Hardware-in-Loop Test-Bed for Pursuit-Evasion Game Simulation
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Solution Overview
Problem
Pursuit-evasion games are typically simulated numerically, lacking consideration for real-world physical constraints and time-delay feedback, which limits their effectiveness in demonstrating complex scenarios.
Innovation Solution
A hardware-in-loop test-bed is developed using robots, a drone, and a computer to demonstrate three-player pursuit-evasion games, incorporating delay compensation and tracking-by-detection processes to model real-world interactions and calculate optimal robot commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If numerical simulations are used to implement pursuit-evasion games, then computational feasibility is improved, but real-world physical constraints and time-delay feedback are not fully considered
Solution Approach 1:
The patent introduces a hardware-in-loop test-bed as an intermediary system between numerical simulations and real-world applications. This test-bed includes physical robots with sensors and actuators that operate in a controlled environment, bridging the gap between computational models and real-world physics. The test-bed incorporates delay compensation mechanisms and physical constraint models that are absent in pure simulations, while maintaining computational tractability through simplified physics models and controlled experimental conditions.
2Reliability
If a hardware-in-loop test-bed is implemented to demonstrate pursuit-evasion games with real-world limitations, then reliability and realism are improved, but device complexity increases
Solution Approach 1:
The hardware-in-loop test-bed is segmented into distinct modular components: robot platforms with standardized actuation systems, drone observers with computer vision modules, delay compensation units, and control software layers. Each component can be independently developed, tested, and replaced. The system uses standardized communication protocols and interfaces between modules, reducing integration complexity while maintaining realistic physical interactions.
Solution Approach 2:
The test-bed uses simplified copies and models of real-world systems: scaled-down robot versions with representative dynamics, simulated sensor models that replicate real sensor behavior, and virtual environments that mirror physical spaces. These copies capture essential physical constraints and time delays without requiring full-scale complex hardware, reducing overall system complexity while preserving realism.
3Measurement precision
If tracking-by-detection process with background modeling and multiple target associations is used, then measurement precision of robot state is improved, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary background modeling during idle periods or initialization phases, creating and storing background subtracted images before they are needed for tracking. Robot templates and detection parameters are pre-computed and cached. When real-time tracking is required, the system only needs to perform template matching against pre-processed backgrounds, significantly reducing processing time while maintaining high detection precision through the use of pre-analyzed spatial and temporal information.
Data Source
AI summary
Methods and devices for demonstrating three-player pursuit-evasion (PE) game are provided using a hardware-in-loop test-bed. Robots including pursuer robots and an evader robot are arranged on a solid surface. A drone is positioned flying above to oversee the robots to capture a video or an image sequence of the robots. A robot thread process and a drone thread process are implemented by a computer. In the robot thread process, a tracking-by-detection process is perform to provide a state of the robot including a location and a heading direction of the robot; a delay compensation is conducted; and a PE game is called to calculate a robot command. In the drone thread process, a drone control is calculated to make the drone follow an evader robot, the drone control is sent to the drone, and user commands are also checked.