Mobile Robot Confidence-Zone Control Under Sensor Uncertainty
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Solution Overview
Problem
Existing robot motion planning algorithms are challenged by sensor uncertainties due to noise, biases, and calibration errors, leading to suboptimal or unsafe paths and potential collisions.
Innovation Solution
A system that generates a unified confident zone map based on sensor data, assesses confidence levels, and employs a combination of receding horizon planning and hard control to navigate through more reliable sensor zones, using a dynamic cost function to adjust trajectory plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robot motion planning algorithms use sensor data for navigation, then the robot can operate autonomously in dynamic environments, but sensor uncertainties (noise, biases, calibration errors) lead to inaccurate state estimation and suboptimal or unsafe paths
Solution Approach 1:
The patent introduces an intermediary layer (state estimation module with confidence assessment and unified confident zone map) between sensor data and motion planning algorithms. This intermediary processes sensor measurements, evaluates their reliability, and provides corrected state estimates to the planner, thereby mediating the harmful effect of sensor uncertainties on navigation safety
Solution Approach 2:
The system implements feedback by continuously assessing confidence levels of state estimates and using this information to adjust planning decisions. When confidence is low, the system modifies planning behavior (e.g., increases safety margins, selects more conservative paths), creating a feedback loop that adapts planning reliability to actual sensor performance conditions
2Productivity
If the robot relies on accurate state estimation for motion planning, then planning algorithms can generate optimal paths, but sensor uncertainties cause incorrect perceptions of environment and position
Solution Approach 1:
The patent changes the parameter representation by introducing confidence levels as an additional parameter alongside state estimates. Instead of treating state estimates as fixed values, the system represents them with associated confidence metrics, allowing the planner to account for measurement precision variations and adjust trajectories accordingly to maintain efficiency despite sensor uncertainties
3Adaptability or versatility
If the robot uses multiple sensor modalities for state estimation, then the system can operate in diverse environments, but combining multiple sensors introduces additional sources of uncertainty and complexity
Solution Approach 1:
The patent creates a universal confidence assessment framework that works across multiple sensor modalities (LIDAR, cameras, IMU, wheel encoders). The unified confident zone map serves as a multi-functional data structure that integrates confidence information from all sensor types, allowing the system to handle diverse environments without requiring separate processing pipelines for each sensor modality
Data Source
AI summary
A computer-implemented system and method relate a mobile robot. State data is generated using sensor data from at least one sensor. A current confident zone is identified on a unified confident zone map using the state data. The unified confident zone map includes confident zones. Each confident zone is indicative of a given confidence level of given state data of a selected sensor modality for a given location. Assessment data is generated that indicates whether the current confident zone is deemed a failure zone. A mobile robot is controlled based on a control command. The control command relates to a recovery plan of moving the mobile robot out of the current confident zone when the assessment data indicates that the current confident zone is the failure zone. The control command relates to another plan when the assessment data indicates that the current confident zone is not the failure zone.