Editorial: AI for Robot Modeling, Path Planning, and Intelligent Control

摘要

Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence demonstrated by humans. Examples of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects, which is usually regarded as intelligent control. In recent years, the applications of AI to robotics have experimented with exponential growth. AI plays a crucial role in the path planning of robots, allowing fast responses to changes in complex environments. It also plays a leading role in modeling and intelligent control of robots by allowing a more complex feedback analysis, self-tuning applications, and on-the-fly adaptation to environmental changes.
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\nChanging industrial environments like flexible manufacturing facilities and automated warehouses where robots are intended to work side by side with humans are benefiting directly from advancements in complex path planning and autonomous decision making based on AI-powered algorithms. On the consumer side, applications like cleaning robots and delivery robots are also becoming part of our daily lives. The implementation of AI-powered path planning and control algorithms drastically improves the efficiency and practicality of these robots, as the environments in which these robots must operate is highly dynamic and needs constant adaptation.
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\nThis Research Topic is organized under the section “Robotic Control Systems” within Frontiers in Robotics and AI. The first article by Tan et al. is focused on designing mechanisms and algorithms for robotics, which serves as a platform for path planning and control. Current robot designs have been taking inspiration from games and entertainment artifacts (GEAs). However, there is a lack of systematic and general processes for implementing a GEA-inspired design in robotics. In this article, a systematic robot design paradigm is proposed based on the inspiration of GEAs. Both problem-driven and solution-driven processes can be followed to make use of analogies of GEAs so that robotic solutions can be obtained for real-world problems. The application of the design paradigm is demonstrated by using a reconfigurable floor cleaning robot and its path planning algorithm.
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\nDue to the capacity of reasoning, AI plays a crucial role in achieving safe human-robot interaction (HRI) for collaborative robots. The article by Du et al. combines different AI technologies to achieve active collision avoidance for safe HRI. A Microsoft's motion sensing input device named Kinect is employed to detect anyone who enters the workspace of the robot so that the skeleton data of the human can be calculated in real-time. An expert system with collision avoidance knowledge is employed to analyze the behavior of the human for active collision avoidance. An artificial potential field method is adopted to plan a new path for the robot such that it can bypass the human in real-time. Experiments show that by applying these AI-powered algorithms, the proposed system can safeguard the human by detecting the human and analyzing the motion of the human.
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\nAn important issue for collaborative robots is to learn the compositionality of human activities, i.e., to recognize both activities and their comprising actions. Even a small set of actions and objects can create a large combination of possible activities. Most existing approaches in this topic address action and activity recognitions separately. The article by Mici et al. suggests learning human activities concurrently on two levels of semantic and temporal complexity: Transitive actions such as reaching and opening a cereal box, and high-level activities such as having breakfast. The learning model consists of a hierarchy of growing-when-required (GWR) networks which can process and learn inherent spatiotemporal dependencies of multiple visual cues abstracted from hu