Robot Map Motion Primitives With Constraint Regions for Narrow Navigation
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Solution Overview
Problem
Conventional robotic navigation systems lack the necessary context to effectively navigate through constrained environments, often becoming stuck or trapped due to the inability to communicate high-level navigation goals and intentions to lower-level obstacle avoidance systems, which limits their ability to respond to dynamic obstacles and deviations from the path.
Innovation Solution
A method and system for constructing constrained motion primitives from robot maps, which involves generating waypoints, edges, goal regions, and constraint regions using sensor data to provide context for local navigation systems, allowing them to make informed decisions and optimize routes while maintaining safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robotic navigation systems use basic path planning without context information, then the system complexity is low, but the robot becomes stuck or trapped in constrained environments due to inability to communicate high-level navigation goals to lower-level obstacle avoidance systems
Solution Approach 1:
The patent introduces constraint regions as an intermediary layer between high-level path planning and low-level obstacle avoidance systems. These constraint regions encode navigation context and intentions, allowing effective communication between different control levels without increasing overall system complexity. The constraint regions act as a mediator that translates high-level goals into actionable constraints for local navigation.
Solution Approach 2:
The navigation system is segmented into distinct functional layers: high-level path planning generates waypoints, constraint regions encode navigation context, and local obstacle avoidance executes maneuvers within constraints. This segmentation allows each layer to operate independently with well-defined interfaces, improving reliability without requiring complex integration between systems.
2Adaptability or versatility
If the robot follows a rigid pre-planned path without context, then the path planning is simple, but the robot cannot respond effectively to dynamic obstacles and path deviations
Solution Approach 1:
The constraint regions provide dynamic boundaries that adapt to the robot's current state and environment. Rather than rigid pre-planned paths, the system uses dynamic constraint regions that can be adjusted in real-time based on sensor feedback and changing conditions, enabling effective response to dynamic obstacles while maintaining manageable system complexity.
Solution Approach 2:
The system continuously monitors the robot's position relative to constraint regions and adjusts local navigation commands accordingly. This feedback mechanism allows the robot to respond to path deviations and dynamic obstacles by staying within constraint boundaries, providing adaptability without requiring complex re-planning algorithms.
3Reliability
If the robot allows free local navigation without constraints, then the robot can avoid obstacles flexibly, but the robot may deviate from high-level navigation goals and lose localization context
Solution Approach 1:
The constraint regions provide localized quality control by defining specific boundaries and requirements for different segments of the navigation path. Each constraint region encodes local context such as terrain features, safe operating zones, and localization requirements, allowing flexible obstacle avoidance within each local area while maintaining global navigation goals.
Data Source
AI summary
A method includes receiving sensor data of an environment about a robot and generating a plurality of waypoints and a plurality of edges each connecting a pair of the waypoints. The method includes receiving a target destination for the robot to navigate to and determining a route specification based on waypoints and corresponding edges for the robot to follow for navigating the robot to the target destination selected from waypoints and edges previously generated. For each waypoint, the method includes generating a goal region encompassing the corresponding waypoint and generating at least one constraint region encompassing a goal region. The at least one constraint region establishes boundaries for the robot to remain within while traversing toward the target destination. The method includes navigating the robot to the target destination by traversing the robot through each goal region while maintaining the robot within the at least one constraint region.