Bionic Robust Memristor-Based Artificial Nociception System for Robotics
Nociception is an important ability for robots to interact safely with humans or work in hostile environments. By referring to previous research in mechanical receptors with ring oscillators and memristor-based nociceptors, we propose a complete robotic sensing system that senses forces and processes electrical signals at both the edge and the central. This artificial system mimics the nociception behavior of the human nervous system processing external damaging mechanical stimuli. Given mechanical receptors and nociceptors are subject to damage under harsh working conditions, we designed an error detection module that involves memristor crossbar arrays (CBA) to detect components' potential failures. Furthermore, we propose solutions to non-idealities such as the quantization error and the memristance programming variations to boost the accuracy and the robustness of our failure detection memristor crossbar array. The precision CBA proposed reduces the quantization error for a single cell to at least 1/180 of the error under the common strategy. Our simulations show that using serial connection memristors cells and high resistance ratio cells reduce the output current variation due to programming variation to 62.2% and 71.52% respectively. The full-system simulation under a typical application scenario shows the system's ability to sense noxious stimuli and maintain system robustness at a high level.
