Autonomous Mobile Robot Route Planning for Crowded Moving Obstacles
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Solution Overview
Problem
Autonomous mobile robots face challenges in navigating through densely crowded environments, such as train stations or airports, as they struggle to avoid collisions with multiple moving objects like pedestrians, leading to deviations from their intended routes and difficulties in updating their paths in real time.
Innovation Solution
An autonomous mobile robot system that includes a determination unit for self-positioning, a detection unit for object positioning and velocity, a first arithmetic unit for calculating course directions, a second arithmetic unit using a trained model to estimate future positions without collisions, and a movement control unit for updating routes based on these calculations, allowing for efficient navigation through crowded spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous mobile robot continuously avoids moving objects by deviating from the original route, then collision avoidance is improved, but the difficulty of updating the moving route in real time increases
Solution Approach 1:
The system pre-calculates multiple candidate routes before the robot starts moving. When obstacles are detected, the system can immediately switch to a pre-computed alternative route without performing complex real-time path planning, thus resolving the contradiction between collision avoidance and route update complexity
Solution Approach 2:
The route planning system dynamically adapts by having multiple pre-computed routes ready. The robot can flexibly switch between different pre-calculated routes based on real-time obstacle detection, maintaining both collision avoidance capability and computational efficiency
2Ease of operation
If the autonomous mobile robot updates the moving route in real time to avoid moving objects, then the smoothness of movement to destination is improved, but the calculation time and processing load increase
Solution Approach 1:
Multiple candidate routes are calculated in advance before the robot begins navigation. This preliminary calculation eliminates the need for time-consuming real-time path recomputation when obstacles are encountered, reducing calculation time while maintaining movement smoothness through immediate route switching
Solution Approach 2:
The system combines offline route calculation with online obstacle detection. By merging pre-computed route data with real-time sensor information, the system achieves smooth navigation without the computational burden of full real-time path planning
3Reliability
If the autonomous mobile robot examines multiple candidate course directions, then the quality of the determined moving route is improved, but the calculation time increases
Solution Approach 1:
Multiple candidate course directions and their corresponding routes are calculated in advance. This allows the system to evaluate multiple options quickly during runtime by simply comparing pre-computed route qualities rather than performing exhaustive real-time calculations, thus improving route quality without excessive calculation time
Solution Approach 2:
The system calculates more candidate routes than strictly necessary (excessive action) during the offline phase. This over-preparation ensures that high-quality routes are available for immediate selection, trading offline computational effort for online speed
Data Source
AI summary
An autonomous mobile robot includes a first arithmetic unit configured to calculate a course direction based on an own position, a moving-object position, and a moving-object velocity vector, the course direction being a direction in which the autonomous mobile robot should travel, a second arithmetic unit configured to input the own position, the moving-object position, the moving-object velocity vector, and the course direction into a trained model and thereby calculate an estimated position, the trained model being a model that has been trained, the estimated position being a position at which the autonomous mobile robot is estimated to arrive a predetermined time later without colliding with the moving object, a generating unit configured to generate a remaining route from the estimated position to a destination, and a movement control unit configured to control a movement to the destination based on the course direction and the remaining route.