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Chapter 10: Swarm Collaboration and Game-Theoretic Adversarial Interactions

The concept of swarms originated from biological research. French zoologist Grasse, based on observations of termite nest-building behavior, first proposed the concept of stigmergy—a mechanism of intermittent coordination among individuals that enables complex intelligent activities without centralized planning or direct communication. This marked the beginning of the autonomous swarm concept entering human understanding and gradually developing. Extending from biological systems to Multi-Agent Systems (MAS), the concept of swarms has continued to evolve and enrich.

10.1 Background and Theory

Intelligent unmanned swarm systems represent a systemic integration and capability leap of the technologies discussed in previous chapters, signifying the advancement of intelligent unmanned systems to a higher level. Such systems must possess self-organization, self-adaptation, and fault-tolerance capabilities to flexibly execute diverse tasks—including cooperative formation, area coverage, target tracking, and game-theoretic adversarial interactions—in dynamic, even unstructured environments.

10.1.1 Swarm System Concept

The concept of a swarm initially emerged from biological research. Inspired by observations of termite nest-building behavior, the term stigmergy was first introduced, describing an indirect coordination mechanism among individuals that accomplishes complex intelligent tasks without centralized planning or direct communication.

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10.1.2 Development of Swarm System Research

The evolution of swarm system research can be divided into three phases:
- Foundational theory and conceptual exploration phase (1990s to early 2000s),
- Practical application and platform development phase (mid-2000s to 2010s),
- Future-oriented and integrated innovation phase (2010s to present).

10.2 Framework and Interfaces

The RflySim toolchain provides robust support and a reusable paradigm for the development and application of intelligent unmanned swarm systems. It supports the swarm collaborative control architecture and interface usage, enabling a complete engineering workflow—from algorithm development and simulation validation to real-platform deployment.

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10.2.1 Swarm Collaborative Control and Formation

RflySim supports one-click initiation of multi-aircraft swarm simulations, offering development environments for both MATLAB/Simulink and Python. It supports software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations for multiple aircraft, as well as distributed swarm simulations across multiple computers within a local area network.

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10.2.2 Intelligent Game-Theoretic and Adversarial Interactions

The toolchain enables intelligent unmanned swarms to flexibly perform diverse tasks—including cooperative formation, area coverage, target tracking, and game-theoretic adversarial interactions—in dynamic and even unstructured environments. Compared to single unmanned platforms, this significantly enhances overall reliability, task adaptability, and execution efficiency.

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10.3 Showcase of Representative Cases

Large-scale Swarm Search and Target Localization Simulation:

Vertical Takeoff and Landing (VTOL) Aircraft Multi-Agent Formation:

10.4 Course-Linked Video Lectures

Public Lecture Replay (Session 8: Swarm Collaboration and Game-Theoretic Adversarial Interactions):

10.5 Chapter Experiment Cases

The relevant verification experiments and guided cases for this chapter are stored in the [Installation Directory]\RflySimAPIs\10.RflySimSwarm folder.

10.5.1 Interface Learning Experiments

Stored in the 10.RflySimSwarm\0.ApiExps folder, these experiments cover foundational platform interface tutorials and general introductions to various tools.

Experiment 1: Simulink Acceleration Mode Simulation Experiment for Four UAVs

📝 Experiment Overview:
This experiment uses the Simulink interface to control four UAVs to perform a formation flying simulation drawing a circle. By leveraging Simulink’s acceleration mode, simulation efficiency is enhanced, addressing real-time performance and resource consumption issues associated with complex control algorithms, enabling rapid validation of multi-UAV control algorithms.

Experiment 2: Remote Pixhawk Flight Controller Hardware Reboot via UDP

📝 Experiment Overview:
This experiment demonstrates how to remotely reboot Pixhawk flight controller hardware via UDP broadcast, resolving issues such as failure to take off or abnormal flight behavior caused by disordered flight controller parameters in Hardware-in-the-Loop (HITL) simulations, thereby enabling re-initialization of HITL simulations.

