Embodied Adaptive Sensing for Odor Concentration Maximization in Bio-Inspired Robotics

Animals exhibit remarkable adaptability in sensing their environments, employing strategies that optimize information gathering. For instance, silk moths adjust their wingflapping frequency to detect pheromones, while dogs modify their sniffing behavior by altering sniff height and frequency based on proximity to an odor source. Despite the potential to enhance odor detection for olfactory navigation by drawing inspiration from these natural mechanisms, many existing approaches focus on computationally intensive methods like multi-sensory integration or rely on multiple robots for odor localization, rather than leveraging embodied sensing. In this study, we propose an embodied adaptive sensing strategy that enhances odor detection by implementing an active odor sensor on a legged robot and applying a bio-inspired adaptive robot height control system for dynamically adapting the robot's height based on real-time gas concentration feedback. The control system employs a simple artificial hormone mechanism to regulate the robot height by processing gas concentration derivatives, mimicking biological adaptability. By utilizing the interaction between the active odor sensor, adaptive control system, and the legged body, this approach allows the robot to optimize its height online to capture the maximum gas concentration, thereby reducing the need for complex algorithms and high computational resources. As a result, it offers a more efficient solution for odor-driven tasks, with potential applications in real-world environments.