Robot Roadmap Routing Using Sensed Unoccupied Warehouse Regions
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Solution Overview
Problem
Existing robotic navigation systems in warehouses and similar environments are inefficient due to the limitations of restricting robots to predefined lanes, which can lead to suboptimal paths and increased travel times, especially when encountering obstacles or unoccupied areas.
Innovation Solution
A method that determines a roadmap with designated regions adjacent to lanes, allowing robots to deviate from fixed paths when unoccupied, using sensor data to adjust routes and optimize travel routes in real-time, enabling opportunistic routing and improved system efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots are restricted to predefined lanes for navigation, then route planning is simplified and collision avoidance is improved, but travel time increases and path efficiency deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static predefined lanes to dynamic route planning. The system determines initial routes using predefined lanes for safety, then dynamically adjusts routes in real-time based on sensor data about unoccupied areas, allowing robots to deviate from fixed paths when conditions permit, thus reducing travel time while maintaining collision avoidance through continuous environmental monitoring
Solution Approach 2:
The system implements feedback by continuously receiving sensor data from robots about their environment and using this information to adjust routes. The central controller monitors real-time conditions and dynamically modifies navigation paths based on feedback about unoccupied areas, enabling the system to optimize travel time while maintaining safety through ongoing environmental awareness
2Device complexity
If robots follow fixed predefined paths, then system complexity is reduced and control is simplified, but adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The system resolves this contradiction by implementing dynamic route determination that adapts to changing environmental conditions. While the overall architecture remains structured with central control, the actual navigation paths are dynamically adjusted based on real-time sensor data about unoccupied areas, allowing the system to maintain relatively simple control structures while achieving high adaptability to environmental changes
Solution Approach 2:
The patent applies parameter changes by modifying navigation parameters (routes) based on environmental conditions. The system changes route parameters dynamically when sensor data indicates unoccupied areas, allowing robots to adapt their paths without fundamentally changing the control system architecture, thus maintaining simplicity while achieving adaptability
3Productivity
If robots use opportunistic routing through unoccupied areas, then travel efficiency is improved and productivity increases, but navigation complexity and real-time computation requirements worsen
Solution Approach 1:
The patent applies segmentation by dividing the navigation problem into distinct phases: initial route determination using predefined lanes, real-time sensor data collection about unoccupied areas, and dynamic route adjustment when opportunities arise. This segmentation allows the system to achieve high travel efficiency through opportunistic routing while managing navigation complexity by breaking it into manageable computational tasks executed at different stages
Solution Approach 2:
The system uses preliminary action by determining initial routes using predefined lanes before execution, and by having robots continuously collect sensor data about their environment in advance. This preliminary preparation enables efficient opportunistic routing during execution without requiring complex real-time computation, as the foundation for route optimization is established beforehand
Data Source
AI summary
Systems and methods related to roadmaps for mobile robots are provided. A computing device can determine a roadmap of an environment. The roadmap can include lanes and a designated region that is adjacent to a first lane of the plurality of lanes and suitable for robotic traversal when unoccupied. The computing device can determine a first route between first and second points in the environment that uses the first lane. The computing device can send a direction to use the first route to a first robot. The computing device can receive, from the first robot, sensor data indicative of an occupied status of the designated region. The computing device can determine a second route between the first and second points through the designated region based on the occupied status of the designated region. The computing device can send a direction to use the second route to a second robot.