Experiment 3: Multi-UAV Terrain Altitude Acquisition Interface

📝 Experiment Overview:
Using the terrain altitude acquisition interface of the RflySim platform, this experiment loads map data via LoadPngData, retrieves terrain altitude at specified coordinates via getTerrainAltData, and automatically configures initial positions for multiple aircraft. Taking 12 UAVs as an example, it demonstrates how to automatically compute terrain altitude information for initial UAV placement based on the current terrain.

Experiment 4: MATLAB-Based ROS2 Control Experiment

📝 Experiment Overview:
Through this experiment, students will master the integration of ROS2 with MATLAB, understand data transmission principles of the MAVLink protocol within the PX4-ROS2-MATLAB control chain, and learn how to build UAV ROS2 control nodes and flight control logic using Simulink, enabling hardware-in-the-loop simulation validation.

Experiment 5: Batch Launch and Co-Simulation of Multiple Vehicle Types

📝 Experiment Overview:
This experiment uses batch scripts to launch and co-simulate multiple vehicle types—including multirotors, ground vehicles, and fixed-wing aircraft—in parallel—validating the stability of multi-vehicle concurrent operation in the RflySim environment and enabling rapid setup of multi-agent scenarios.

Experiment 6: ROS 2 Multi-UAV Swarm Control

📝 Experiment Overview:
This experiment teaches communication between ROS 2 and the PX4 flight control system, and demonstrates how to achieve cooperative multi-UAV control using Python scripts. Students will master Offboard-mode position control and swarm formation flight techniques.

Experiment 7: MAVLink_Full Swarm Control Experiment

📝 Experiment Overview:
Using the MAVLink communication interface of the RflySim platform, this experiment controls the position, velocity, and heading of four UAVs. It introduces the UDP mode of MAVLink_Full communication and demonstrates coordinated swarm control of UAVs.

Experiment 8: RflySim Swarm RflyUdpFast Interface Experiment

📝 Experiment Overview:
This experiment utilizes the RflySim swarm Simulink-RflyUdpFast interface to control multiple UAVs in Offboard mode. Students will learn the FullData and SimpleData modes of UDP communication and methods for multi-UAV cooperative control.

Experiment 9: Four-UAV Point-Mass Model Swarm Experiment

📝 Experiment Overview: Implements quadcopter swarm simulation on the RflySim platform using Python point-mass models. By commanding 4 drones to take off, hover for several seconds, and then descend, this experiment verifies the swarm simulation capability of the simplified flight control model.

Experiment 10: Swarm RflyUdpRaw Interface Experiment

📝 Experiment Overview:
Controls 3 drones in Offboard mode by invoking the RflySim swarm Simulink-RflyUdpRaw interface. This experiment teaches IP configuration, UDP port setup, and MAVLink encoding methods for multi-drone swarm control.

Experiment 11: Fixed-Wing Point-Mass Model Swarm Experiment

📝 Experiment Overview:
Constructs a fixed-wing point-mass model to achieve precise control over velocity, yaw angle, altitude, and position commands, verifying trajectory tracking performance.

Experiment 12: RflySerialRaw Interface for Single Drone Offboard Control

📝 Experiment Overview:
Controls a single drone in Offboard mode via the RflySim swarm Simulink-RflySerialRaw interface. This experiment covers SerialRaw serial communication configuration and methods for issuing Offboard commands to multiple drones.

Experiment 13: RflyUdpMavlink Interface for Multi-Drone Offboard Control

📝 Experiment Overview:
Controls multiple drones in Offboard mode by invoking the RflySim swarm Simulink-RflyUdpMavlink interface. This experiment teaches the use of three control modes: position, velocity, and acceleration.

Experiment 14: RflySim Motion Capture VRPN Data Reception Interface Experiment

📝 Experiment Overview:
By invoking the RflySim swarm Simulink-RflyVrpnRecv interface, this experiment achieves real-time acquisition of six-degree-of-freedom information (position, velocity, acceleration, etc.) for objects such as drones in a motion capture environment. It configures the motion capture IP, drone IP, and port, and sends motion capture data to the flight controller for real flight control.

Experiment 15: High-Maneuverability Acceleration Control Experiment

📝 Experiment Overview:
This experiment implements drone Offboard mode control and high-maneuverability acceleration control via a Simulink model. Students will master the use of the mode switching module (None/Offboard/Arm/Takeoff/Flying/Land/Disarm) and verify the acceleration control interface.

