Robotic Target Route Definition Using 3D Virtual Environments
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Solution Overview
Problem
Current firearms training systems using mobile targets are cumbersome, particularly when defining routes on a computer screen, as they require operators to imagine three-dimensional scenarios from a two-dimensional perspective, and often necessitate additional steps like generating maps, which complicates the creation and execution of training scenarios with elevation changes and multiple moving targets.
Innovation Solution
A method and system for arranging firearms training scenarios using robotic mobile targets that record and replay operations data, allowing targets to base their actions on previously recorded commands, actions, and outcomes, enabling flexible route definition and execution, including deviation from pre-programmed paths to avoid obstacles or change parameters randomly or repeatably for increased challenge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If routes are defined on a computer screen using traditional methods, then the training scenario can be set up, but the operator has to imagine three-dimensional scenarios from a two-dimensional perspective which is difficult and time-consuming
Solution Approach 1:
The system captures the actual visual scene from the trainee's perspective using a camera or sensor, and creates a digital copy of this three-dimensional environment. This digital copy is then used for route definition, allowing operators to work directly with the actual spatial layout rather than imagining it from a 2D screen, thereby significantly reducing the time and cognitive load required for scenario creation.
Solution Approach 2:
The invention transitions from traditional two-dimensional screen-based route definition to a three-dimensional virtual representation that mirrors the actual training environment. By displaying routes and targets in a 3D virtual space that matches the physical layout, operators can define scenarios more intuitively and efficiently, eliminating the mental transformation required between 2D screens and 3D reality.
2Adaptability or versatility
If multiple mobile targets are used in training exercises, then training realism is improved, but operator workload increases significantly when directly radio-controlled
Solution Approach 1:
The system enables mobile targets to operate autonomously by equipping them with onboard computers that generate their own routes based on pre-defined constraints and the virtual environment. This self-service capability allows multiple targets to move independently without requiring continuous operator control, dramatically reducing operator workload while maintaining training realism and flexibility.
Solution Approach 2:
The system dynamically generates routes for mobile targets based on real-time conditions, virtual environment data, and pre-defined constraints. Rather than using fixed pre-programmed routes, the targets can adapt their movement patterns dynamically, allowing operators to set up versatile training scenarios without manually controlling each target's every move.
3Adaptability or versatility
If trackless mobile targets are used to move along any route, then training flexibility is improved, but novel methods of defining routes are required which complicates the process
Solution Approach 1:
The system creates a digital copy of the physical training environment in three dimensions, including all obstacles, cover positions, and spatial relationships. This virtual copy serves as the foundation for route definition, allowing operators to define routes by referencing actual physical features rather than abstract coordinates, thereby simplifying the interface while maintaining full route flexibility.
Solution Approach 2:
The virtual environment system serves multiple functions: it acts as a mapping tool, a route planning interface, a training scenario editor, and a real-time display for operators. By consolidating these functions into a single multi-functional platform, the system achieves high route flexibility without proportionally increasing complexity, as the same virtual model supports all route definition and execution tasks.
4Ease of manufacture
If maps are generated before using mobile trackless targets in a new training range, then route definition can proceed, but an extra step is required that may need additional resources such as internet connection
Solution Approach 1:
The system includes onboard sensors and cameras in the mobile targets that automatically capture and process spatial data of the training environment during their operation. This self-service mapping capability eliminates the need for separate pre-mapping steps, as the targets contribute to creating the virtual environment data while performing their training function, thereby reducing setup complexity and resource requirements.
Data Source
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AI summary
Systems and methods of arranging a firearms training scenario utilising at least one robotic mobile target in a training area are disclosed, the method including the steps of: sending commands to at least one robotic target in a training area to cause the target to operate in the training area; recording operations data representative of the operations carried out by the at least one robotic target; and subsequently conducting a training scenario in the training area wherein the at least one robotic target bases its actions at least partially on the previously recorded operations data.