Mobile Robot Volumetric Point Cloud Navigation
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Solution Overview
Problem
Current mobile robots lack effective navigation and object detection capabilities, particularly in dynamic environments, due to limitations in sensing and processing technologies, which hinder their ability to efficiently move and interact with humans and their surroundings.
Innovation Solution
A mobile robot system equipped with a high-performance computer capable of processing over 1000 million instructions per second, combined with a volumetric point cloud imaging device and a holonomic drive system, enables the robot to navigate and detect objects by capturing three-dimensional depth images and emitting speckle patterns to determine distances and object locations, allowing for precise movement and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mobile robot uses conventional sensing and processing technologies, then the device complexity is reduced, but the navigation and object detection capabilities are insufficient
Solution Approach 1:
The sensing system is segmented into multiple specialized sensors: volumetric point cloud imaging device for 3D spatial mapping, time-of-flight camera for depth measurement, and other detectors for specific functions. This segmentation allows each sensor to optimize for its specific task, improving overall navigation reliability without requiring a single overly complex system
Solution Approach 2:
The controller is designed as a multi-functional processing unit that handles data from multiple sensor types, performs 3D mapping, object detection, path planning, and drive command generation. This universal controller integrates multiple functions into a single device, managing complexity while maintaining advanced navigation capabilities
2Measurement precision
If the imaging device is positioned at ground level, then the device complexity is reduced, but the ability to detect objects and capture three-dimensional depth images is limited
Solution Approach 1:
The imaging device is positioned in a elevated dimension above ground level, specifically at a height that allows volumetric point cloud capture. This dimensional change provides a broader field of view and enables 3D depth imaging of the environment, significantly improving object detection accuracy and spatial awareness
3Adaptability or versatility
If the robot uses simple drive commands, then the ease of operation is improved, but the ability to navigate complex environments and interact with humans is hindered
Solution Approach 1:
The controller continuously receives feedback from multiple sensors including point cloud data, depth images, and object detection results. This feedback loop enables the robot to adapt its drive commands in real-time based on environmental conditions, human presence, and navigation goals, providing complex interaction capabilities while maintaining operational simplicity through automated decision-making
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 system enables the robot to effectively navigate and interact with its environment by accurately detecting obstacles and tracking objects, ensuring safe and efficient movement, even in complex settings.
Implementation Method 1
The imaging device determines a time-of-flight between emitting the light and receiving reflected light from the scene. The controller uses the time-of-flight for determining a distance to the reflecting surfaces of the object
Implementation Method 2
The imaging device emits light onto a scene about the robot and capture images of the scene along a drive direction of the robot
Implementation Method 3
The imaging device may include a speckle emitter emitting a speckle pattern of light onto a scene along a drive direction of the robot and an imager receiving reflections of the speckle pattern from the object in the scene
Implementation Method 4
The controller determines a primary speckle pattern on the target object and computes at least one of a respective cross-correlation and a decorrelation between the primary speckle pattern and the speckle patterns of the reference images
Data Source
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AI summary
A mobile robot (100) that includes a drive system (200), a controller (500) in communication with the drive system, and a volumetric point cloud imaging device (450) supported above the drive system at a height of greater than about one feet above the ground and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane in a direction of movement of the mobile robot. The controller receives point cloud signals from the imaging device and issues drive commands to the drive system based at least in part on the received point cloud signals.