10.5.2 Basic Usage Experiments

Stored in the 10.RflySimSwarm\1.BasicExps folder, these experiments provide a comprehensive set of supplementary teaching materials for beginners.

Experiment 1: MATLAB Control of UAV Simulation Experiment

📝 Experiment Overview:
Learn the Simulink communication interface modules of the RflySim toolchain—including FullData, SimpleData, RflyUdpRaw, RflySerialRaw, and RflyUdpMavlink modes—and practice communication control and simulation verification for single- and four-UAV systems.

Experiment 2: Swarm Light Show Demonstration

📝 Experiment Overview: Import multi-UAV trajectory data into RflySim 3D via Python scripts for 3D visualization preview, demonstrating lighting transformation effects. This is used to evaluate UAV formation flight performance and optimize flight paths.

Experiment 3: Quadrotor Point-Mass Model Swarm Experiment

📝 Experiment Overview: Implement takeoff and circular formation flight of 8 quadrotors using point-mass models on the RflySim platform. This validates the effectiveness of the hardware-in-the-loop (HIL) simulation method combining high-precision 6DOF models with real PX4 flight controllers.

Experiment 4: RflySim 3D Collision Interface Experiment

📝 Experiment Overview: Demonstrate collision effects for UAVs in the 3D engine via RflySim platform APIs, including single/multi-UAV collision detection, Simulink physics engine response, and P-mode communication optimization. This experiment focuses on learning collision detection and collision avoidance strategy design.

Experiment 5: Swarm Formation Control Simulation with Automatic Collision Avoidance

📝 Experiment Overview: Based on MATLAB/Simulink and the RflyUdpFast transmission module, implement 8-quadrotor figure-8 formation flight control with automatic collision avoidance, supporting 1–10 UAVs in formation.

Experiment 6: RflySim 3D Collision Detection API

📝 Experiment Overview: Demonstrate the usage of RflySim platform collision APIs, implementing UAV collision effects in the 3D engine via raycasting. This validates collision detection and response mechanisms, and evaluates flight safety.

Experiment 7: 8-UAV Point-Mass Model Swarm Simulation

📝 Experiment Overview: Implement takeoff and circular flight missions for 8 quadrotors using point-mass models on the RflySim platform. This validates the effectiveness of the hardware-in-the-loop (HIL) simulation method integrating high-precision 6DOF models (CopterSim) with real flight control systems (PX4).

Experiment 8: Single-UAV Control in RflyUdpFast FullData Mode

📝 Experiment Overview: Use the FullData mode of the RflyUdpFast transmission module to receive UAV state information, and implement local position motion control (circular trajectory) for a single UAV via Simulink modeling. This enables software-in-the-loop (SIL) or hardware-in-the-loop (HIL) simulation experiments.

Experiment 9: 8-UAV Figure-8 Formation Flight Control

📝 Experiment Overview: Implement figure-8 formation flight control for 8 quadrotors using MATLAB/Simulink and the RflyUdpFast module. This validates the effectiveness and stability of multi-UAV formation control algorithms.

Experiment 10: Vision-Based UAV Swarm Following Experiment

📝 Experiment Overview: Identify red circular targets via HSV color space segmentation, and implement visual servoing control for two- or four-UAV formations to follow a moving target using a proportional controller. This experiment focuses on learning and practicing vision-based formation control techniques.

Experiment 11: RflyUdpMavlink Real-Time Simulation

📝 Experiment Overview:
Utilize the RflyUdpMavlink library to implement MAVLink message transmission, reception, and parsing, and control UAVs via Simulink S-Functions for Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) simulations.

Experiment 12: RflyUdpMavlink Real UAV Control

📝 Experiment Overview:
Connect Simulink to real UAVs via the RflyUdpMavlink communication interface to implement safety self-checks and status monitoring. Supports both SIL and HIL modes for algorithm validation and flight controller performance testing.

