Robot Sensor Filtering for False Positive Obstacle Detection
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Solution Overview
Problem
Robots equipped with sensors often detect false positives, such as light reflections or thermal noise, which can lead to inefficient navigation and require operator intervention, as existing systems lack effective methods to filter out these inaccuracies.
Innovation Solution
A robotic system comprising sensors, a mapping unit, navigation unit, filters of increasing strength, and a processor that detects false positives and applies appropriate filters to remove them, using digital or material filters, and dynamically adjusts filter configurations based on sensor data and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors are used for object detection, then the robot can detect obstacles and navigate autonomously, but false positives are generated due to light reflections and thermal noise
Solution Approach 1:
The patent introduces an intermediary processing system between the sensor and the navigation decision-making process. This intermediary system applies multiple filtering algorithms (spatial filtering, temporal filtering, and machine learning-based filtering) to sensor data before it is used for navigation decisions, thereby eliminating false positives while preserving true obstacle detections
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated and used to adjust filtering parameters. When false positives are identified (through operator correction or algorithmic detection), the system learns from these errors and adjusts its filtering strength dynamically to prevent recurrence while maintaining detection sensitivity
2Reliability
If filter strength is increased to remove false positives, then detection accuracy improves, but more real objects may be filtered out as false positives
Solution Approach 1:
The patent segments the filtering process into multiple independent stages: spatial filtering (separating noise from signal based on location patterns), temporal filtering (separating transient noise from persistent signals), and semantic filtering (using machine learning to distinguish real objects from false positives). Each stage operates with different filtering strengths and can be independently adjusted
Solution Approach 2:
The system dynamically adjusts filter strength based on environmental context, sensor confidence levels, and historical data. Filtering parameters are not fixed but adapt in real-time based on the situation, allowing the system to be more permissive in low-risk situations and more stringent when false positives are more likely
3Reliability
If operator intervention is required to correct false positives, then detection accuracy can be improved, but navigation efficiency decreases
Solution Approach 1:
The system implements self-service through automated false positive detection and correction mechanisms. Machine learning algorithms automatically identify and correct false positives without operator intervention, while the system continuously learns from operator corrections to improve its autonomous correction capabilities over time
Solution Approach 2:
The system performs preliminary filtering and false positive detection before navigation decisions are made. By pre-processing sensor data through multiple filtering stages and identifying potential false positives in advance, the system prevents navigation errors without requiring real-time operator intervention
Data Source
AI summary
Systems and methods for removing false positives from sensor detection for robotic apparatuses traveling a route, wherein the robot may use a material filter, digital filter, or a combination of digital and material filters of increasing strength to remove a false positive from sensor detection. After removing the false positive, signals to one or more motors coupled to the robot may configure the robotic to travel past the false positive along the route.