Adaptive Robot Planning and Learning in Dynamic Environments: Enhancing Autonomy and Efficiency

Adaptive Robot Planning and learning in Dynamic Environments is a rapidly developing area that aims at improving both the autonomy and efficiency of robotic systems. In the case of dynamic and uncertain environments, traditional pre-programmed robots have no means to adapt properly leading directly in decline performance. They are adaptive robots, which enrich the intelligent functionalities through capable machine learning algorithms and robust planning strategies to allow them to autonomously adapt their behaviour and decision process on-the-fly. In this paper, we proposed adaptive robot planning and learning technologies that incorporate reinforcement learning (RL), deep-learning methods and probabilistic models for enhancing robustness as well as autonomy in robotic systems. In this paper we present different ways of perceiving dynamic environments, by using a set of sensors measuring autonomous driving related information and the complementary algorithms to translate sensory input data into information about local spatial environment conditions suitable for real time decision making such as sensor fusion or on-site big data processing-based methods. Additionally, we focus on how continuous learning can be employed to facilitate adaptability of robots and dynamically adjust their strategies based upon both historical experiences and new observations. Applications and benefits of the adaptive planning and learning are demonstrated via case studies in autonomous driving, robotic surgery or industrial automation. These examples illustrate the ways adaptive robots can help to make operations more efficient, minimize human intervention and boost safety in potentially hazardous environments. The paper covered challenges involved with adaptive systems, including the computational complexity required to update models in real-time and need for safety mechanisms that robustly fail or degrade safely. The current status of some future directions is proposed to encourage interdisciplinary collaboration can help tackle these challenges, and take the field a step further. We use adaptive robot planning and learning to move towards more intelligent, efficient, autonomous robotic systems that are effective within highly dynamic environments.