Experiment 13: Dual-UAV Collision MAVLink API Experiment

📝 Experiment Overview:
Use the Python API to control two aircraft in RflySim 3D and simulate a collision process, evaluating collision detection accuracy, response mechanisms, and system performance.

Experiment 14: Four-UAV Simulation in RflyUdpFast FullData Mode

📝 Experiment Overview:
Use the FullData mode of the RflyUdpFast transmission module from the RflySim toolchain to build a Simulink model for centralized position control of quadrotor UAVs, validating control algorithm effectiveness and analyzing swarm coordination performance.

Experiment 15: RflySim UDP Mode Dual-UAV Collision Experiment

📝 Experiment Overview:
Simulate the process of two aircraft taking off and colliding via the Python API, evaluating the detection capability, response mechanism, and system performance of the RflySim 3D collision engine. Learn multi-UAV collision simulation methods under P0-P3 communication modes.

Experiment 16: Four-UAV Global Coordinate Control Experiment in RflyUdpFast FullData Mode

📝 Experiment Overview:
Learn to use the RflyUdpFast transmission module (FullData mode) from the RflySim toolchain Simulink library to receive state information from four UAVs, build a local position motion control model, and implement centralized coordinated control and trajectory tracking for a quadrotor UAV swarm.

Experiment 17: RflySim UDP Mode Dual-UAV Collision Simulink Experiment

📝 Experiment Overview:
Use MATLAB/Simulink to control two aircraft via UDP mode for collision simulation experiments, evaluating the collision detection, response mechanism, and system performance of RflySim 3D in collision engine mode.

Experiment 18: Single-UAV Circle Drawing Experiment in RflyUdpFast SimpleData Mode

📝 Experiment Overview:
Learn to use the SimpleData transmission module of the RflySim toolchain to receive UAV state information, implement single-UAV circular trajectory control via Simulink modeling, and master the parameter configuration of the RflyUdpFast module and software-in-the-loop/hardware-in-the-loop experimental methods.

Experiment 19: Four-UAV Simulation in RflyUdpFast SimpleData Mode

📝 Experiment Overview:
Learn to use the RflyUdpFast transmission module (SimpleData mode) of the RflySim toolchain to receive state information from quadrotor UAVs, implement single-UAV local position motion control via Simulink modeling, master the method of converting control commands into offboard signals for transmission to the flight controller, and conduct software/hardware-in-the-loop simulation experiments.

Experiment 20: Four-UAV Global Coordinate Control in SimpleData Mode

📝 Experiment Overview:
Implement centralized global coordinate control for a quadrotor UAV swarm using the SimpleData mode of the RflyUdpFast transmission module. The experiment includes building a Simulink control model, subscribing to localPos position data, generating velocity control commands by differencing with a circular desired trajectory, converting them into offboard signals via the SimpleCtrl4D module for transmission to the flight controller, and conducting software-in-the-loop or hardware-in-the-loop simulation experiments.

Experiment 21: UAV Control Experiment in FullDataModel Mode

📝 Experiment Overview:
Use the RflyUdpFast transmission module for software-in-the-loop/hardware-in-the-loop experiments. Subscribe to localPos position data output from the FullData bus, generate velocity control commands with a circular desired trajectory, and convert them into offboard mode signals via the vel_ned_full module to control UAV flight.

Experiment 22: Single-UAV Control in RflySerialRaw FullData Mode

📝 Experiment Overview:
Receive UAV state via the RflyUdpFast transmission module, and use Simulink to perform local position circular motion control simulation for a single UAV.

Experiment 23: Single-UAV Communication Experiment in RflyUdpMavlink FullData Mode

📝 Experiment Overview:
Use the Mavlink_simple mode of the RflyUdpMavlink library to receive and send MAVLink messages, achieving the same functionality as the UDP_simple mode of the RflyUdpFast module. This is used for single-UAV control and can run software-in-the-loop or hardware-in-the-loop simulations.

10.5.3 Advanced Development Experiments

Stored in the 10.RflySimSwarm\2.AdvExps folder, these experiments further familiarize users with certain low-level firmware ecosystem configurations.

