Mobile Robot Friction Estimation via Torque Transitions
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Solution Overview
Problem
Mobile robots face challenges in accurately estimating friction coefficients between their wheels and the ground, especially on complex 3D terrains, which affects their traversability and path planning.
Innovation Solution
A mobile robot system that includes units for estimating contact angles, traction forces, normal forces, and friction coefficients, along with uncertainty analysis, to determine the maximum friction coefficient by monitoring torque changes, enabling improved friction coefficient estimation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If friction coefficient estimation is performed on complicated 3D terrain, then traversability prediction accuracy is improved, but measurement precision deteriorates due to terrain complexity
Solution Approach 1:
The patent implements a feedback mechanism where the mobile robot iteratively adjusts torque based on estimated friction coefficients and uncertainties. The controller continuously monitors torque changes and updates friction coefficient estimates using the relationship between torque, contact angles, and ground reaction forces, creating a closed-loop system that improves measurement precision through iterative refinement
Solution Approach 2:
The patent changes the parameter being measured from direct friction coefficient to a combination of contact angles and ground reaction forces. By estimating contact angles between wheels and ground surface, and using the relationship between torque, normal forces, and friction forces, the system transforms an intractable measurement problem into a solvable one through parameter transformation
2Measurement precision
If multiple estimation units are added for contact angles, traction forces, and normal forces, then friction coefficient estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent employs self-service by using the mobile robot's existing motion control system and torque sensors to perform friction coefficient estimation. The controller utilizes already-available data (torque, joint poses, wheel state variables) and existing actuation mechanisms to estimate friction coefficients, eliminating the need for separate dedicated measurement devices
Solution Approach 2:
The controller serves multiple functions: it controls wheel torques for motion, estimates contact angles from torque data, calculates ground reaction forces, and determines friction coefficients. This multi-functionality reduces device complexity by consolidating estimation functions into the existing control system rather than adding separate specialized devices
3Reliability
If real-time friction coefficient estimation is performed during motion, then traversability control is improved, but use of energy increases due to continuous sensing and computation
Solution Approach 1:
The patent implements periodic action by performing friction coefficient estimation at specific intervals during torque changes rather than continuously. The controller estimates friction coefficients at discrete points when torque transitions occur, reducing computational load and energy consumption while maintaining adequate traversability control
Solution Approach 2:
The patent applies preliminary action by estimating friction coefficients before critical traversability decisions are made. The system proactively gathers torque and contact angle data during normal operation to prepare friction coefficient estimates, enabling better traversability control without requiring intensive real-time computation during critical moments
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the robot's ability to navigate complex terrains by accurately determining maximum friction coefficients, improving traversability and motion safety through precise friction coefficient estimation and control.
Implementation Method 1
a friction coefficient estimation unit estimating friction coefficients between the wheels and the ground using the estimated traction forces and the estimated normal forces
Implementation Method 2
a normal force estimation unit estimating normal forces applied to the wheels and normal force uncertainties using the contact angles, the contact angle uncertainties, the traction forces, and joint pose information
Implementation Method 3
a traction force estimation unit estimating traction forces applied to the wheels and traction force uncertainties using state variables of the wheels
Data Source
AI summary
A mobile robot configured to move on a ground. The mobile robot including a contact angle estimation unit estimating contact angles between wheels of the mobile robot and the ground and uncertainties associated with the contact angles, a traction force estimation unit estimating traction forces applied to the wheels and traction force uncertainties, a normal force estimation unit estimating normal forces applied to the wheels and normal force uncertainties, a friction coefficient estimation unit estimating friction coefficients between the wheels and the ground, a friction coefficient uncertainty estimation unit estimating friction coefficient uncertainties, and a controller determining the maximum friction coefficient from among the friction coefficients such that the maximum friction coefficient has an uncertainty less than a threshold and at a point of time when the torque applied to each of the wheels changes from an increasing state to a decreasing state, among the estimated friction coefficients.