5 results about "Combat simulation" patented technology

The application discloses a kind of based on layered multi-agent deep reinforcement learning's unmanned aerial vehicle cluster task planning algorithm and its evaluation method, belong to unmanned aerial vehicle technical field, solve the current unmanned aerial vehicle cluster collaborative task planning problem solving algorithm generally exists online solving efficiency is low, large amount of computation, poor stability, difficult to apply to complex dynamic scene etc.The application first establishes the cluster task allocation and flight path planning problem part Markov decision process model respectively;Second, a layered deep reinforcement learning framework based on multi-agent deep reinforcement learning is proposed, through coupled training, hierarchical solving method, the task allocation and flight path planning associated coupling problem is solved simultaneously;Also based on UE4 and Airsim build unmanned aerial vehicle cluster combat simulation environment, design evaluation index, the result shows that the application has good performance in cluster task planning, can effectively improve the intelligentization and actual combat level of cluster combat.

The present application belongs to the field of military command control and combat simulation, and relates to a local rapid reconstruction method for a killing network based on a complementary network strategy. The method acquires current state information of the killing network and detects failed nodes or edges, constructs a local repair area centered on the failed nodes, establishes a multi-objective optimization model in the repair area aiming to maximize task recovery benefits, minimize resource use costs and minimize task completion delays, solves to obtain a Pareto optimal solution set by using a TM-NSGA-III algorithm guided by a conversion matrix, and finally screens and executes a reconstruction scheme according to a battlefield situation. The present application reduces the search space by localized modeling, combines the conversion matrix to inherit the effective information of the original network, realizes rapid and high-quality reconstruction of the killing network, and improves the resilience and self-healing ability of the killing network in a dynamic battlefield environment.

This invention provides a method, system, device, and medium for generating military combat simulation schemes, belonging to the field of military combat simulation technology. The method includes: parsing combat tasks described in natural language according to a preset large language model to generate structured task data; automatically generating a problem description file conforming to the planning domain definition language specification based on the structured task data and a preset combat domain knowledge base; inputting the problem description file and a predefined domain definition file into a planning domain definition language planner for solving, outputting a feasible action sequence that satisfies all preset hard constraints; using the feasible action sequence as the initial strategy, executing and evaluating it in a simulation environment; iteratively optimizing the action sequence using a reinforcement learning algorithm based on the evaluation results, generating an optimized action sequence as the final simulation scheme. This method solves the problem of low feasibility and rule compliance in existing simulation schemes.

This utility model discloses a team-based collaborative combat simulation robot, belonging to the field of robotics technology, including a perception system, a decision-making system, a motion system, a weapon system, and a communication system. The perception system acquires information about the robot's surrounding environment and its own state. The decision-making system formulates execution plans based on the environmental and state information, including action and confrontation decisions. The motion system executes actions based on action decisions. The weapon system performs confrontation tasks based on confrontation decisions. The communication system enables information exchange. This utility model achieves this by: accurately capturing information through the perception system to provide a basis for decision-making; efficiently generating plans through the decision-making system; flexibly executing actions through the motion system; accurately completing confrontation tasks through the weapon system; and ensuring information exchange through the communication system. The coordinated operation of these systems enables the robot to perform tasks effectively in complex scenarios, improving overall combat effectiveness and adaptability.

This invention provides a training platform and method for air combat command and decision-making intelligent agents, comprising: a support layer: providing general hardware and software support for modeling, training, demonstration, and verification of command and decision-making intelligent agents, as well as system confrontation modeling and simulation; a resource layer: building a combat simulation model library based on a general simulation platform to quickly build a learning environment to meet different air combat command and decision-making intelligent agent training needs, and building an intelligent agent model library based on a general reinforcement learning platform to meet different command and decision-making intelligent agent training needs; and an application layer: decomposing actual command and decision-making problems according to the application needs of combat command and decision-making intelligence, and specifically integrating intelligent agent training application systems to carry out parallel training of command and decision-making intelligent agents and related technology verification applications. This invention flexibly and quickly constructs intelligent agents and their learning environments, and, guided by military business rules, combines multi-agent parallel training with large scenario samples to improve the training efficiency and generalization and expansion capabilities of command and decision-making intelligent agent models.