Experiment 1: MATLAB Centralized Control of Eight-UAV Simulation

📝 Experiment Overview:
Control eight UAVs to perform circular formation flight using the SimpleData mode of the Simulink communication interface and the RflyUdpFast transmission module, mastering MATLAB/Simulink-based centralized swarm control methods.

Experiment 2: Distributed Local Area Network Communication for 8-UAV Simulation

📝 Experiment Overview:
Learn RflySim platform’s distributed LAN multi-UAV cooperative simulation techniques, master configuration methods for broadcast and point-to-point communication, and achieve circular formation flight for 8 UAVs.

Experiment 3: PX4 and Point-Mass Model Hybrid Swarm Control

📝 Experiment Overview:
This experiment implements heterogeneous swarm formation flight on the RflySim platform using PX4 SITL and point-mass models. It covers MAVLink Offboard control procedures, formation following methods, and NED coordinate system applications, with 1 Leader drone guiding 4 Followers to form a diamond-shaped formation.

Experiment 4: Heterogeneous Swarm Cooperative Control (3 Cars + 3 UAVs)

📝 Experiment Overview:
Through coordinated motion between quadrotor UAVs and ground vehicles, this experiment helps understand control logic and communication mechanisms in multi-agent systems.

Experiment 5: BroadNetSwarm_Mat Distributed Local Area Network Broadcast Communication (8-UAV Simulation)

📝 Experiment Overview:
This experiment uses two computers connected via a local area network for co-simulation, enabling 8 UAVs to fly in a circular formation. It introduces RflySim swarm simulation capabilities and MATLAB control program usage.

Experiment 6: 3v3 UAV Regional Defense Combat System

📝 Experiment Overview:
Through a 3v3 UAV regional defense combat scenario, this experiment teaches rule-based behavior decision-making, swarm control principles, and RflySim simulation operations.

Experiment 7: Centralized Control Simulink Simulation for 8-UAV Swarm

📝 Experiment Overview:
Using the RflyUdpFast transmission module to receive UAV states, this experiment performs centralized position motion control of an 8-UAV swarm via Simulink, verifying control algorithm effectiveness and data transmission performance, achieving circular flight for 8 UAVs.

Experiment 8: Collision-Avoidance Speed Control for UAV Swarm

📝 Experiment Overview:
This experiment implements automatic collision-avoidance speed control for UAV swarm formations using MATLAB/Simulink. Based on the artificial potential field algorithm, it enables various formation transitions and obstacle avoidance, supporting both Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) simulation modes.

Experiment 9: UDP-Based 8-UAV Concentric Circle Trajectory Flight

📝 Experiment Overview:
This experiment achieves automatic takeoff and concentric circle trajectory flight for 8 UAVs via UDP communication, validating RflySim’s swarm control and MAVLink UDP communication capabilities.

Experiment 10: Vehicle-UAV Cooperative Tracking Experiment

📝 Experiment Overview:
Through coordinated motion between quadrotor UAVs and ground vehicles, this experiment deepens understanding of multi-agent system control logic and communication mechanisms, while mastering UDP communication, Offboard mode control, and conditional-triggered state machine techniques.

Experiment 11: UDP-Based Multi-UAV Point-Position Formation Control

📝 Experiment Overview:
This experiment implements multi-UAV point-position formation control using MATLAB/Simulink and UDP communication. It covers hierarchical control architecture, PID position control algorithms, coordinate system transformations, and the design and deployment of formation algorithms.

Experiment 12: Swarm Formation Collision-Avoidance Control Experiment

📝 Experiment Overview:
This experiment implements UAV swarm formation control using MATLAB/Simulink, employing automatic collision-avoidance velocity control (in NED coordinate frame). It demonstrates three formations (triangle, horizontal line, vertical line), formation transitions, and obstacle avoidance capabilities in both software-in-the-loop (SIL) and hardware-in-the-loop (HIL) simulations.

Experiment 13: Distributed Local Area Network 8-UAV Cooperative Simulation

📝 Experiment Overview:
Utilize the RflySim platform to achieve joint simulation of 8 UAVs in circular formation flight across two computers on a local area network, performing distributed swarm control via MATLAB/Simulink to overcome the performance limitations of a single computer.

