System to determine collisions of an autonomous mobile device
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Solution Overview
Problem
Existing autonomous mobile devices (AMDs) face challenges in accurately detecting collisions due to false positives from inertial measurement units (IMUs) and the impracticality of using bumper switches, leading to undesirable behaviors and user experience issues.
Innovation Solution
A collision detection system that processes sensor data from IMUs, motor torque, and wheel speed sensors, using physics-based models to calculate residual values compared against thresholds, determining actual collisions and their locations, thereby reducing false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bumper switches are used to detect collisions, then collision detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical bumper switches with a physics-based model that uses sensor data (IMU, motor torque, wheel speed) to detect collisions. This substitutes a mechanical detection system with a computational model, reducing part count while maintaining collision detection capability.
Solution Approach 2:
The patent introduces a physics-based model as an intermediary that processes sensor data to infer collision events. Instead of directly detecting collisions with mechanical switches, the model acts as a mediator that analyzes multiple sensor inputs to determine collision occurrence, reducing the need for dedicated mechanical sensors.
2Device complexity
If IMU threshold comparison is used to detect collisions, then detection simplicity is improved, but measurement precision deteriorates due to false positives
Solution Approach 1:
The patent merges multiple sensor data sources (IMU, motor torque, wheel speed) into a unified physics-based model for collision detection. By combining these measurements and analyzing them through a physical model, the system achieves more accurate collision detection compared to using IMU threshold comparison alone, while maintaining practical complexity.
Solution Approach 2:
The physics-based model provides a feedback mechanism that compares predicted motion (based on motor torque and wheel speed) with actual motion (from IMU). This feedback loop allows the system to distinguish between normal motion variations and actual collisions, improving measurement precision while keeping the detection method relatively simple.
Data Source
AI summary
An autonomous mobile device (AMD) moves within in a physical space. The AMD may encounter features in the physical space, such as flooring transitions, or obstacles that may impede movement. Sensors such as an inertial measurement unit (IMU), wheel encoders, and motor torque sensors acquire sensor data. Based on the sensor data, measured motion values are determined. Based on physical parameters of the AMD and the sensor data, predicted motion values are determined. A residual or difference between the predicted and measured motion values is calculated. If the residual exceeds a threshold range, a collision may be deemed to have occurred. Characteristics of the residual may be used to determine which side of the AMD collided with an obstacle. Collisions are reliably detected while false detections due to features such as uneven floors or flooring transitions are reduced or eliminated.