AR HUD Control for Multi-Robot Tactical Situational Awareness
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Solution Overview
Problem
Existing combat and rescue technologies lack a holistic system with enhanced features and processes to provide high-levels of situation awareness and intuitive cognition, particularly in navigating complex environments and making efficient mission decisions.
Innovation Solution
The Advanced Robotic Engagement System (ARES) integrates a heads-up display (HUD) with a hand-operable controller, an unmanned ground vehicle (UGV), and an associated unmanned aerial vehicle (UAV) or unmanned underwater vehicle (UUV), utilizing AI rules-based algorithms and dead-reckoning navigation to enhance situational awareness and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple unmanned vehicles (UGV, UAV, UUV) and control devices are integrated into a holistic system, then situation awareness and decision-making capability are enhanced, but device complexity increases
Solution Approach 1:
The system is divided into modular components: handheld controller with communication module, unmanned vehicles (UGV, UAV, UUV) with sensors, and a processing unit. Each component operates independently but communicates through standardized protocols, allowing the complex system to be managed through modular segments that can be developed, tested, and maintained separately while achieving holistic situation awareness when integrated.
2Productivity
If AI algorithms and multiple sensors are deployed to provide strategic recommendations, then mission decision-making effectiveness is improved, but use of energy and computational resources increases
Solution Approach 1:
The AI algorithm processes sensor data selectively based on mission criticality and current situation requirements. Rather than continuously analyzing all sensor inputs at maximum computational power, the system applies partial processing to non-critical data and full processing only to mission-critical information, reducing overall energy consumption while maintaining effective decision-making support.
3Reliability
If the system provides comprehensive mission routes with minimized risks through AI algorithms, then mission success rate is improved, but loss of time in processing and analyzing data increases
Solution Approach 1:
The system pre-processes sensor data and pre-calculates multiple potential mission routes with associated risk assessments before actual mission execution. By performing preliminary analysis of the environment and pre-computing navigation options, the AI algorithm reduces real-time processing requirements, enabling fast decision-making during critical mission phases while maintaining high success rates through pre-evaluated safe routes.
Data Source
AI summary
This invention describes a tactical advanced robotic engagement system (ARES) (100) for combat or rescue mission by employing advanced electronics, AI and AR capabilities. In ARES, a user carries a weapon or tool (102) equipped with a hand-operable controller (150) for controlling an associated UGV (170), UAV (180) or UUV. The UGV (170) provides a ground/home station for the UAV (180). The UGV, UAV is equipped with a camera (290) to obtain real-time photographs or videos and to relay them to a heads-up display (HUD) (110) mounted on the user's helmet (104). The HUD (110) system provides intuitive UIs (132) for communication and navigation of the UGV, UAV; AR information reduces visual cognitive and mental loads on the user, thereby enhancing situation awareness and allowing the user to maintain heads-up, eyes-out and hands-on trigger readiness. The HUD (110) also provides intuitive UIs to connect up with peers and/or a Command Centre (190).