Experiment 14: Vehicle-UAV Cooperative Yaw Control Experiment

📝 Experiment Overview:
Master the cooperative control method of UAVs and ground vehicles in multi-agent systems, and understand the PID regulation mechanism of yaw control and its application in target tracking.

Experiment 15: MATLAB Multi-UAV Circular Trajectory Formation Control

📝 Experiment Overview:
Implement circular trajectory formation flight control for 6 UAVs using MATLAB/Simulink. Learn PID control, UDP communication, and circular trajectory parameterization design, covering core processes such as initialization configuration, trajectory generation, and mode switching.

Experiment 16: UAV Swarm Collision-Avoidance Formation Control Experiment

📝 Experiment Overview:
This experiment implements UAV swarm formation control using MATLAB/Simulink, demonstrating transitions between three formations (triangle, horizontal line, vertical line). It employs the artificial potential field algorithm to achieve automatic obstacle avoidance and formation transformation, including both software-in-the-loop and hardware-in-the-loop simulations.

Experiment 17: Multi-UAV Figure-8 Trajectory Tracking Control

📝 Experiment Overview:
Implement single/multi-UAV figure-8 trajectory tracking control using MATLAB/Simulink. Learn Lissajous curve trajectory generation algorithms and multi-UAV formation coordination mechanisms.

Experiment 18: Heterogeneous UAV and USV Cooperative Tracking

📝 Experiment Overview:
Master the cooperative control and communication mechanisms of heterogeneous multi-agent systems. Learn UDP-based multi-threaded communication and simulation synchronization techniques, and verify the real-time tracking capability of UAVs for USVs.

Experiment 19: Multi-UAV Leader-Follower Formation Control

📝 Experiment Overview:
Implement multi-UAV leader-follower formation tracking control using MATLAB/Simulink. Learn UDP communication mechanisms, leader-follower formation control architecture, and modular design methods, including both software-in-the-loop and hardware-in-the-loop simulations.

Experiment 20: USV_UUV Cooperative Towing Control

📝 Experiment Overview:
Learn the cooperative control principle of surface USV and underwater UUV based on cable towing. Master cable dynamics calculations and the application of PX4 Offboard mode in heterogeneous unmanned systems. Verify the towing control effect through RflySim 3D simulation.

Experiment 21: Multi-UAV Multi-Trajectory Formation Control

📝 Experiment Overview:
Implement multi-UAV formation control and multi-trajectory switching using MATLAB/Simulink. Supports formations such as line, triangle, and square, as well as trajectory modes like figure-8, square, and spiral. Includes state management and smooth trajectory transition algorithms.

Experiment 22: RflySim UDP Virtual Structure Formation Control

📝 Experiment Overview:
Implement multi-UAV formation control based on the virtual structure method using MATLAB/Simulink. Learn UDP communication mechanisms, virtual leader formation algorithms, and PID position control principles. Master the hierarchical control architecture and trajectory planning techniques for multi-rotor UAVs.

Experiment 23: Vehicle-UAV Cooperative Tracking Experiment

📝 Experiment Overview:
Through coordinated motion between quadrotor UAVs and ground vehicles, understand the heterogeneous communication mechanisms and control logic of multi-agent systems. Two unmanned vehicles cruise along a rounded rectangular path, while two UAVs track and follow their corresponding vehicles in real-time, intelligently aligning with the vehicle's heading direction.

10.5.4 Advanced Custom Development Experiments

Stored in the 10.RflySimSwarm\3.CustExps folder, these experiments are designed for advanced users engaged in custom development.

Experiment 1: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
A quadrotor UAV formation trajectory tracking experiment based on the leader-follower method: UAV #1 (leader) vertically takes off to 10 m, followed by UAVs #2, #3, and #4 (followers), which take off sequentially with 3-second intervals and maintain formation while tracking the trajectory.

Experiment 2: Distributed Local Area Network 16-UAV Simulation Experiment

📝 Experiment Overview:
Utilize the cluster simulation capability of the RflySim platform to coordinate 16 UAVs across two PCs connected via a local area network to perform circular flight. This experiment covers multi-UAV cooperative simulation configuration and UDP communication techniques.

