Mobile Robot Position Tracking Under Wheel Slippage
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Solution Overview
Problem
Current autonomous robotic devices face challenges in accurately tracking displacement and rotation, especially when encountering slippage, which affects their ability to generate reliable maps of their environment and maintain thorough coverage.
Innovation Solution
The robotic device employs a combination of sensors, including visual, encoder, optoelectronic, and depth sensors, to capture readings of its environment, determine displacement, detect slippage, and estimate a corrected position, using a processor to create an ensemble of possible positions and determine the most feasible one based on visual readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If encoder sensor readings are used to track displacement, then the robotic device can maintain continuous position tracking, but slippage causes measurement errors that reduce position accuracy
Solution Approach 1:
The patent introduces an intermediary system that reconciles conflicting position data from multiple sources. The processor acts as a mediator that receives encoder sensor readings (which fail during slippage) and compensates using visual feature tracking and depth sensor data, thereby maintaining reliable position tracking even when slippage occurs
Solution Approach 2:
The system dynamically changes the weighting and reliance on different sensor parameters based on detected slippage conditions. When slippage is detected through encoder readings indicating unexpected displacement, the system shifts from relying on encoder data to relying more heavily on visual and depth sensor parameters for position calculation
2Measurement precision
If multiple sensors are combined to improve position tracking accuracy, then measurement precision increases, but device complexity increases
Solution Approach 1:
The patent makes the processor universally capable of handling multiple sensor types and multiple tracking methods. The same processor that runs the core control algorithm also fuses data from encoders, visual sensors, and depth sensors, eliminating the need for separate dedicated processing units for each sensor type and reducing overall system complexity
Solution Approach 2:
The patent merges multiple sensing functions into a unified position estimation framework. Instead of having separate systems for encoder-based tracking and visual-based tracking, the patent combines them into a single integrated system where the processor fuses all sensor inputs to produce a unified, accurate position estimate
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
This solution enables the robotic device to accurately track its position and movement, even in the presence of slippage, thereby improving its mapping capabilities and coverage efficiency.
Implementation Method 1
capturing, with an encoder sensor, readings of wheel rotation indicative of displacement of the robotic device
Implementation Method 2
capturing, with an optoelectronic sensor, readings of a driving surface of the environment of the robotic device; determining, with the processor, displacement of the robotic device in two dimensions based on the optoelectronic sensor readings
Implementation Method 3
capturing, with a visual sensor, visual readings to objects within an environment of the robotic device
Implementation Method 4
capturing, with a depth sensor, distances to obstacles as the robot moves within the environment
Data Source
AI summary
Provided is a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing visual readings to objects within an environment; capturing readings of wheel rotation; capturing readings of a driving surface; capturing distances to obstacles; determining displacement of the robotic device in two dimensions based on sensor readings of the driving surface; estimating, with the processor, a corrected position of the robotic device to replace a last known position of the robotic device; determining a most feasible element in an ensemble based on the visual readings; and determining a most feasible position of the robotic device as the corrected position based on the most feasible element in the ensemble and the visual readings.