Mobile Robot Position Correction for Drift and Slippage
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Solution Overview
Problem
Autonomous and semi-autonomous robots face challenges in maintaining accurate position tracking due to drift or slippage, which affects their navigation and task execution, especially when moving between known and new positions.
Innovation Solution
A wheeled device equipped with exteroceptive and proprioceptive sensors, along with a processor that estimates corrected positions using simulated ensemble positions, reduces knowledge loss due to drift or slippage by combining sensor readings and possible error variations, and transmits status information to initiate tasks in other devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the robot uses proprioceptive sensors to track position during movement, then navigation capability is improved, but position accuracy deteriorates due to drift and slippage
Solution Approach 1:
The system uses exteroceptive sensors to capture environmental readings and compares them against expected readings based on proprioceptive sensor data. This feedback loop allows the robot to detect position drift and slippage by identifying discrepancies between actual sensor observations and predicted sensor readings, then corrects the position estimate accordingly.
Solution Approach 2:
The environment itself serves as an intermediary reference frame. By using exteroceptive sensors to observe environmental features and comparing these observations with predictions based on proprioceptive data, the system creates an indirect method for verifying and correcting position estimates, overcoming the limitations of direct proprioceptive measurement.
2Adaptability or versatility
If the robot performs multiple tasks by moving between different positions, then task versatility is improved, but position knowledge is lost due to cumulative drift
Solution Approach 1:
After completing each task and moving to the next position, the system continuously uses exteroceptive sensor feedback to verify and correct its position estimate. This ongoing correction process prevents cumulative drift from causing complete loss of position knowledge, allowing the robot to maintain accurate positioning across multiple task transitions.
Solution Approach 2:
The system performs rapid corrections by comparing exteroceptive readings with predicted readings at key moments during task execution. This allows the robot to quickly identify and correct position drift before it accumulates significantly, maintaining position knowledge efficiency across multiple tasks without requiring continuous heavy computation.
Data Source
AI summary
Provided is a tangible, non-transitory, machine readable medium storing instructions that when executed by a processor effectuates operations including: capturing, with at least one exteroceptive sensor, readings of an environment and capturing, with at least one proprioceptive sensor, readings indicative of displacement of a wheeled device; estimating, with the processor using an ensemble of simulated positions of possible new locations of the wheeled device, the readings of the environment, and the readings indicative of displacement, a corrected position of the wheeled device to replace a last known position of the wheeled device; determining, by the processor using the readings of the exteroceptive sensor, a most feasible position of the wheeled device as the corrected position; and, transmitting, by the processor, status information of tasks performed by the wheeled device to an external processor, wherein the status information initiates a second wheeled device to perform a second task.