Mobile Robot Obstacle State Detection for Adaptive Navigation
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Solution Overview
Problem
Autonomous and semi-autonomous mobile robots face challenges in navigating facilities with diverse obstacles, including stationary and moving objects, as existing technologies fail to accurately determine the operational state of obstacles, leading to inefficient navigation and potential collisions.
Innovation Solution
The mobile robot captures sensor data, detects obstacles, determines their operational state by analyzing attributes such as movement and operational presence, and selects appropriate navigational constraints to avoid collisions, using a combination of sensors and machine learning algorithms to categorize obstacles as static or dynamic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the robot treats all obstacles as static, then navigation is simplified, but collision risk increases with operational obstacles
Solution Approach 1:
The system dynamically adjusts the classification of obstacles from static to dynamic based on detected operational attributes. Sensors monitor obstacles for signs of operation (motors, hydraulics, controls), and when operational attributes are detected, the obstacle's navigational classification changes from static to dynamic, allowing the robot to adapt its navigation strategy in real-time without manually reconfiguring the system.
Solution Approach 2:
The system changes the operational parameter of obstacle classification from static to dynamic based on detected attributes. By monitoring parameters such as motor activity, hydraulic flow, control inputs, and operational state, the system transitions the obstacle's navigational parameter between static and dynamic categories, enabling adaptive collision avoidance while maintaining simplified navigation for truly stationary objects.
2Measurement precision
If the robot uses multiple sensors to detect operational state, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the detection task across multiple specialized sensors, each monitoring specific operational attributes (motors, hydraulics, controls). Rather than using one complex sensor, the system divides detection into separate channels: motor sensors detect electrical activity, hydraulic sensors detect fluid flow, and control sensors detect operator inputs. This segmentation improves detection accuracy while keeping each sensor component relatively simple and modular.
Solution Approach 2:
The sensor system is designed with multi-functionality, where a single integrated sensing platform monitors multiple operational attributes simultaneously. The sensor array can detect motor activity, hydraulic operation, control inputs, and general operational state using a unified detection framework, reducing overall system complexity compared to separate dedicated sensors for each attribute.
Data Source
AI summary
A method includes: capturing, using a sensor of a mobile robot, sensor data representing a physical environment of the mobile robot; detecting, from the sensor data, an obstacle in the physical environment; responsive to detecting the obstacle, determining from the sensor data whether the obstacle exhibits a predetermined attribute; assigning a first operational state or a second operational state to the obstacle, according to the determination; selecting a navigational constraint based on the assigned operational state; and controlling a locomotive assembly of the mobile robot to navigate the physical environment based on the selected navigational constraint.