Neuromorphic electronics for robotic perception, navigation and control: A survey

Abstract

Neuromorphic electronics have great potential in the emulation of the sensory, cognitive, self-learning, and actuating functions of robots. While typically implemented in rigid silicon, emerging technologies in organic and flexible electronic materials have also led to tremendous advances in the development of neuromorphic perception systems. However, a comprehensive review of the contribution/role of organic neuromorphic electronics for robotic applications is still missing. This review presents advancements in silicon-based and organic neuromorphic electronics for intelligent robot development, focusing on perception, navigation, and learning-based control. Organic synaptic devices, along with dynamic vision sensors, enable diverse forms of sensory-enabled computational perception, offering tunability, stability, low power consumption, and conformal substrates. Integration of simultaneous localization and mapping techniques and path planning algorithms empowers robots to efficiently navigate, build accurate maps, and make informed decisions. Different learning algorithms and their hardware implementations in neuromorphic robotic control are explored, enabling robots to learn and adapt to dynamic environments. The review highlights the potential of neuromorphic electronics for sensing, thinking, and acting in advanced robotic systems. Organic, inorganic, and hybrid materials are discussed for implementing perception, navigation, and control in robots. Future research directions in the field are outlined. Leveraging various neuromorphic electronics unlocks the full potential of intelligent robotic systems for diverse applications.