Autonomous Route Scheduling for Time-Windowed Mobile Tasks
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Solution Overview
Problem
In limited outdoor areas like construction sites, autonomous vehicles need to efficiently transport loads between locations within a scheduled time frame, while avoiding obstacles and optimizing task timing to ensure safety and efficiency.
Innovation Solution
A route generation device for autonomous mobile bodies that generates routes based on allowable arrival times, task specifics, and required time periods, allowing the vehicles to arrive at destinations within set time windows, avoid obstacles, and manage overlapping tasks, while minimizing load and optimizing task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the autonomous mobile body travels at high speed to reach the destination within the limited scheduled period, then the arrival time is improved, but the safety and stability of task execution deteriorate
Solution Approach 1:
The system performs preliminary calculation of the required time period for each task before route generation. By pre-determining the time needed for loading, unloading, and other tasks based on their specifics, the system can plan the entire route and schedule in advance, ensuring that high-speed travel does not compromise task execution safety. The controller calculates required time periods for all tasks beforehand and integrates them into the route planning to guarantee timely completion without sacrificing reliability.
Solution Approach 2:
The system dynamically adjusts the route based on multiple allowable arrival time periods assigned to different destinations. Instead of using a fixed schedule, the controller can select from multiple time period options for each destination, allowing flexible optimization of travel speed and task timing. This dynamic approach enables the system to achieve fast arrival when conditions permit while maintaining safety margins when needed.
2Productivity
If multiple tasks with different specifics are assigned to one destination, then the productivity is improved, but the complexity of route generation and task scheduling increases
Solution Approach 1:
The system segments tasks by their specific requirements (loading, unloading, inspection, etc.) and assigns different required time periods to each task type. By dividing the overall task portfolio into standardized categories with predefined time requirements, the controller can systematically manage multiple tasks at one destination without being overwhelmed by complexity. Each task segment is evaluated independently for its time requirements, then integrated into the overall route plan.
Solution Approach 2:
The system changes the parameter of time period allocation based on task specifics. For each task type (loading, unloading, inspection), the system assigns a specific required time period parameter that reflects its complexity and resource requirements. This parameter-based approach allows the controller to automatically adjust the schedule for multiple tasks at a single destination by varying the time period parameter according to each task's characteristics, thereby managing complexity through standardized parameter assignment.
3Productivity
If the autonomous mobile body loads a large number of loads to reduce travel frequency, then the productivity is improved, but the required time period for loading and unloading increases
Solution Approach 1:
The system applies partial loading strategies where the autonomous mobile body may not always load to maximum capacity. Instead, it optimizes load quantities based on the allowable arrival time periods and task requirements at destinations. Sometimes loading fewer loads is preferable to maintain flexibility in scheduling and avoid excessive loading/unloading time that would negate the benefits of reduced travel frequency. The controller calculates the optimal load quantity that balances transport efficiency with time constraints.
Solution Approach 2:
The system uses feedback from the calculated required time periods to adjust loading strategies. When the system determines that loading a full load would cause the total time (travel + loading + unloading) to exceed the allowable arrival time period, it receives feedback to reduce the load quantity or adjust the route. This feedback mechanism allows the system to dynamically optimize between transport efficiency and time consumption, ensuring that increased load capacity does not inadvertently increase total task completion time beyond acceptable limits.
Data Source
AI summary
The route generation device includes a controller configured to generate, in response to acceptance of an allowable arrival time period assigned to at least one of the plurality of destinations, an input of information regarding specifics of a task to be performed at the at least one destination, and setting of a required time period according to the specifics of the task, a route that allows the autonomous mobile body to arrive at the at least one destination within the allowable arrival time period, based on the required time period. The controller is configured to control and cause the autonomous mobile body to travel along the generated route.