Vehicle Positioning for Multi-Target Interception Scheduling
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Solution Overview
Problem
Conventional methods for positioning vehicles relative to moving targets are hindered by uncertainty in target location, leading to reduced planning optimality, shorter planning horizons, and increased fuel expenditure, compromising safety and requiring continuous communication links.
Innovation Solution
A computer-implemented method that uses Mixed-Integer Linear Programming to optimize vehicle positioning and scheduling, ensuring vehicles can travel between targets within their maximum velocity, allowing for longer planning horizons and improved task execution schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional planning systems reduce planning horizon to a single task when dealing with moving targets, then uncertainty about target location is managed, but plan optimality is reduced and less work is completed in a given amount of time
Solution Approach 1:
The system performs preliminary computation of positions for multiple future tasks by predicting target locations at different future times. The Mixed-Integer Linear Programming formulation calculates optimal positions for a vehicle across multiple tasks in advance, considering predicted target movements, thereby extending the planning horizon beyond single-task conventional approaches while managing uncertainty through prediction models
Solution Approach 2:
The system dynamically adjusts the planning horizon and task schedules based on predicted target movements and vehicle capabilities. The optimization formulation incorporates time-varying target positions and vehicle velocity constraints to dynamically determine optimal task execution times and positions, allowing the planner to adapt to changing conditions while maintaining multi-task optimization
2Device complexity
If conventional planning systems reduce planning horizon to a single task, then computational complexity is reduced, but fuel expenditure constraints are more difficult to take into account
Solution Approach 1:
The system changes the formulation parameters from conventional single-task planning to a Mixed-Integer Linear Programming formulation that optimizes multiple tasks simultaneously. The objective function incorporates fuel expenditure constraints and vehicle velocity limits, transforming the planning problem into an optimization framework where fuel efficiency is explicitly considered alongside task completion requirements
Solution Approach 2:
The optimization formulation serves multiple functions simultaneously: it schedules task execution times, determines vehicle positions, manages fuel expenditure constraints, and ensures timely target interception. This multi-functional approach integrates what were previously separate considerations into a unified optimization framework
3Ease of operation
If conventional planning systems reduce planning horizon to a single task, then task scheduling simplicity is maintained, but safety is compromised as vehicles will have to receive further plan updates before they can be recovered
Solution Approach 1:
The system performs preliminary computation of optimal positions and schedules for multiple future tasks, allowing the vehicle to receive a comprehensive plan in advance rather than requiring continuous updates. The Mixed-Integer Linear Programming formulation calculates the complete trajectory and task execution schedule beforehand, enabling the vehicle to operate autonomously for extended periods without communication interruptions
Solution Approach 2:
The system ensures continuous task execution by computing positions and schedules that account for vehicle movement between tasks. The optimization formulation guarantees that the vehicle can continuously progress through the task sequence without requiring interruption for plan updates, maintaining operational continuity and safety
Data Source
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AI summary
A method and system for positioning a vehicle in relation to each moving target of an ordered set of moving targets. Each of the moving targets moves from an initial position at a constant velocity. Embodiments can compute (602) an estimated time for the vehicle to be positioned within a predetermined proximity of one of the moving targets; compute (604) an estimated location of the moving target at the estimated time, based on a current position of the moving target and the constant velocity of the moving target, and compute (606) a required velocity for the vehicle to move from its current position to reach the estimated location by the estimated time. If the required velocity is less than or equal to a maximum velocity of the vehicle, outputting (312) the estimated time and the estimated location for use in positioning the vehicle.