Robotic Workspace Perimeter Mapping Using Depth Sensor Coordinates
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Solution Overview
Problem
Current methods for robotic devices to map environments require significant computational power and often rely on inaccurate sensor statistics, leading to poor performance, and may necessitate additional components like beacons, which increase costs and space requirements.
Innovation Solution
A method using a robotic device equipped with sensors to obtain depth data and create a map of a workspace by driving within the environment, expanding the map as new data is collected, and utilizing a communication device for task management and error handling, without the need for extensive processing or additional components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EKF technique is used to estimate pose and position with complete state vector and error covariance matrix, then mapping accuracy is improved, but computational power requirement increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for mapping (perimeter coordinates and internal area points) from the environment, rather than processing complete state vectors and covariance matrices. This is achieved by using a depth sensor to directly measure distances to perimeter elements and storing only the relevant coordinate data, thereby eliminating the computational burden of EKF while maintaining mapping accuracy.
Solution Approach 2:
Instead of using complex EKF algorithms to estimate position and then map the environment, the patent inverts the approach by directly measuring perimeter coordinates with a depth sensor and using those measurements to construct the map. This reversal eliminates the need for iterative estimation and covariance calculations, significantly reducing computational requirements.
2Measurement precision
If distance sensor rotates 360-degrees to measure distances to objects, then complete environment mapping is achieved, but the robotic device cannot perform work simultaneously
Solution Approach 1:
The depth sensor is designed to perform multiple functions: it can quickly scan the environment to map perimeters and internal areas, and then be reused for ongoing work tasks within the mapped space. The system achieves both complete mapping and work performance by using the depth sensor's rapid measurement capability to capture essential spatial information without requiring continuous 360-degree rotation during work operations.
Solution Approach 2:
The patent performs the complete 360-degree environment mapping as a preliminary action before the robotic device begins its work tasks. By completing the mapping first and storing the perimeter and internal area data, the device can then operate efficiently within the pre-mapped space without needing to continuously rotate the sensor, thereby enabling simultaneous work performance.
3Loss of information
If occupancy map tracks all points including perimeters, empty spaces, and spaces beyond perimeters, then comprehensive environment representation is achieved, but computational cost increases
Solution Approach 1:
The patent extracts only the essential spatial information needed for robotic operation: perimeter coordinates that define the workspace boundaries and internal area points that indicate navigable spaces. By using a depth sensor to directly measure and store only these relevant coordinates rather than tracking all possible points in the environment, the system achieves comprehensive environment representation with significantly reduced computational cost.
4Reliability
If additional components like beacons are added for mapping, then mapping reliability is improved, but device complexity and space requirements increase
Solution Approach 1:
The robotic device uses its own depth sensor to perform mapping operations without requiring external beacons or additional components. The depth sensor leverages the device's position and orientation data to directly measure distances to perimeter elements and construct the map, thereby achieving reliable mapping while minimizing device complexity and space requirements.
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
This approach provides a computationally inexpensive and cost-effective solution for marking the perimeter of a workspace, allowing the robotic device to efficiently operate and perform tasks within the mapped area while minimizing errors and component requirements.
Implementation Method 1
the first depth data comprises a first set of radial distances from the robot at a first position to wall surfaces of the workspace
Data Source
AI summary
Provided is a medium storing instructions that when executed by one or more processors of a robot effectuate operations including: obtaining, with a processor, first data indicative of a position of the robot in a workspace; actuating, with the processor, the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining, with the processor, second data indicative of displacement of the robot as the robot drives within the workspace; and forming, with the processor, the map of the workspace based on at least some of the first data; wherein: the map of the workspace expands as new first data of the workspace are obtained with the processor; and the robot is paired with an application of a communication device.