Obstacle recognition method for autonomous robots
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Solution Overview
Problem
Autonomous robots face challenges in efficiently mapping and navigating complex environments due to limitations in sensor integration and processing power, leading to inaccurate mapping and delayed decision-making.
Innovation Solution
The implementation of a real-time navigational stack that integrates LIDAR data, image sensors, and wheel encoders to create a comprehensive map of the workspace, allowing for real-time processing and decision-making, enabling efficient navigation and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (LIDAR, image sensors, wheel encoders) are integrated for comprehensive environmental mapping, then mapping accuracy and navigation capability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex sensing and processing system into distinct functional modules: LIDAR data acquisition module, image sensor module, wheel encoder module, and separate processing stages. Each sensor type processes data independently through dedicated algorithms before integration, reducing overall system complexity while maintaining comprehensive mapping capability
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between raw sensor data and final mapping output. This intermediate stage performs data fusion, coordinate transformation, and feature matching, acting as a buffer that simplifies the integration of multiple sensor types by standardizing their outputs
2Speed
If real-time processing of multiple sensor data streams is implemented, then decision-making speed is improved, but processing power requirements and computational load increase
Solution Approach 1:
The patent implements periodic processing cycles where sensor data is accumulated over fixed time intervals and processed in batches. This periodic approach reduces instantaneous processing power requirements while maintaining real-time decision-making capability through timely updates at each cycle
Solution Approach 2:
The patent applies partial processing by focusing computational resources on the most critical data streams and features at any given moment. Rather than processing all sensor data equally, the system prioritizes processing based on current navigation needs and environmental complexity, reducing overall computational load
3Measurement precision
If comprehensive environmental mapping is performed using multiple data sources, then navigation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of sensor data including calibration, noise filtering, and feature extraction before main mapping operations. By preparing data in advance through these preliminary steps, the actual mapping and navigation computations can proceed more quickly with pre-processed, ready-to-use data
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 enables robots to create accurate maps and navigate complex environments efficiently, reducing processing delays and improving task completion times.
Implementation Method 1
capturing, by a LIDAR disposed on the robot, LIDAR data as the robot performs work within the workspace, wherein the LIDAR data is indicative of distances from the LIDAR to objects and perimeters immediately surrounding the robot
Data Source
AI summary
Provided is a method for operating a robot, including: capturing images of a workspace; capturing movement data indicative of movement of the robot; capturing LIDAR data as the robot performs work within the workspace; comparing at least one object from the captured images to objects in an object dictionary; identifying a class to which the at least one object belongs; generating a first iteration of a map of the workspace based on the LIDAR data; generating additional iterations of the map based on newly captured LIDAR data and newly captured movement data; actuating the robot to drive along a trajectory that follows along a planned path by providing pulses to one or more electric motors of wheels of the robot; and localizing the robot within an iteration of the map by estimating a position of the robot based on the movement data, slippage, and sensor errors.