi: 10.3390/s21248331.
Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of "How clean is clean" is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.
清洁是日常生活中最重要的基本任务之一。此外，清洁的重要性促使研究人员努力将包括机器人技术在内的前沿技术引入清洁领域。然而，评估清洁质量的有效方法是一个同样重要的研究问题，需要解决。解决“清洁到什么程度才算清洁”这一基本问题的首要步骤是使用自主清洁审计机器人来审计给定区域的清洁度。这项研究工作专注于一种基于强化学习的经验驱动污垢探测策略，用于清洁审计机器人。所提出的方法使用基于策略的近端策略优化（PPO）学习方法来生成路径点和采样决策，以探索给定区域中可能存在的污垢积累区域。策略网络在具有模拟污垢模式的多个环境中进行训练。使用名为 BELUGA 的内部开发的清洁审计机器人在模拟和真实环境中进行了实验，以验证训练后的策略。
