Mobile Robot Route Recovery Using Local Obstacle Maps
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Solution Overview
Problem
Existing navigation systems for mobile robots, particularly those using topological maps, struggle to effectively navigate around unforeseen obstacles that were not accounted for during the initial mapping process.
Innovation Solution
The system generates a local obstacle map using current sensor data and identifies alternative paths to untraveled waypoints, allowing the robot to avoid obstacles and resume its original navigation route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the robot uses a topological map for navigation, then the navigation system can operate with reduced computational resources, but the robot cannot effectively navigate around unforeseen obstacles that were not accounted for during initial mapping
Solution Approach 1:
The navigation system is segmented into two distinct components: a global topological map for overall route planning and a local metric map for obstacle avoidance. The topological map provides the high-level navigation framework with reduced computational requirements, while the local metric map handles real-time obstacle detection and path adjustment, resolving the contradiction between computational efficiency and adaptability to unforeseen obstacles.
Solution Approach 2:
A local metric map acts as an intermediary between the global topological map and the robot's immediate environment. When obstacles are detected by sensors, the local metric map processes this information and generates adjusted movement instructions, serving as a mediator that translates global navigation goals into locally adaptive actions without requiring the entire system to switch to computationally intensive full metric mapping.
2Reliability
If the robot generates a full metric map in real-time, then the robot can accurately navigate around obstacles, but the computational resources and processing time increase significantly
Solution Approach 1:
Instead of generating a complete metric map of the entire environment, the system creates a localized metric map only in the immediate vicinity of the robot. This local quality approach focuses computational resources on the relevant area for obstacle avoidance while maintaining accuracy where needed, significantly reducing processing time compared to full environment mapping.
Solution Approach 2:
The system performs partial metric mapping only when and where obstacles are detected, rather than continuously mapping the entire environment. This partial action approach generates metric map data selectively in the robot's local vicinity, providing sufficient accuracy for obstacle avoidance without the excessive computational burden of complete environmental mapping.
3Adaptability or versatility
If the robot continuously updates the global map with new obstacle information, then the navigation system maintains high adaptability, but the system complexity and computational load increase
Solution Approach 1:
The system extracts obstacle avoidance functionality from the global topological map and places it in a separate local metric map module. When obstacles are detected, only the local metric map is updated with new information, while the global topological map remains unchanged. This extraction reduces system complexity by isolating real-time updates to a localized component rather than requiring continuous global map regeneration.
Data Source
AI summary
A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include receiving a navigation route for a mobile robot. The navigation route includes a sequence of waypoints connected by edges. Each edge corresponds to movement instructions that navigate the mobile robot between waypoints of the sequence of waypoints. While the mobile robot is traveling along the navigation route, the operations include determining that the mobile robot is unable to execute a respective movement instruction for a respective edge of the navigation route due to an obstacle obstructing the respective edge, generating an alternative path to navigate the mobile robot to an untraveled waypoint in the sequence of waypoints, and resuming travel by the mobile robot along the navigation route. The alternative path avoids the obstacle.