Mobile Machine Collision Avoidance With Multi-Sensor Fusion
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Solution Overview
Problem
Existing collision avoidance technologies for mobile robots are insensitive to small, transparent, and opaque objects, and slow in detecting and responding to obstacles due to limitations in sensor detection and calculation methods.
Innovation Solution
A collision avoidance method that fuses data from multiple sensors, including RGB-D cameras, LiDAR, sonars, and infrared range finders, using Kalman filters to enhance detection accuracy and speed, calculates a closed-form solution for collision distance, and adjusts the mobile machine's velocity to avoid obstacles effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distance sensor is used to detect obstacles, then the robot can identify danger areas, but the detection is insensitive to small, transparent, and opaque objects
Solution Approach 1:
The patent combines multiple sensors (RGB-D camera, LiDAR, sonar, infrared range finder) into a unified sensor fusion system. Each sensor type detects different characteristics of obstacles, and their data is integrated through Kalman filtering to achieve comprehensive obstacle detection that overcomes the limitations of individual sensors, particularly for small, transparent, and opaque objects.
Solution Approach 2:
The system creates a composite sensing approach by integrating data from heterogeneous sensor types (optical, acoustic, electromagnetic). This composite detection methodology combines the strengths of each sensor modality to achieve superior detection capability across diverse obstacle types that single sensors cannot detect reliably.
2Loss of time
If traditional distance calculation methods are used, then the robot can determine safe speed, but the calculation is slow and affects collision avoidance response time
Solution Approach 1:
The system pre-calculates the collision distance using a closed-form solution that directly computes the result without iterative approximation. This preliminary analytical solution provides immediate collision risk assessment, enabling real-time velocity adjustment and rapid collision avoidance response without computational delay.
Solution Approach 2:
The patent replaces traditional iterative numerical calculation methods with a closed-form mathematical solution. This substitution eliminates the need for repeated computational iterations, providing instant collision distance calculation that enables real-time control decisions and rapid response to emerging collision risks.
3Measurement precision
If multiple sensors are used to improve detection accuracy, then detection precision increases, but device complexity increases
Solution Approach 1:
The Kalman filter serves as a universal processing algorithm that handles data from all sensor types (RGB-D camera, LiDAR, sonar, infrared range finder) through a unified mathematical framework. This multi-functional approach integrates heterogeneous sensor data streams into a single coherent obstacle representation, managing system complexity through algorithmic unification.
Solution Approach 2:
The Kalman filter acts as an intermediary processing layer between the multiple sensors and the control system. It mediates the integration of diverse sensor data, filtering and fusing information from all sources to produce a unified, accurate obstacle model that simplifies downstream decision-making despite the complexity of multiple input sensors.
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
The method improves detection and response times, ensuring safe navigation by accurately identifying and avoiding diverse obstacles, including small and transparent objects, while reducing computational complexity and ensuring smooth movement.
Implementation Method 1
RGB-D cameras
Implementation Method 2
RGB-D cameras
Implementation Method 3
LiDAR
Implementation Method 4
sonars
Implementation Method 5
infrared range finders
Data Source
AI summary
Collision avoidance for a mobile machine having a plurality of sensors is disclosed. The mobile machine is avoided from colliding with a collision object by fusing sensor data received from the plurality of sensors to obtain a plurality of data points corresponding to the collision object, calculating a closed-form solution of a distance between the mobile machine and each of the plurality of data points, calculating a maximum allowed velocity of the mobile machine based on the shortest distance between the mobile machine and the plurality of data points and a current velocity of the mobile machine, and controlling the mobile machine to move according to the maximum allowed velocity.