LiDAR Robot Routing with Obstacle Frequency Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing serving robots often plan driving routes that are not reasonable for humans, leading to inefficiency and potential safety hazards due to collisions with obstacles or users, as they primarily consider size and collision avoidance without accounting for obstacle frequency.
Innovation Solution
A robot equipped with a LiDAR sensor generates a map of its environment, detects obstacles, calculates obstacle frequency in each area, determines an obstacle area based on weight values and clustering, and generates a driving route that bypasses these areas using a second map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot plans a driving route only considering its size and collision avoidance, then the robot can avoid direct collisions with obstacles, but the robot may select unreasonable routes (e.g., under tables or between chairs) that reduce service efficiency and may cause the robot to get stuck
Solution Approach 1:
The robot performs preliminary actions by generating a map of the environment and detecting obstacles before planning the driving route. This allows the robot to identify potential problem areas in advance and select more reasonable routes that avoid both collisions and inefficient paths
Solution Approach 2:
The robot uses feedback from LiDAR sensor data and obstacle detection results to continuously improve route planning. By analyzing detected obstacles and their positions, the robot adjusts its route selection to balance collision avoidance with service efficiency
2Adaptability or versatility
If the robot drives between tables to serve food, then the robot can access various service areas, but safety problems such as collisions between serving robots and restaurant customers may occur
Solution Approach 1:
The robot performs preliminary obstacle detection and map generation before driving between tables, allowing it to identify customer locations and potential collision risks in advance. This enables the robot to plan safer routes that maintain service area coverage while reducing collision risks
Solution Approach 2:
The robot continuously receives feedback from LiDAR sensors during movement between tables, detecting customers and obstacles in real-time. This feedback allows the robot to adjust its driving path dynamically to avoid collisions while maintaining access to service areas
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 enhances the robot's efficiency and safety by avoiding obstacles and minimizing user inconvenience, improving overall work performance and user satisfaction.
Implementation Method 1
a light detection and ranging (LiDAR) sensor
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A robot may include a LiDAR sensor, and a processor configured to acquire, based on a sensing value of the LiDAR sensor, a first map that covers a space where the robot is located, detect one or more obstacles existing in the space based on the sensing value of the LiDAR sensor, acquire a number of times that each of a plurality of areas in the first map is occupied by the one or more obstacles, based on location information of the one or more obstacles, determine an obstacle area based on the number of times that each of the plurality of areas is occupied by the one or more obstacles, and acquire a second map indicating the obstacle area on the first map to determine a driving route of the robot based on the second map.