Robot Navigation in Elevators Using Dynamic Subspace Priorities
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Solution Overview
Problem
Existing robot navigation systems fail to efficiently plan travel routes in specific spaces, such as elevators, considering both travel information and object locations, leading to potential interference with users and suboptimal service delivery.
Innovation Solution
A robot equipped with a memory for map information, sensors for object detection, and a processor that identifies priority areas within a stopover location, updates priority information based on object locations and predicted departure times, and controls its movement accordingly to optimize route planning and user convenience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot travels in a specific space without considering object locations and priorities, then the navigation system is simple, but the robot interferes with users and service delivery is suboptimal
Solution Approach 1:
The specific space is divided into multiple subspaces, each with assigned priority levels. The processor segments the navigation decision-making into evaluating multiple subspaces and their priorities, allowing the robot to navigate efficiently by selecting subspaces with appropriate priority levels while maintaining manageable system complexity through structured division.
Solution Approach 2:
The system pre-identifies multiple subspaces and assigns priority information to each subspace before the robot enters the specific space. This preliminary classification and prioritization enables the robot to make efficient navigation decisions without real-time complex calculations, improving service delivery while keeping the navigation system relatively simple.
2Speed
If the robot updates priority information based on object locations and departure times, then the navigation efficiency is improved, but the computational complexity increases
Solution Approach 1:
The priority information of subspaces is dynamically updated based on real-time factors such as object locations and predicted departure times. The processor adjusts priority levels adaptively, allowing the robot to optimize navigation speed by selecting subspaces with updated priority information while managing computational complexity through focused real-time updates rather than continuous complex calculations.
3Productivity
If the robot identifies multiple subspaces with priority information, then the service delivery is optimized, but the information processing load increases
Solution Approach 1:
Different subspaces are assigned different priority levels based on their specific characteristics and the robot's current needs. The processor focuses information processing on relevant subspaces with appropriate priority levels rather than uniformly processing all spatial information, optimizing service delivery while reducing overall information processing load through selective attention to high-priority subspaces.
Data Source
AI summary
Disclosed is a robot traveling in a specific space. The robot includes a memory in which map information on a driving space is stored, one or more sensors, a driver; and one or more processors configured to identify the specific space as a plurality of subspaces when the specific space is included as a stopover on a traveling route identified based on the map information, identify priority information of each of the plurality of subspaces, identify a location of an object within the specific space based on sensing data acquired through the one or more sensors, update the priority information of each of the plurality of subspaces based on at least one of the identified location of the object or a predicted departure time to a next stopover within the traveling route, and control the driver to move based on the updated priority information.