Neural Network MPC With Bayesian Inference for Uncertain Navigation
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Solution Overview
Problem
Conventional model predictive control (MPC) methods struggle with uncertainty, particularly aleatoric and epistemic uncertainty, leading to suboptimal solutions that lack robustness and fail to capture multi-modal decision-making scenarios, especially in complex environments with obstacles.
Innovation Solution
A Bayesian formulation for MPC is developed, utilizing Stein variational inference to represent a posterior distribution over control outputs as a set of particles, enabling the system to reason about a spectrum of solutions and provide a principled framework for generalizing various MPC algorithms, thereby addressing multi-modal planning problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional model predictive control methods are used, then the control system is simple to implement, but the system fails to handle uncertainty and produces suboptimal solutions lacking robustness
Solution Approach 1:
The patent transforms the control problem by changing the mathematical formulation from deterministic to Bayesian, introducing probability distributions over control outputs. This parameter change enables the system to represent uncertainty explicitly through posterior distributions, improving robustness while managing complexity through structured Bayesian inference
Solution Approach 2:
The patent introduces Stein variational inference as an intermediary computational framework that bridges the gap between Bayesian formulation and practical control implementation. This intermediary method enables efficient approximation of posterior distributions over control outputs, making the complex Bayesian approach computationally tractable
2Adaptability or versatility
If conventional MPC methods are used, then the computational process is fast, but the system cannot capture multi-modal decision-making scenarios
Solution Approach 1:
The patent segments the posterior distribution over control outputs into multiple discrete particles, where each particle represents a potential solution mode. This segmentation enables the system to capture multi-modal decision-making scenarios by maintaining diverse candidate solutions rather than converging to a single mode, while the particle filter approach manages computational burden through selective sampling
3Measurement precision
If conventional MPC methods are used, then the control algorithm is straightforward, but the system lacks accuracy in complex environments with obstacles
Solution Approach 1:
The patent implements feedback through Bayesian inference, where the posterior distribution over control outputs is continuously updated based on system observations and uncertainty estimates. This feedback mechanism improves accuracy in complex environments by adapting control decisions to actual system behavior and uncertainty levels, while the structured Bayesian framework manages the complexity of uncertainty handling
Data Source
AI summary
Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.