My research objective is to explicitly bridge the gap between high computational performance and low power dissipation of robot on-board hardware by designing a bio-inspired tapered whisker neuromorphic computing (also called reservoir computing) system for offroad robot environment perception and navigation, that centres the interaction between a robot's body and its environment. Mobile robots performing tasks in unknown environments need to traverse a variety of complex terrains, and they must be able to reliably and quickly identify and characterize these terrains to avoid getting into potentially challenging or catastrophic circumstances. To solve this problem, I drew inspiration from animals like rats and seals, just relying on whiskers to perceive surroundings information and survive in dark and narrow environments. Additionally, I looked to the human cochlear which can separate different frequencies of sound. Based on these insights, my work addresses this need by exploring the physical whisker-based reservoir computing for quick and cost-efficient mobile robots environment perception and navigation step by step. This research could help us understand how the compliance of the biological counterparts helps robots to dynamically interact with the environment and provides a new solution compared with current methods for robot environment perception and navigation with limited computational resources, such as Mars.

翻译：我的研究目标是设计一种受生物启发的锥形触须神经形态计算（也称为储层计算）系统，用于越野机器人的环境感知与导航，该系统以机器人身体与其环境之间的交互为核心，从而明确弥合机器人机载硬件的高计算性能与低功耗之间的差距。在未知环境中执行任务的移动机器人需要穿越各种复杂地形，并且必须能够可靠且快速地识别和表征这些地形，以避免陷入潜在困难或灾难性的情况。为了解决这个问题，我从老鼠和海豹等动物身上汲取灵感，它们仅依靠触须感知周围环境信息，并在黑暗狭窄的环境中生存。此外，我还参考了人类耳蜗能够分离不同频率声音的能力。基于这些见解，我的工作通过逐步探索基于物理触须的储层计算，以快速且高效的方式满足移动机器人环境感知与导航的需求。这项研究有助于我们理解生物对应体的柔顺性如何帮助机器人与环境动态交互，并为当前在计算资源受限（例如火星）情况下的机器人环境感知与导航方法提供了新的解决方案。