Sustainable Sensor Fusion And Perception Frameworks For Intelligent Iot Robotics

Abstract

Sensor fusion is also a core feature of robotic perception systems because it allows autonomous platforms to create consistent environmental representations based on heterogeneous data streams. Probabilistic frameworks integrate multi-modal sensor streams, including visual, ranging, and thermal modalities, through Bayes filtering techniques that properly account for measurement uncertainty and environmental variability. Edge computing architectures remove cloud dependence and minimize network bandwidth utilization as well as energy usage of centralized processing models. Continuous polling strategies are substituted by event-driven sensing mechanisms, which significantly reduce idle power consumption by allowing deeper processor sleep states because interrupt-based data acquisition is used. Adaptive orchestration layers are used to track environmental complexity by the metric of entropy, dynamically changing the sampling frequencies to achieve a balance between the goals of perceptual accuracy and power conservation. The hybrid filtering architectures are hybrid in that they incorporate both parametric approaches to linear subsystems and non-parametric approaches to non-linear dynamics to provide better stability in difficult signal conditions. Neural networks modeled as quantized neural networks are fast (computationally) on integer arithmetic but are also not inaccurate (within acceptable tolerance levels) compared to full-precision implementations. Dynamic voltage scaling and vectorized operations are hardware acceleration techniques that have allowed real-time perception to be supported on embedded platforms with resource constraints. Autonomous navigation, precision agriculture, and assistive healthcare equipment are all industrial uses. The sustainable perception architectures are also linked to the low carbon emission, strong production ecosystems, and smart urban mobility infrastructure in tandem with the aims of global development.