Swarm Agent Control Using Probability Maps for Noisy Target Detection
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Solution Overview
Problem
The autonomous control of swarms of mobile agents, such as drones or robots, in noisy and uncertain environments for efficient target detection and collaboration remains a challenging task.
Innovation Solution
A method and system utilizing neural networks for reinforcement learning to iteratively control the movement of agents, integrating sensor data to optimize target detection by updating probability maps and adjusting network weights based on rewards and errors, enabling efficient collaboration among agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autonomous control methods are used for swarms of mobile agents, then the system structure is relatively simple, but the target detection accuracy and collaboration efficiency deteriorate in noisy and uncertain environments
Solution Approach 1:
The patent implements feedback mechanisms where agents continuously share sensor data and probability maps with the central controller, which updates the neural network model and redistributes updated probability maps to all agents. This closed-loop feedback system enables adaptive response to noisy environments while maintaining coordinated swarm behavior, resolving the contradiction between detection accuracy and system complexity.
Solution Approach 2:
The patent introduces a central controller as an intermediary that coordinates communication between agents and manages the neural network model. This intermediary consolidates the complexity of target detection and collaboration logic, allowing individual agents to maintain simpler autonomous navigation capabilities while achieving high detection accuracy through coordinated information sharing and probabilistic reasoning.
2Measurement precision
If agents share information on different levels with various sensors, then the target detection accuracy improves, but the communication and data processing complexity increases
Solution Approach 1:
The patent transforms raw sensor data from multiple agents into probability maps that represent the likelihood of target presence in different spatial locations. This parameter transformation consolidates diverse sensor inputs into a unified probabilistic representation, enabling accurate target detection while simplifying the complexity of processing data from various sensors operating at different information levels.
Solution Approach 2:
The patent divides the complex task of target detection into segmented probability maps for different spatial regions, with each agent contributing sensor data to specific zones. This segmentation allows the system to process information from multiple agents in a structured manner, improving detection accuracy through comprehensive coverage while managing data processing complexity through regional decomposition.
3Productivity
If the neural network model processes all sensor data from multiple agents, then the detection speed improves, but the computational resources and energy consumption increase
Solution Approach 1:
The patent implements partial processing where the neural network model processes sensor data selectively based on the current probability map and mission objectives. Rather than continuously processing all available sensor data from all agents, the system focuses computational resources on processing data from agents in regions of interest or where detection uncertainty is highest, thereby improving detection speed while reducing overall energy consumption.
4Productivity
If agents autonomously navigate and detect targets independently, then the system complexity is reduced, but the collaboration efficiency and detection coverage deteriorate
Solution Approach 1:
The patent merges the autonomous navigation capabilities of individual agents with centralized coordination through probability map sharing. Each agent maintains independent navigation and sensor processing, but collaborates by contributing data to and receiving updated probability maps from the central controller. This merging approach achieves comprehensive detection coverage through coordinated swarm behavior while managing collaboration complexity through a standardized probability map exchange protocol.
Data Source
AI summary
A system and method of distributed controlling of movement of a plurality of agents may include: associating each agent with a respective turn, and at each turn, performing the following steps by the associated agent: receiving a probability map, comprising probability values representing probability of location of one or more targets in an area of interest; applying a Neural Network (NN) model on the probability map to produce Predicted Cumulative Reward (PCR) values, where each PCR value (i) corresponds to a respective optional movement action of the agent and (ii) predicts a future cumulative reward representing aggregation of data in the probability map by the plurality of agents; moving the associated agent based on the PCR values; receiving a signal indicating location of targets in the area of interest; updating the probability map, based on the received signal; and transferring the turn to subsequent agents of the plurality of agents.