Autonomous Robot Stability Monitoring via Attraction Domain Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous mobile robots (AMRs) face challenges in maintaining predictable and stable behavior due to environmental changes and equipment malfunctions, leading to potential safety hazards, and existing methods for estimating attraction domains are conservative and do not adequately guarantee safe operation.
Innovation Solution
The method involves using Lurie-Postnikov functions to estimate attraction domains in the state space of AMRs, ensuring prescribed exponential stability by defining a measure of distance from a nominal state, and employing GNSS and IMU data to monitor and maintain the robot within a specified attraction domain, with notifications for unsafe conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard Lyapunov functions are used to estimate attraction domains, then the estimation is conservative and ensures safety, but the volume of the attraction domain is reduced, limiting the robot's operational flexibility
Solution Approach 1:
The patent transforms the conservative attraction domain estimation into a less conservative one by changing the mathematical parameters and functions used. Specifically, it employs optimal control theory and modified Lyapunov functions with adjustable parameters to expand the estimated attraction domain volume while preserving the essential safety guarantee property.
Solution Approach 2:
The patent introduces additional dimensions to the estimation problem by considering not just the standard state space but also incorporating control input spaces and time-dependent parameters. This multi-dimensional approach allows for a more comprehensive and less conservative characterization of the attraction domain.
2Adaptability or versatility
If the attraction domain is expanded to increase operational flexibility, then the robot can operate in more states, but the safety guarantee may be compromised due to less conservative estimation
Solution Approach 1:
The patent implements a feedback mechanism where the estimated attraction domain is continuously refined based on system behavior observations. The control algorithm uses feedback from the actual system trajectory to adjust the estimation parameters, ensuring that safety guarantees are maintained even as the domain expands to provide greater operational flexibility.
Solution Approach 2:
The patent performs preliminary optimization of the attraction domain estimation before actual robot operation. By pre-calculating the expanded domain boundaries using optimal control theory and verifying safety conditions in advance, the system establishes operational flexibility while pre-confirming safety guarantees before deployment.
3Measurement precision
If more general parametric classes of Lyapunov functions are used, then the freedom of choice increases and the attraction domain estimate improves, but the complexity of parameter optimization increases
Solution Approach 1:
The patent segments the complex parameter optimization problem into manageable sub-problems. It divides the Lyapunov function parameters into distinct groups (e.g., polynomial coefficients, scaling factors, time constants) and optimizes them separately using hierarchical methods, reducing the overall optimization complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent introduces intermediary variables and auxiliary functions that bridge the gap between the general parametric Lyapunov functions and the optimization process. These intermediaries transform the complex high-dimensional optimization into a series of simpler low-dimensional problems that are computationally tractable.
Data Source
AI summary
System for monitoring stability of autonomous robot, including a GNSS navigation receiver including antenna, analog front end, plurality of channels, inertial measurement unit (IMU) and a processor, all generating navigation and orientation data for the robot; based on the navigation and orientation data, calculating position and direction of movement for the robot; calculating spatial and orientation coordinates z1, z2 of the robot, relating to the position and direction of movement; continuing with programmed path for the robot for any spatial and orientation coordinates z1, z2 within an attraction domain, where a measure V(z) of distance from zero in z1, z2 plane are defined by Lurie-Postnikov functions and is less than 1; for spatial and orientation coordinates outside the attraction domain with V(z)>1, terminating the programmed path and generating notification.