Drone Swarm Mission Planning With Self-Learning and VR Support
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Solution Overview
Problem
Existing drone swarm systems for crisis situation surveillance lack adaptability and reusability, as they employ a fixed strategy and do not utilize data from previous missions for optimizing future operations or training emergency responders effectively.
Innovation Solution
An autonomous system with self-learning algorithms and AI, integrated into a Central Command System, generates adaptive mission plans and creates a 3D virtual reality environment for human operators to intervene and train, optimizing drone swarm configurations and allowing for continuous learning and mission refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed strategy is used for drone swarm control, then the system is simple to operate, but the system lacks adaptability to different missions and environments
Solution Approach 1:
The patent implements dynamic adaptability by enabling the drone swarm control system to automatically adjust its configuration and mission plan based on real-time environmental conditions, mission requirements, and drone availability. The system transitions from static fixed strategies to dynamic adaptive control through continuous learning algorithms that optimize swarm behavior for different scenarios.
Solution Approach 2:
The system employs self-learning algorithms that enable the drone swarm controller to autonomously improve its performance over time by learning from previous missions and environmental data. This self-service capability allows the system to automatically adapt without requiring constant human intervention or reconfiguration, resolving the contradiction between adaptability and complexity.
2Productivity
If data from previous missions is not utilized, then the system operates with consistent procedures, but the system cannot optimize future missions or train emergency responders effectively
Solution Approach 1:
The patent implements a feedback mechanism where data from previous missions is systematically collected, analyzed, and used to optimize future mission plans. The self-learning algorithms process historical mission data to identify patterns and improve swarm control strategies, ensuring that valuable information is retained and applied to enhance future operational efficiency.
Solution Approach 2:
The system performs preliminary analysis of historical mission data before executing new missions, using learned insights to pre-optimize mission plans and swarm configurations. This preliminary action based on past experience enables the system to start each mission with improved strategies already in place, enhancing productivity while effectively utilizing accumulated information.
3Adaptability or versatility
If a central command system with fixed strategies is used, then the system is easy to operate, but the system lacks continuous optimization capability
Solution Approach 1:
The patent implements self-service through autonomous self-learning algorithms that continuously optimize mission plans without requiring manual reconfiguration by operators. The system automatically learns from experience and adapts to new scenarios, maintaining operational simplicity while achieving continuous optimization through automated intelligence rather than human intervention.
Solution Approach 2:
The system dynamically changes operational parameters and mission plan configurations based on learned insights from previous missions and real-time conditions. This automatic parameter optimization enables continuous improvement of system performance while maintaining ease of operation, as the changes are made autonomously by the control algorithm rather than requiring manual adjustment by operators.
4Adaptability or versatility
If no virtual reality environment is provided, then the system has fewer components, but the system cannot provide effective training for emergency responders
Solution Approach 1:
The patent creates a virtual reality copy of the physical environment and drone swarm operations, allowing emergency responders to train in a simulated three-dimensional space. This virtual copy reproduces critical mission scenarios without requiring actual drone deployment, providing effective training capability while keeping the physical system structure relatively simple by using software-based simulation rather than additional physical hardware.
Data Source
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AI summary
Autonomous system and method for recognition and support, according to the invention, involves a Central Command System capable of generating a mission plan for a swarm of drones, designed on the basis of real situations obtained following previous missions or following the generation of real situations through a self-learning mechanism, central system that can structure a three-dimensional environment for virtual reality glasses, based on information obtained from one or more Drone swarm control devices, device that processes and coordinates information from a simulator or from a Drone Swarm, composed of any number of drone-type devices.