Experiment 3: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
A quadrotor UAV formation trajectory tracking experiment based on the leader-follower method, where four UAVs take off sequentially and track a circular trajectory via Python interface control. The algorithm is migrated to WSL to resolve platform compatibility issues.

Experiment 4: Multi-UAV Point-Mass Model Swarm Experiment

📝 Experiment Overview:
Implement takeoff and circular flight for multiple point-mass-model quadrotor UAVs on the RflySim platform, supporting swarm scales of 30, 100, or 200 UAVs, to validate the feasibility of swarm algorithms in software-in-the-loop (SITL) simulation.

Experiment 5: Swarm Intelligence Tutorial

📝 Experiment Overview:
Includes three multi-UAV swarm intelligent control experiments: Ant Algorithm-based Path Planning, Olfati-Saber Swarm Obstacle Avoidance Algorithm, and Deep Reinforcement Learning-based UAV Area Defense, covering core algorithms such as multi-UAV cooperative path planning, obstacle-avoidance aggregation, and intelligent defense.

Experiment 6: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
A quadrotor formation trajectory tracking experiment based on the leader-follower method, where multiple UAVs are sequentially launched and maintain formation flight via the Python interface. Focuses on learning distributed formation control algorithms and network communication mechanisms.

Experiment 7: 50-UAV Formation Control Hardware-in-the-Loop Simulation

📝 Experiment Overview:
Hardware-in-the-loop simulation of 50 UAVs divided into three groups for formation control, conducted across multiple computers on a local network. Commands are transmitted between master and slave machines via UDP, validating swarm simulation and formation control algorithms.

Experiment 8: 100-UAV Formation Control Hardware-in-the-Loop Simulation

📝 Experiment Overview:
Hardware-in-the-loop simulation of 100 UAVs divided into three groups for formation control, conducted across 10 computers on a local network. Commands are transmitted between master and slave machines via UDP, enabling learning of distributed swarm simulation and formation control methods.

Experiment 9: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
A quadrotor formation control experiment based on the leader-follower method, where distributed multi-UAV trajectory tracking is implemented via the Python interface. UAV #1 (leader) and UAV #3 (follower) take off sequentially with a 3-second interval and maintain formation flight.

Experiment 10: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
A quadrotor formation trajectory tracking experiment based on the leader-follower method, where UAV #1 (leader) guides UAVs #2, #3, and #4 (followers) to take off sequentially and perform route-planned flight via the Python interface.

Experiment 11: PX4 + MPSwarm25 Hybrid Swarm Control

📝 Experiment Overview:
Master flight control methods for hybrid swarm systems combining PX4 SITL and point-mass models. Achieves synchronized flight of five independent formations: 5 PX4 SITL UAVs as Leaders and 20 point-mass model UAVs as Followers.

Experiment 12: Ant Algorithm-Based Multi-UAV Path Planning

📝 Experiment Overview:
By applying and optimizing the ant algorithm, plan feasible and optimal paths for multiple UAVs that meet obstacle avoidance and collision avoidance requirements, learning the application of swarm intelligence algorithms in trajectory planning.

Experiment 13: Distributed 8-UAV Broadcast Communication Simulation

📝 Experiment Overview:
Demonstrates a distributed simulation system based on RflySim, achieving cooperative flight control of 8 UAVs via LAN UDP broadcast, mastering distributed architecture and Git deployment methods.

Experiment 14: Distributed LAN Broadcast Communication 16-UAV Simulation Experiment

📝 Experiment Overview:
This experiment learns to use two computers on a LAN for co-simulation on the RflySim platform, achieving circular flight of 16 UAVs, mastering distributed swarm simulation configuration and operation.

Experiment 15: Swarm Control Program EXE File Generation

📝 Experiment Overview:
This experiment demonstrates converting a Simulink simulation program for 4 UAVs into an executable .exe file, learning MATLAB/Simulink code generation and VS compiler configuration, verifying that the exe correctly executes UAV simulation tasks and evaluating its performance.

Experiment 16: DistSim Distributed Cluster Communication Test

📝 Experiment Overview:
Perform distributed communication tests between two computers using DistSim software, learning LAN configuration, node ID setting, and mastering methods to send test commands to all computers or specific computers.

