Sensor Tasking Planning System Using Branch and Bound Heuristics
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Solution Overview
Problem
Scheduling and tasking of unmanned aerial vehicles (UAVs) to optimize information collection is a complex combinatorial problem, leading to inefficient resource utilization and poor system performance due to the overwhelming analysis required by human operators in military surveillance and tracking applications.
Innovation Solution
An improved planning system using a branch and bound approach with heuristic methods, including a progressive lower bound and hybrid local-global branch and bound techniques, to expedite the determination of optimal sensor tasking and management plans for near real-time decision-making, reducing the search space and computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human operators manually schedule and task UAVs to optimize information collection, then flexibility and adaptability are maintained, but the complexity of analysis overwhelms operators leading to inefficient resource utilization and poor system performance
Solution Approach 1:
The system implements automated sensor resource allocation and scheduling that performs tasking decisions autonomously without requiring manual operator intervention for each scheduling decision. The branch and bound algorithm with heuristic methods enables the system to self-manage the complex combinatorial optimization problem of UAV tasking, thereby reducing operator workload while maintaining or improving system performance through deterministic optimization
Solution Approach 2:
The patent replaces the mechanical human cognitive processing system with an automated computational system based on branch and bound algorithms and heuristic methods. This substitution enables the system to handle the complex combinatorial optimization of sensor tasking schedules deterministically and efficiently, overcoming the limitations of human operator capacity while maintaining operational flexibility
2Measurement precision
If exhaustive search methods are used to solve the combinatorial optimization problem of sensor tasking, then optimal solutions are guaranteed, but the computational time and resources become prohibitively large
Solution Approach 1:
The patent segments the search space by dividing it into manageable branches that can be explored systematically. The branch and bound method partitions the combinatorial optimization problem into smaller sub-problems represented as decision trees, where each node represents a partial solution. This segmentation allows the algorithm to explore only promising regions of the search space while pruning irrelevant branches, thereby finding optimal solutions without exhaustive search
Solution Approach 2:
The patent applies preliminary action through the use of heuristic methods that provide initial estimates and lower bounds for the optimization problem. These heuristics pre-evaluate potential solutions and establish bounds that guide the branch and bound search, allowing the algorithm to prune branches that cannot lead to optimal solutions. This preliminary evaluation significantly reduces the computational time required while maintaining solution optimality
3Productivity
If deterministic optimization methods are applied to sensor tasking schedules, then optimal resource allocation is achieved, but the computational complexity increases significantly
Solution Approach 1:
The patent changes the parameters of the optimization approach by combining exact deterministic methods (branch and bound) with heuristic estimation techniques. This hybrid approach modifies the computational parameters by introducing adaptive pruning criteria and heuristic-guided search strategies that reduce the effective search space. The result is a deterministic optimization method that achieves optimal resource allocation with reduced computational complexity compared to traditional exhaustive or purely heuristic approaches
Data Source
AI summary
To improve the scheduling and tasking of sensors, the present disclosure describes an improved planning system and method for the allocation and management of sensors. In one embodiment, the planning system uses a branch and bound approach of tasking sensors using a heuristic to expedite arrival at a deterministic solution. In another embodiment, a progressive lower bound is applied to the branch and bound approach. Also, in another embodiment, a hybrid branch and bound approach is used where both local and global planning are employed in a tiered fashion.