Semantic Map Generation for Mobile Security and Inventory Robots
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robots lack the capability to effectively integrate with commercial and industrial settings, particularly in environments requiring frequent human-robot interactions, such as retail and security environments, due to limitations in communication and operational flexibility.
Innovation Solution
A mobile robot system that can perform various functions including security operations, infrastructure management, navigation, and inventory management, equipped with sensors and communication tools to interact with building infrastructure and personnel, and generate semantic maps for enhanced situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots are deployed in commercial and industrial settings, then security operations and inventory management capabilities are improved, but communication and operational flexibility limitations prevent effective integration with building infrastructure
Solution Approach 1:
The robot is designed with multi-functional capabilities including security monitoring, inventory management, navigation, and communication with building infrastructure systems. A single robot platform performs diverse functions by interfacing with different building systems (HVAC, lighting, security cameras, access control), eliminating the need for separate specialized devices and improving overall system integration.
Solution Approach 2:
The robot acts as an intermediary between building infrastructure systems and users or control centers. It communicates with building systems through standardized protocols, translating between different system interfaces and providing a unified communication channel that simplifies integration complexity while enabling versatile operations.
2Measurement precision
If the robot uses multiple sensors to generate semantic maps, then situational awareness and object identification are improved, but system complexity and processing requirements increase
Solution Approach 1:
The robot combines multiple sensor types (cameras, LIDAR, RFID readers, microphones) into a unified sensing system that generates comprehensive semantic maps. The sensors work together synergistically, with each sensor type contributing specific information that complements the others, improving object identification accuracy while sharing common processing resources to manage complexity.
Solution Approach 2:
The robot autonomously processes sensor data to generate and update semantic maps without requiring external intervention. The onboard processing system automatically fuses data from multiple sensors, identifies objects and their states, and maintains updated environmental models, reducing the need for complex external processing infrastructure.
3Reliability
If the robot performs real-time security monitoring and generates detailed maps, then security detection capability is improved, but energy consumption and processing load increase
Solution Approach 1:
The robot performs security monitoring and map generation in periodic cycles rather than continuously. It alternates between active sensing phases (collecting data from sensors) and processing phases (analyzing data and updating maps), allowing energy-intensive operations to be distributed over time and reducing peak power consumption while maintaining reliable security detection.
Solution Approach 2:
The robot applies partial processing to sensor data in real-time, focusing computational resources on detecting security-relevant features rather than processing all sensor data at full resolution. This selective processing approach maintains security detection reliability by prioritizing critical analysis while reducing overall energy consumption through optimized resource allocation.
Data Source
AI summary
A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory. Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.