SRS Specification v2.4NVIDIA AI & WebRTC PoweredDefense Ready

Final Year Project Defense Preparation Platform

Active Project: Autonomous Drone Swarm Pathfinding in GPS-Denied Environments using Reinforcement Learning. Practice multi-tier adversarial mock viva interviews, receive real-time project enhancements, and perform grounded vector Q&A via NVIDIA LLM.

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Autonomous Drone Swarm Pathfinding in GPS-Denied Environments using Reinforcement Learning

Candidate: Sarah Jenkins
Document Sections:5 Parsed

Interview Questions

36

Across 3 difficulty tiers

3 TiersLow, Mid, High

Defense Readiness

88%

Academic Jury Rubric

+12%vs last mock

RAG Sections

5

Grounded chunk index

100%Embedding coverage

AI Viva Engine

NVIDIA LLM

Real-time Evaluation

ActiveLow Latency

Real-Time Defense Analytics & Performance Charts

Interactive graphs, topic mastery distributions, progress gauges, and viva readiness indicators

Overall Metric

Defense Readiness

Jury rubric index

+14% vs Baseline
88%
Proctoring

Trust & Integrity

Anti-spoof optical score

Zero Anomalies
96%
RAG Studio

Vector Grounding

HNSW citation depth

100% Citation Match
100%
Progression

Tier Completion

Low, Mid, High tested

36/36 Answered
92%
Trajectory Curve

Viva Defense Performance & Latency Trajectory

0%25%50%75%100%S1S2S3S4S5S6
Real-Time NVIDIA LLM Evaluation StreamPeak Score: 94% (Latency Optimization)
Bar Analysis

Engineering Competency Mastery

vs Faculty Cutoff
System Architecture88%
Security & Auth94%
Data Pipeline & APIs82%
Latency & Throughput91%
Adversarial Defense78%
Oral Clarity & Rubric86%
Candidate Score Faculty Cutoff (75%)
Pie Distribution

Project Domain & Topic Weighting

100% Vector Indexed
4 CoreDomains
Architecture & Design
35%
Protocols & APIs
25%
Security & Failover
25%
Performance & Latency
15%
Tier Breakdown

Question Tier Progression & Velocity

36 Total Questions
Tier 1: Low (Core Scope & MCQs)12 Questions (100% Passed)
Tier 2: Middle (Architecture Trade-offs)12 Questions (88% Passed)
Tier 3: High (Adversarial Failure Modes)12 Question (78% Scored)
Avg Oral Defense: 48sPractice High Tier →

SRS Core Functional Modules

Section 4.1 – 4.3
36 Questions
Module 1 (SRS 4.1)

Mock Project Interview

Adaptive multi-tier viva grilling (Low, Middle, High). Evaluate your oral defense with real-time feedback, grading metrics, and model answers.

3 Categories
Module 2 (SRS 4.2)

Project Enhancement

Automatic architecture review and feature suggestions. Identifies areas for improvement, security gaps, and advanced feature additions.

Vector Grounded
Module 3 (SRS 4.3)

RAG Project Q&A

Semantic retrieval across document chunks. Ask any technical query about your document and get verified, citation-backed answers.

Parsed Project Document Sections

Content extracted from Drone_Swarm_RL_Navigation.pdf and indexed for Q&A and question generation

5 Sections Ready
1. Problem Statement & Scope#1

Search and rescue in collapsed mines requires autonomous aerial swarms operating without global satellite navigation or manual human piloting.

2. Multi-Agent Reinforcement Learning Architecture#2

Trained using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) with centralized training and decentralized execution across 6 quadcopters.

3. Sensor Suite & SLAM Integration#3

RPLiDAR A2 360-degree laser scanner paired with PMW3901 optical flow sensor and Intel RealSense T265 tracking camera for visual-inertial odometry.

4. Communication & Ad-Hoc Mesh Protocols#4

Peer-to-peer 2.4GHz WiFi mesh networking using BATMAN-adv protocol guaranteeing sub-15ms packet latency across 60-meter node separation.

5. Empirical Validation & Benchmarks#5

Tested across 25 subterranean simulations yielding 94.2% collision-free trajectory completion and 128ms maximum path recalculation delay.