Neuromorphic Computing for Real-Time Robotic Perception and Decision-Making

Abstract

In order to enable robotic systems, achieve a high level of autonomy and responsiveness in complex, dynamic and unstructured environments, it has become imperative for what it is worth to equip them with technologies that will enable them to seamlessly navigate their way in such environments through real-time perception, path planning and decision making. Neuromorphic computing, inspired by the architecture and functioning of the human brain, offers a promising approach to enhance real-time robotic perception and decision-making. This paradigm leverages spiking neural networks like the way the human brain works and specialized hardware to emulate neural processes, enabling robots to process sensory information more efficiently and to learn from their surroundings as well as adapt to new situations, enhancing their ability to make autonomous decisions By integrating neuromorphic systems, robots can achieve higher levels of autonomy, responsiveness, and energy efficiency, crucial for dynamic environments. By mimicking the brain's parallel processing capabilities, neuromorphic systems can handle multiple tasks simultaneously; improving overall performance and responsiveness. This paper explores the principles of neuromorphic computing, its application in robotic systems, and the potential benefits for real-time perception and decision-making tasks. By examining recent advancements and potential challenges, this abstract highlight the transformative role of neuromorphic computing in advancing the next generation of intelligent and responsive robotic systems.