Unmanned Machine Formation Control for Probabilistic Target Tracking
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Solution Overview
Problem
Current methods for capturing a target object using unmanned machines are labor-intensive and manpower-consuming, leading to inefficiencies and potential misses due to insufficient manpower, especially in situations requiring rapid response and tracking.
Innovation Solution
A control device and method that includes an information acquisition and transmission unit, target object detection unit, presence probability calculation unit, formation determination unit, and operation setting unit, which work together to determine the formation and operation of multiple unmanned machines based on the presence probability distribution of the target object, enabling efficient tracking and capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple unmanned machines are used to search for and capture a target object, then the reliability of target capture is improved, but the device complexity and coordination difficulty increase
Solution Approach 1:
The patent merges multiple unmanned machines into a coordinated swarm system that shares information and coordinates actions through a common control framework. The formation determination unit integrates position data from multiple machines to establish cooperative search patterns, while the operation amount calculation unit harmonizes their movements to maintain optimal formations during target pursuit, thereby improving capture reliability without overwhelming coordination complexity
Solution Approach 2:
The system dynamically adjusts the formation and operational parameters of unmanned machines based on real-time target position data and machine states. The formation determination unit continuously recalculates optimal formations as machines move and detect targets, while the operation amount calculation unit adapts speed and trajectory commands to maintain formation integrity during dynamic pursuit, enabling flexible adaptation to changing search conditions
2Productivity
If unmanned machines autonomously switch missions based on situation, then the productivity and response time are improved, but the extent of automation and decision-making complexity increase
Solution Approach 1:
The system implements autonomous mission switching by monitoring changes in key parameters such as target detection status, formation position, and machine velocity. When parameters indicate a need for mission change (e.g., target detected, formation disrupted), the control device automatically transitions between search, tracking, and pursuit missions by adjusting operational parameters and formation configurations, thereby improving productivity through automated parameter-based decision-making
Solution Approach 2:
The autonomous mission switching mechanism incorporates continuous feedback from sensor data, position information, and formation status. The control device monitors feedback signals indicating target detection, machine relative positions, and mission effectiveness, and automatically adjusts missions based on this feedback loop, enabling intelligent adaptation to changing situations without requiring complex centralized decision-making
3Measurement precision
If the formation of unmanned machines is optimized based on presence probability distribution, then the measurement precision of target location is improved, but the calculation complexity and processing time increase
Solution Approach 1:
The system performs preliminary calculations of presence probability distributions and optimal formation configurations based on predicted target movement patterns and historical data. By pre-computing formation strategies and probability maps in advance, the formation determination unit can quickly adjust machine positions to high-probability areas without requiring complex real-time calculations, thereby improving target location precision while managing computational complexity through advance preparation
Data Source
AI summary
A control device acquires position information of an unmanned machine subject to control, and position information of an unmanned machine not subject to control; attempts to detect a target object, using a sensor signal from a sensor mounted in at least one of the unmanned machines; calculates a presence probability distribution of the target object based on information on a position and a time at which detection of the target object is successful; determines a formation of the unmanned machines based on the presence probability distribution of the target object; calculates an operation amount of the unmanned machine subject to control, based on the formation; and performs operation setting on the unmanned machine subject to control, according to the calculated operation amount.