Loadable Vehicle Routing With Mass-Aware MUTEX Coordination
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Solution Overview
Problem
Existing vehicle route planning systems fail to optimize loading and unloading operations in mutually exclusive zones, leading to energy waste, underutilization of resources, and unnecessary hardware wear due to inadequate consideration of vehicle mass variations and loading/unloading times.
Innovation Solution
A computer system that plans vehicle routes by optimizing loading and unloading times and amounts using a dynamic vehicle model that includes mass as a state variable, constrained by safety and operational objectives, solving a mixed-integer quadratic program to determine vehicle crossing orders and control signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If vehicles leave loading/unloading stations as soon as loading/unloading terminates, then the vehicles can reduce waiting time, but this forces vehicles into dense traffic which wastes energy and increases hardware wear
Solution Approach 1:
The system performs preliminary trajectory planning that anticipates future traffic conditions and loading/unloading requirements. Vehicles are scheduled to remain at stations longer than minimum required, with trajectories pre-calculated to avoid dense traffic periods, thus preventing energy waste before it occurs rather than reacting to it
Solution Approach 2:
The trajectory planning system dynamically adjusts vehicle schedules based on real-time and historical traffic data, loading/unloading requirements, and vehicle positions. This dynamic optimization determines optimal departure times that balance waiting time reduction with energy efficiency, avoiding fixed rigid schedules
2Device complexity
If simple rulesets such as traffic lights are used for MUTEX zone coordination, then the system complexity is reduced, but the mission goals (transport load, arrival time, energy efficiency) are not satisfied
Solution Approach 1:
The coordination system segments the complex trajectory optimization problem into multiple manageable components: MUTEX zone identification, vehicle scheduling, trajectory generation, and real-time coordination. This segmentation allows complex optimization to be achieved through modular, manageable subsystems rather than a monolithic complex system
Solution Approach 2:
The system introduces a central coordination entity that acts as an intermediary between vehicles and MUTEX zones. This mediator manages the complex optimization by processing vehicle requests, determining optimal schedules, and coordinating trajectories, thus achieving high mission goal satisfaction without requiring each vehicle to independently manage complex coordination
3Loss of energy
If vehicles spend more time at loading/unloading zones to avoid conflicts and optimize trajectories, then energy efficiency and safety improve, but the overall mission time increases
Solution Approach 1:
The system performs preliminary trajectory planning that anticipates future traffic conditions and loading/unloading requirements. Vehicles are scheduled to remain at stations longer than minimum required, with trajectories pre-calculated to avoid dense traffic periods, thus preventing energy waste before it occurs rather than reacting to it
Solution Approach 2:
The optimization system dynamically adjusts multiple parameters including departure time, arrival time, loading/unloading duration, and trajectory shape. By changing these parameters simultaneously rather than sequentially, the system achieves optimal balance between energy efficiency and mission time
Data Source
AI summary
A computer-implemented method plans routes for a plurality of vehicles operating in a common environment which includes a plurality of mutual exclusion zones by obtaining a predefined objective function; solving a first optimization problem for a first objective function derived from the predefined objective function, to obtain a vehicle crossing order at each mutual exclusion zone, wherein the first optimization problem is subject to safety constraints; and solving an optimal-control problem for the predefined objective function subject to the obtained vehicle crossing order at the MUTEX zones and subject to the safety constraints, to obtain a control signal for each of the vehicles. At least some of the vehicles are loadable, and the OCP is constrained by a dynamic vehicle model representing evolution with respect to path length for each vehicle, in which time, path speed and mass are state variables.