Chapter 8: Multimodal Perception and Intelligent Decision-Making¶
Visual perception is the process by which machines acquire environmental information through sensors and analyze and interpret images using computer vision technology. It encompasses tasks such as object detection and recognition, providing the system with environmental awareness capabilities. Obstacle avoidance decision-making relies on environmental information obtained through visual perception, employing environment modeling, path planning, and intelligent decision-making algorithms to formulate behavior strategies that avoid obstacles, prevent collisions, and achieve predefined objectives. The two are interdependent: visual perception provides environmental data for obstacle avoidance decision-making, while the latter executes actions based on this data. They play critical roles in fields such as autonomous driving and robot navigation, promoting the intelligent application and development of unmanned systems in complex environments.
8.1 Background and Theory¶
Multimodal perception and intelligent decision-making technologies are the core pillars for intelligent unmanned systems to achieve efficient collaboration, autonomous operation, and safety assurance, forming a closed-loop mechanism of "perception–cognition–action."
8.1.1 Multisource Information Fusion and Robust Perception¶
Traditional positioning and control schemes for unmanned systems often rely on single-sensor inputs such as GNSS satellite positioning. In highly interfered environments—including forests, urban canyons, over sea surfaces, or indoors—integrating multimodal sensor data (e.g., vision, LiDAR, IMU) has become an inevitable choice. Through techniques such as SLAM algorithms, robust, continuous, and dynamically updatable environmental models can be constructed.
8.1.2 Obstacle-Avoidance Path Planning and Intelligent Decision-Making¶
Obstacle avoidance planning relies on fused perception data. Combined with advanced methods including deep learning, reinforcement learning, behavior trees, graph search, and optimization algorithms, the system can autonomously assess obstacle risks, dynamically adjust speed and heading, and generate safe and efficient paths in real time.
8.2 Framework and Interfaces¶
The RflySim toolchain, combined with typical development cases, provides a detailed introduction to the supporting capabilities for intelligent perception and decision-making tasks, including sensor interfaces, data acquisition and processing workflows, and typical task algorithm architectures.
8.2.1 Image Acquisition in Virtual Environments¶
RflySim offers a high-fidelity virtual sensor simulation environment, supporting the generation of multimodal sensor data—including RGB vision, depth images, LiDAR, and IMU—providing realistic test data sources for visual perception algorithms.
8.2.2 Object Detection and Tracking¶
The platform supports algorithm validation for typical vision tasks, including object detection and tracking, path planning, and obstacle avoidance strategies. It provides standardized interface frameworks to help developers efficiently achieve the transition from simulation validation to real-machine deployment.
8.3 Showcase of Outstanding Cases¶
Five-UAV Visual Shared SLAM Hardware-in-the-Loop Simulation:
Simulation Algorithm Development and Validation:
8.4 Course-Linked Video Lectures¶
Public Lecture Replay for This Chapter (Session 7: Multimodal Perception and Intelligent Decision-Making):
8.5 Chapter Experiment Cases¶
The related verification experiments and guided cases for this chapter are located in the [Installation Directory]\RflySimAPIs\8.RflySimVision folder.
8.5.1 Interface Learning Experiments¶
Stored in the 8.RflySimVision\0.ApiExps folder, these experiments cover foundational platform interface tutorials and general introductions to various tools.