Zifan Wang

Representing art using a robotic system is part of artificial intelligence in our lives, especially in the realm of emotional expression. Developing a painting robot involves addressing how to enable the robot to emulate human artistic processes, which often include imprecise techniques or errors akin to those made by human artists. This paper discusses our development of an innovative painting robot utilizing the sim-to-real approach within learning technology. Specifically, this pipeline operates under a deep reinforcement learning (DRL) framework designed to learn drawing strategies from training data derived from real-world settings, aiming for the robot’s proficiency in emulating human artistic expressions. Accordingly, the framework comprises two modules when given a target drawing image: the first module trains in a simulated environment to break down the target image into individual strokes; the second module then learns how to execute these strokes in a real environment. Our experiments have shown that this system can meet our objectives effectively.

This paper addresses the problem of relative position-based formation planning for a leader–follower multi-robot setup, where the robots adjust the formation parameters, such as size and three-dimensional orientation, to avoid collisions and progress toward their goal. Specifically, we develop a virtual sub-target-based obstacle avoidance method, which involves a transitional virtual sub-target that guides the robots to avoid obstacles according to obstacle information, target, and boundary. Moreover, we develop a changing formation strategy to determine the necessity to avoid collisions and a priority-based model to determine which robots move, thus dynamically adjusting the relative distance between the followers and the leader. The backstepping-based sliding motion controller guarantees that the trajectory and velocity tracking errors converge to zero. The proposed robot navigation method can be employed in various environments and types of obstacles, allowing for a formation change. Furthermore, it is efficient and scalable under various numbers of robots. The approach is experimentally verified using three real mobile robots and up to five mobile robots in simulated scenarios.