Experiment 17: 30-UAV Point-Mass Model Swarm Experiment

📝 Experiment Overview:
Implement takeoff, hover, and synchronized circular flight tasks for 30 point-mass-model quadrotor UAVs on the RflySim platform, demonstrating single-computer hundred-level UAV swarm simulation capability.

Experiment 18: Multi-UAV Formation Control Hardware-in-the-Loop Simulation

📝 Experiment Overview:
Use the RflySim cluster simulation function to co-simulate across multiple computers on a LAN, controlling 50-100 aircraft divided into three groups for formation control, learning hardware-in-the-loop simulation technology.

Experiment 19: Distributed Simulation 8-UAV Experiment

📝 Experiment Overview:
Based on the RflySim platform, demonstrate the distributed simulation system architecture, learn to collaboratively run an 8-UAV swarm simulation on multiple computers, mastering Git code synchronization, JSON configuration-driven, and UDP network communication configuration methods.

Experiment 20: Quadrotor Distributed Trajectory Tracking Experiment

📝 Experiment Overview:
Implement distributed control and cooperative trajectory tracking of quadrotor UAV swarms through the RflySim simulation platform, mastering multi-UAV coordinated control methods, understanding leader-follower formation control strategies and UDP network communication mechanisms.

Experiment 21: 100-UAV Point-Mass Model Swarm Experiment

📝 Experiment Overview:
Implement a 100-UAV swarm simulation using Python point-mass models on the RflySim platform, completing takeoff, hover, and synchronized circular tasks, evaluating flight performance and control accuracy of the combination of high-precision models and real flight control systems.

Experiment 22: Olfati-Saber UAV Swarm Obstacle Avoidance Experiment

📝 Experiment Overview:
Use the Olfati-Saber algorithm to achieve obstacle avoidance, collision avoidance, and target aggregation for 6 UAVs, traversing obstacle areas to complete swarm flight tasks.

Experiment 23: DistSim Distributed 16-UAV Cooperative Flight

📝 Experiment Overview:
Demonstrate establishing a distributed communication system between two computers to control 16 UAVs for cooperative circular flight, learning distributed system architecture, UDP communication, multi-UAV cooperative control, and automated deployment.

Experiment 24: Embodied Intelligence Reinforcement Learning Quadrotor Hover Control

📝 Experiment Overview:
Using the Genesis simulation environment and RflySim software-in-the-loop, implement acceleration-level hover control for a quadrotor UAV through the PPO reinforcement learning algorithm, mastering sample equivalence and parameter tuning methods from GPU high-parallel training to CPU low-parallel training, including domain randomization and noise injection to enhance policy generalization.

Experiment 25: Multi-UAV Area Defense Reinforcement Learning

📝 Experiment Overview:
Use the MADDPG deep reinforcement learning algorithm to train a UAV defense model, achieving multi-agent cooperative control where defensive UAVs automatically pursue and shoot down attacking UAVs.

Experiment 26: Point-Mass Model 200-UAV Dual-Computer Swarm Simulation

📝 Experiment Overview:
Based on the RflySim platform, implement takeoff and circular flight tasks for 200 point-mass-model UAVs on two computers, demonstrating the swarm simulation effect of combining high-precision models with real flight control systems.

Experiment 27: 10-UAV Circular Flight

📝 Experiment Overview:
Implement circular trajectory flight control for 10 UAVs in Offboard mode through the RflySim cluster Simulink-RflyUDPFast interface, learning multi-UAV formation and UDP communication.

Experiment 28: Swarm Distributed Simulation Experiment

📝 Experiment Overview:
Demonstrate the working principle of the distributed simulation system based on the RflySim platform, set up Windows and Linux (NX) node environments, and implement Git automatic code deployment and multi-node collaborative simulation control.

Experiment 29: Multi-UAV Area Defense Confrontation System

📝 Experiment Overview:
Through a 5v5 UAV area defense confrontation system, learn rule-based behavior decision-making mechanisms, master RflySim simulation platform operations, understand UAV swarm confrontation system architecture, and achieve multi-UAV coordinated control and state management.