KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking
Closing the Sensemaking Gap in Urban Search & Rescue
In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the "sensemaking gap." In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with Al-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving sur-vivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations.
Quantifiable Impact on Search & Rescue Operations
KHAIT demonstrates a significant enhancement in USAR operations, improving both efficiency and effectiveness.
22%
Average Time Reduction in Survivor Localization
76.5
Average System Usability Score (SUS)
57.35%
Max Individual Time Reduction Observed
Deep Analysis & Enterprise Applications
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KHAIT Overview
Operational Flow
Technology Deep Dive
User & Animal Experience
Challenges & Future
22%
Average Time Reduction in Survivor Localization
Revolutionizing USAR with AI & AR
The KHAIT system addresses the critical 'sensemaking gap' in Urban Search and Rescue (USAR) by integrating AI-powered cameras and Augmented Reality (AR) headsets. This innovative approach allows handlers to gain real-time visual insights from a canine's perspective, improving survivor localization precision and speed. In real-world USAR simulations, KHAIT demonstrated an average 22% decrease in survival allocation time, significantly enhancing operational efficiency.
Enterprise Process Flow
Canine detects survivor, emits Trained Final Response (TFR)
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KHAIT transmits canine location and video feed to handler's AR device
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Handler navigates to canine/survivor location using AR guidance
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Handler projects AR marker for precise survivor location
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Rescue team receives marker, triages survivor, provides support
| Feature | KHAIT System | Traditional Methods |
|---|---|---|
| Canine Perspective | Real-time AI-enhanced video feed via AR headset | Limited handler visibility, reliance on auditory cues |
| Object Detection | AI-powered YOLOv8l for precise object identification | Manual visual search by handler |
| Localization | SLAM-based spatial anchoring, AR markers for precise location sharing | Handler navigates manually, GPS often ineffective indoors |
| Communication | Local mesh network for real-time data flow to multiple rescuers | Verbal communication, line-of-sight dependent |
| Situational Awareness | Enhanced by AR overlays and canine's view, foresees obstacles | Limited by handler's vantage point and obstructions |
| Efficiency | 22% average time reduction in survivor localization | Delays due to navigation challenges, access routes |
Hardware & Software Architecture
KHAIT integrates a smart AI-enabled canine harness with a multi-user AR application. Key hardware includes a lightweight harness (2 kg total weight) with an NVIDIA Jetson Orin Nano (8GB) for edge AI processing, a CMOS 4K Autofocus camera for high-resolution video, and a portable 27000mAh power bank. Handlers wear a HoloLens-2 AR headset. The software architecture utilizes Unity and Mixed Reality Toolkit 3 (MRTK) for the AR interface, with a Flask Python backend and Redis for data handling. A secure local mesh network ensures reliable communication between all components, supporting real-time video feeds with YOLOv8l-generated object detection bounding boxes, and advanced localization via the Ajna module.
Rescuer Feedback: Enhanced Situational Awareness
All participants expressed positive sentiments toward using KHAIT, noting significant improvements in situational awareness. Rescuers highlighted the ability to 'observe AI detections from a search' and 'foresee obstacles through the AR feed,' which streamlined navigation and decision-making. The average System Usability Scale (SUS) score was 76.5, indicating good usability, with one rescuer giving a perfect 100, stating the system showed 'high usability and integration, and showed no limiting barriers to effective system use.' These insights confirm KHAIT's potential to empower human rescuers by providing real-time, contextual information.
Canine Welfare & Interaction
Ensuring canine welfare was paramount. Canines underwent habituation to the KHAIT harness (2 kg total weight) to ensure comfort and safety. Observations during trials indicated that while most canines adapted well, some, like Loki and Val, required more time due to harness fit or behavioral independence issues. KHAIT also revealed an unanticipated benefit: handlers could interpret canine confusion when struggling to pinpoint scents, allowing them to intervene or redirect, thus preventing false negatives. Future work will focus on harness redesign to optimize weight distribution and fit for diverse canine body shapes, ensuring long-term comfort and safety for these invaluable partners.
Limitations & Future Directions
Current limitations include the small sample size of participants and canines, and the inherent variability of real-world USAR environments, making precise statistical comparisons challenging. The complexity and weight of SLAM systems make them currently impractical for direct integration into canine harnesses. Future work involves conducting more rigorous IRB-approved and IACUC-approved studies with a larger pool of SAR professionals and canines. We plan to refine harness ergonomics for optimal weight distribution and fit, explore alternative lightweight localization solutions, and further develop KHAIT's collaborative sensemaking capabilities through consensus building across multiple rescuers.
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Your AI Implementation Roadmap
Our phased approach ensures a smooth, effective, and tailored integration of AI capabilities into your enterprise, leveraging insights from cutting-edge research.
Phase 1: Discovery & Strategy
Initial consultations to understand your specific operational challenges and objectives, drawing parallels to the sensemaking gaps identified in USAR. We will define key performance indicators and a tailored AI strategy.
Phase 2: Pilot Program & Customization
Deployment of a proof-of-concept, integrating components similar to KHAIT's real-time data visualization and object detection into your existing infrastructure. This phase focuses on customizing AI models and interfaces to your unique environment and user needs.
Phase 3: Full-Scale Integration & Training
Seamless integration of the customized AI system across your enterprise, supported by comprehensive training for your teams. We ensure that the technology enhances, rather than complicates, daily operations, much like KHAIT empowers SAR handlers.
Phase 4: Optimization & Continuous Improvement
Ongoing monitoring, performance tuning, and iterative refinement of the AI system based on real-world feedback and emerging needs. This phase ensures sustained efficiency gains and adaptability to future challenges.
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