Autonomous Robot Mapping and Localization for Efficient Navigation
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Solution Overview
Problem
Autonomous robotic devices face challenges in efficiently navigating and mapping environments to avoid repetitive tasks, such as cleaning areas that have already been cleaned, due to limitations in self-localization and spatial awareness.
Innovation Solution
The robotic device is equipped with a chassis, wheels, sensors, a camera, and a processor that captures spatial data, generates a spatial model of the surroundings, and infers its location by combining measurements from various sensors to create a map and plan movement paths, allowing it to autonomously navigate and avoid repetitive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robotic device uses basic navigation methods, then the device complexity is low, but the measurement precision of location is insufficient causing repetitive tasks
Solution Approach 1:
The patent combines multiple sensors (cameras, LIDAR, ultrasonic sensors, infrared sensors, GPS) into an integrated sensor system that works together to determine location. The processor fuses data from all these sensors to achieve accurate location determination without relying on a single complex system.
Solution Approach 2:
The sensor system is designed to perform multiple functions: capturing spatial data for mapping, determining location for navigation, avoiding obstacles, and tracking movement. This multi-functional approach reduces the need for separate specialized systems.
2Measurement precision
If the robotic device captures comprehensive spatial data, then the spatial model accuracy is improved, but the use of energy increases
Solution Approach 1:
The processor selectively processes spatial data based on current task requirements and confidence levels. Not all sensor data is processed with equal depth - the system adjusts processing intensity to balance accuracy needs with energy conservation.
Solution Approach 2:
The spatial data processing is divided into discrete steps: initial spatial model generation, location determination, path planning, and real-time adjustment. This segmentation allows the system to process data incrementally rather than all at once, reducing peak energy consumption.
3Productivity
If the robotic device generates detailed movement paths, then the task execution efficiency is improved, but the loss of time for path calculation increases
Solution Approach 1:
The system pre-generates spatial models and pre-calculates potential paths during periods when the device is stationary or less active. This preliminary preparation reduces the computational burden during active task execution, improving overall efficiency.
Solution Approach 2:
The path planning system dynamically adjusts the level of detail in movement paths based on environmental complexity and task urgency. In simple environments, simplified paths are used for speed; in complex environments, more detailed paths are generated for accuracy.
Data Source
AI summary
Provided is a robot, including: a chassis; a set of wheels; a plurality of sensors; a camera; a processor; a memory storing instructions that when executed by the processor effectuate operations including: capturing, with the camera, spatial data of surroundings of the robot; generating, with the processor, a spatial model of the surroundings based on at least the spatial data of the surroundings; generating, with the processor, a movement path based on the spatial model of the surroundings; and inferring, with the processor, a location of the robot.