Pipeline-Oriented Learning-Assisted Predictive Control for Large-Scale Path-Constrained Autonomous Navigation

Abstract

Massive autonomous navigation systems should meet path constraints, real-time latency needs and growing computational constraints due to the integration of perception, learning and control. Even though predictive control provides a sound-principled approach to constraint-aware navigation, when integrated with learning-based components, it is usually implemented in monolithic architectures that are highly coupled, thereby constraining scalability, parallelism, and distributed implementation. This paper will solve these shortcomings by developing a pipeline-based learning-assisted predictive control system of autonomous navigation at large scale with path-constricted conditions. The suggested framework represents the perception-learning-prediction-control end-to-end pipeline through a directed processing pipeline graph, which allows a stage wise decomposition, run parallel and latency-sensitive coordination of heterogeneous distributed computing resources. The pipeline contains a learning-assisted predictive controller, which enhances the adaptiveness of the controller during the instance of environmental uncertainty without violating harsh path and actuation constraints. Latency models of explicit calculations and communication are included to make sure it is feasible in the real-time, with the distributed scheduling and decoupling of the stages. This proposal is confirmed in extensive experimental analysis within a large scale navigation environment which shows that the proposed pipeline-based architecture achieves major improvement in end to end latency and scalability relative to traditional monolithic learning based control mechanisms without compromising navigation precision and constraint satisfaction. The findings also indicate that learning support helps to increase the robustness in the disturbance of time dynamics, and the abstraction of the pipeline provides predictable and efficient system-wide execution. In general, the paper provides a scalable and generalizable pipeline-based information on the integration of learning-assisted predictive control to distributed autonomous navigation systems and other latency-intensive cyber-physical systems.