Characterization of motion and warning light signals for flying robots

Abstract: As the use of flying robots in various types of human-robot collaborative work continues to increase, but their interaction with humans remains somewhat deficient, it has become critical to ensure that flying robots are able to convey information to humans in a timely and accurate manner. Lightspeak is an effective means to achieve this goal by controlling the lights on flying robots to convey information in a specific pattern, color, or frequency. In the field of industrial robotics, specific light patterns can be used to indicate the robot's working status, production progress, or possible malfunctions. Such visual signals can help plant operators quickly recognize robot status and act accordingly, thereby increasing productivity and reducing potential errors or hazards. Although the application of light language in industrial robotics has achieved some success, the research on human-robot interaction for flying robots is still relatively limited. Especially in dynamic and complex environments, how to design efficient light language patterns to adapt to the multi-dimensional motion characteristics of flying robots and to ensure that humans can quickly interpret light language messages remains an urgent challenge. Using virtual reality (VR) technology, this study aims to develop a set of flying robot intention characterization methods based on light signals by simulating flying robot light changes, designing light language rules applicable to the multi-degree-of-freedom motion and warning states of flying robots, and optimizing their performance in user understanding and interaction through experiments. Based on the task analysis of the flying robot, this study identifies seven degrees of freedom of motion and three main warning modes by decomposing the motion and warning states of the flying robot in detail. Based on this, we designed three basic light modes including constant light, blinking, and surge, and optimized their colors, blinking frequencies, and timings. The experiment uses 73 motion state light languages and 8 alarm state light languages to test the characterization effect and user preference of different light languages in multiple contexts through animated video and randomized design. The experimental study shows that in the stationary state, the all-light constant light signal has the highest preference rate, while in the dynamic motion state and emergency alarm state, the flashing signal (e.g., double flashing, long and short flashing) exhibits higher saliency and information transfer efficiency. Users' choices of light language patterns are highly correlated with their visual saliency, clarity of signal meaning, and situational urgency. The red light signal has a significant advantage in the warning state, and the high-frequency flashing signal is the most effective in the “hardware failure” scenario, while the stable constant light signal is more suitable for the “active stop” scenario. In this study, we propose a light language design method that combines perceptual theory, color design, and light animation, which significantly improves the efficiency of the flying robot's message delivery in multi-degree-of-freedom motion and alarm states. Through experimental verification, this set of light language rules provides important support for the safety and human-robot interaction ability of flying robots in complex application scenarios, and lays a foundation for the expansion of the light language in the field of robotics.

Keywords: Flying robot, Human‒Machine Interaction, Light Design

DOI: 10.54941/ahfe1005844

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