Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Autonomous edge computing in robotics, smart cities, and autonomous vehicles
relies on the seamless integration of sensing, processing, and actuation for
real-time decision-making in dynamic environments. At its core is the
sensing-to-action loop, which iteratively aligns sensor inputs with
computational models to drive adaptive control strategies. These loops can
adapt to hyper-local conditions, enhancing resource efficiency and
responsiveness, but also face challenges such as resource constraints,
synchronization delays in multi-modal data fusion, and the risk of cascading
errors in feedback loops. This article explores how proactive, context-aware
sensing-to-action and action-to-sensing adaptations can enhance efficiency by
dynamically adjusting sensing and computation based on task demands, such as
sensing a very limited part of the environment and predicting the rest. By
guiding sensing through control actions, action-to-sensing pathways can improve
task relevance and resource use, but they also require robust monitoring to
prevent cascading errors and maintain reliability. Multi-agent sensing-action
loops further extend these capabilities through coordinated sensing and actions
across distributed agents, optimizing resource use via collaboration.
Additionally, neuromorphic computing, inspired by biological systems, provides
an efficient framework for spike-based, event-driven processing that conserves
energy, reduces latency, and supports hierarchical control--making it ideal for
multi-agent optimization. This article highlights the importance of end-to-end
co-design strategies that align algorithmic models with hardware and
environmental dynamics and improve cross-layer interdependencies to improve
throughput, precision, and adaptability for energy-efficient edge autonomy in
complex environments.
