Probabilistic Robot Motion Control Under Structural Constraints
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Solution Overview
Problem
Model-based control systems, particularly in robotic systems, face challenges when dealing with uncertain motion models and changing dynamics, as conventional methods like adaptive or learning-based MPC can lead to suboptimal performance or instability, especially when structural constraints are not incorporated into the learning process.
Innovation Solution
A probabilistic control approach using a probabilistic filter that estimates the state and motion model of a robotic system by representing the motion model as a time-varying Gaussian process with structural constraints, allowing for recursive updates of both the state distribution and the Gaussian distributions of the basis functions, ensuring the structural constraints are satisfied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive or learning-based MPC is used to estimate unknown parameters, then the operation of the machine is improved, but the system becomes insufficient when dynamics are changing and structural constraints are not incorporated
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the motion model parameters in real-time as the system operates. The controller learns changing dynamics through recursive estimation of parameters while maintaining adaptability to structural constraints through probabilistic modeling. This allows the system to evolve its understanding of the machine dynamics without requiring complete reconfiguration.
Solution Approach 2:
The patent changes the approach from fixed parameter estimation to probabilistic parameter representation. By modeling parameters as probability distributions rather than fixed values, the system can capture uncertainty and adapt to changing dynamics. The parameters are updated recursively based on observed data while respecting structural constraints through the probabilistic framework.
2Adaptability or versatility
If learning is done recursively in real time, then the controller adapts to changing dynamics, but the computational complexity increases and structural constraints may be violated
Solution Approach 1:
The patent segments the learning process into manageable components by separating the estimation of different parameters and using modular probabilistic models. This segmentation allows real-time computation by breaking down the complex learning task into smaller, computationally tractable sub-tasks that can be executed recursively without overwhelming computational demands.
Solution Approach 2:
The patent transforms the computational problem by changing from direct parameter optimization to probabilistic inference. This parameter transformation simplifies the computational complexity by using statistical methods that are more efficient for real-time processing while naturally incorporating structural constraints through the probability distributions.
3Productivity
If structural constraints are not incorporated into the learning process, then the learning becomes efficient, but the learned system may not be physical and cannot be used for control
Solution Approach 1:
The patent applies preliminary anti-action by proactively preventing the learning process from producing non-physical results. Structural constraints are built into the probabilistic model beforehand, so the learning process is guided from the start to respect physical feasibility. This prevents the need for corrective actions later and maintains both efficiency and reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the structural constraints provide continuous guidance to the learning process. The probabilistic model uses feedback from observed system behavior to update parameters while constantly checking against structural constraints. This feedback loop ensures learning efficiency is maintained while guaranteeing physical feasibility of the learned model.
Data Source
AI summary
A probabilistic feedback controller for controlling an operation of a robotic system using a probabilistic filter subject to a structural constraint on an operation of the robotic system is configured to execute a probabilistic filter estimates a distribution of a current state of the robotic system given a previous state of the robotic system based on a motion model of the robotic system perturbed by stochastic process noise and a measurement model of the robotic system perturbed by stochastic measurement noise having an uncertainty modeled as a time-varying Gaussian process represented as a weighted combination of time-varying basis functions with weights defined by corresponding Gaussian distributions. The probabilistic filter recursively updates both the distribution of the current state of the robotic system and the Gaussian distributions of the weights of the basis functions selected to satisfy the structural constraint indicated by measurements of the state of a robotic system.