A Neural Network Based Teleoperation for Remote Controlled Vehicles
Optimizing remote vehicle control through neural-adaptive frameworks for superior stability and real-time performance.
Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbances (e.g., aerodynamic drag, bank angles) coupled with highly nonlinear tire-road dynamics. To address...
ArXiv CS.RO
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This research bridges the gap between theoretical robotics and practical edge computing by utilizing neural networks to overcome latency-induced instability in remote driving. By replacing computationally heavy model-predictive control with lightweight adaptive networks, it enables real-time vehicle navigation in unpredictable, high-stakes environments.
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