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.

Walking animals can continuously adapt their locomotion to deal with unpredictable changing environments. They can also take proactive steps to avoid colliding with an obstacle. In this study, we aim to realize such features for autonomous walking robots so that they can efficiently traverse complex terrains. To achieve this, we propose novel bioinspired adaptive neuroendocrine control. In contrast to conventional locomotion control methods, this approach does not require robot and environmental models, exteroceptive feedback, or multiple learning trials. It integrates three main modular neural mechanisms, relying only on proprioceptive feedback and short-term memory, namely: 1) neural central pattern generator (CPG)-based control; 2) an artificial hormone network (AHN); and 3) unsupervised input correlation-based learning (ICO). The neural CPG-based control creates insect-like gaits, while the AHN can continuously adapt robot joint movement individually with respect to the terrain during the stance phase using only the torque feedback. In parallel, the ICO generates short-term memory for proactive obstacle negotiation during the swing phase, allowing the posterior legs to step over the obstacle before hitting it. The control approach is evaluated on a bioinspired hexapod robot walking on complex unpredictable terrains (e.g., gravel, grass, and extreme random stepfield). The results show that the robot can successfully perform energy-efficient autonomous locomotion and online continuous adaptation with proactivity to overcome such terrains. Since our adaptive neural control approach does not require a robot model, it is general and can be applied to other bioinspired walking robots to achieve a similar adaptive, autonomous, and versatile function.

In order to use walking robots for exploration in a real complex environment, an adaptive control system is required to allow them to successfully and efficiently traverse the terrains. To achieve this, we propose here our adaptive locomotion control technique of a walking robot. It is based on a modular structure, combining neural control with an artificial hormone system. The neural control coordinates all leg joints of the robot and generates its locomotion with various insect-like gaits. In parallel, the artificial hormone system uses the motor commands from the neural control and foot contact feedback to estimate the walking state and automatically adapt the joint movements with respect to the terrain. The adaptability is quickly achieved in an online manner within a few seconds. Robot walking experiments show that this adaptive control technique enables a six-legged robot to adapt to various difficult terrains with energy efficiency. Such terrains include sand (loose ground), sponge with different softness levels (soft/compliant ground), grass (vegetated ground), and floor/pavement (hard ground). The technique does not require robot kinematics or an environmental model; and can, therefore, be potentially applied to different legged robots to achieve stable online adaptation and energy-efficient locomotion on unpredictable (compliant) terrains.