Sensor Integration for ABU Robocon 2025: Basketball Challenge

Basketball Game Task Analysis: Break down the basketball game into discrete tasks with specific sensing needs.

Performance Metric Definition: Create specific, quantifiable requirements for each sensor function.

Competition Rule Impact Analysis: Identify all rule constraints and opportunities related to sensing systems.

Prioritization Framework: Create a structured approach to allocating resources based on sensing priorities.

Perception Coverage Mapping: Create comprehensive coverage maps ensuring complete perception of relevant game elements.

Redundancy Planning: Design redundant sensing for critical functions while optimizing resource use.

Sensor Placement Optimization: Determine optimal locations for each sensor based on practical testing.

Inter-Robot Sensing Coordination: Create coordinated sensing strategies between multiple robots.

Decision Matrix Development: Build practical decision tools for objective sensor comparison and selection.

Specification Analysis Techniques: Develop expertise in translating specifications to real-world performance expectations.

Availability & Lead Time Management: Create practical approaches to ensuring timely sensor availability.

Cost-Benefit Analysis: Develop framework for making optimal cost-performance tradeoff decisions.

Standardized Testing Implementation: Create replicable, objective testing procedures for sensor evaluation.

Comparative Analysis Techniques: Master data analysis for effective sensor performance comparison.

Field-Relevant Testing: Develop testing approaches that accurately reflect competition environments.

Documentation & Knowledge Sharing: Establish effective documentation practices for sharing sensor benchmarking results.

Camera Type Selection: Identify optimal camera types for specific basketball detection tasks.

Camera Mounting Implementation: Create secure, adjustable, vibration-resistant camera mounts.

Lens Selection & Configuration: Determine ideal lens specifications for basketball tracking applications.

Camera Communication Integration: Create reliable, high-bandwidth connections for camera data transfer.

Processing Hardware Selection: Implement appropriate processing solutions for real-time vision requirements.

Vision Software Implementation: Create efficient vision software optimized for basketball detection tasks.

Algorithm Selection & Tuning: Identify and tune optimal algorithms for specific basketball vision tasks.

Performance Optimization Techniques: Implement practical optimizations for real-time vision performance.

Basketball Detection Implementation: Create reliable basketball detection that works under various conditions.

Feature Extraction Techniques: Implement effective feature extraction for robust basketball recognition.

Color-Based Detection Systems: Create reliable color-based detection systems with environmental robustness.

Shape Recognition Implementation: Implement effective shape recognition for basketball identification.

Variable Lighting Testing: Quantify camera performance across lighting conditions.

Exposure & Gain Configuration: Master camera parameter tuning for consistent performance.

Adaptive Algorithm Implementation: Create vision systems that automatically adapt to varying conditions.

Active Lighting Implementation: Develop effective auxiliary lighting to enhance vision reliability.

Encoder Selection & Integration: Create reliable wheel encoder installations for accurate odometry.

Wheel Odometry Implementation: Implement accurate wheel odometry systems for court navigation.

Drift Mitigation Techniques: Develop practical approaches to minimize odometry drift during operation.

Encoder Mounting Optimization: Create secure, precise encoder mounts that ensure accurate readings.

IMU Selection & Testing: Identify optimal IMU solutions for basketball robot applications.

IMU Calibration Procedures: Create effective calibration procedures for maximizing IMU accuracy.

Gyro Drift Compensation: Implement effective drift compensation for extended operation.

IMU Mounting Implementation: Create optimal mounting arrangements that minimize interference and vibration.

Court Feature Detection: Develop reliable detection of key court features for localization.

Localization Algorithm Implementation: Create effective localization using sensor data from multiple sources.

Distance Sensor Integration: Create reliable distance measurement systems for court mapping.

Landmark Recognition Systems: Implement effective landmark detection for position reference.

Line Detection Implementation: Create reliable line detection systems for boundary awareness.

Proximity Sensor Arrays: Develop comprehensive proximity sensing for obstacle avoidance.

Boundary Sensor Fusion: Create robust boundary detection through sensor fusion approaches.

Boundary Tracking Algorithms: Develop systems that maintain awareness of boundaries during gameplay.

Break-Beam Sensor Implementation: Create reliable break-beam installations for basketball passage detection.

Proximity-Based Ball Detection: Develop effective proximity sensing systems for basketball detection.

Pressure & Weight Sensing: Create reliable pressure or weight sensing for ball presence confirmation.

Redundant Detection Systems: Develop complementary detection methods for robust ball sensing.

Position Feedback Implementation: Create comprehensive position feedback for all critical mechanisms.

Limit Switch Integration: Develop reliable limit sensing for mechanism end-points and references.

Force & Pressure Sensing: Create force sensing systems for optimal ball handling control.

Mechanism Feedback Systems: Implement complete feedback systems for mechanism operational awareness.

Power Level Sensing: Create reliable power monitoring for consistent shooting control.

Angle & Orientation Sensing: Develop accurate angle sensing for precise shot direction control.

Velocity Measurement Implementation: Create accurate velocity sensing for shot power verification.

Shooting Confirmation Sensors: Implement reliable confirmation of shooting mechanism actuation.

Basket Detection Systems: Create reliable basket detection for shot targeting and verification.

Vision-Based Scoring Verification: Develop vision-based scoring detection for game awareness.

Sound-Based Score Detection: Create audio-based scoring confirmation through sound pattern recognition.

Scoring Confidence Metrics: Implement confidence scoring for accurate game state awareness.

Noise Characterization Methods: Develop practical approaches to quantifying different types of sensor noise.

Shielding Implementation: Create effective shielding solutions for sensitive sensors.

Grounding Optimization: Develop optimal grounding approaches for noise reduction.

Power Filtering Implementation: Create clean power delivery systems for improved sensor performance.

Low-Pass Filter Implementation: Create effective low-pass filters for noise reduction in various sensors.

Kalman Filter Application: Develop effective Kalman filters for sensor data enhancement.

Complementary Filter Design: Create efficient complementary filters for combining complementary sensors.

Filter Tuning Methods: Master practical filter tuning for optimal performance.

Complementary Sensor Selection: Develop expertise in selecting sensors with complementary strengths and weaknesses.

Weighted Fusion Implementation: Create effective weighting schemes for combining sensors of different reliability.

State Estimation Techniques: Develop practical state estimation approaches for robot positioning.

Temporal Alignment Methods: Create effective temporal alignment for accurate sensor fusion.

Failure Detection Implementation: Create reliable detection of various sensor failure modes.

Data Validation Techniques: Develop practical approaches to identifying invalid sensor data.

Backup Sensor Systems: Create effective backup sensing for critical functions.

Graceful Degradation Implementation: Implement graceful performance degradation when sensors fail.

Calibration Procedure Development: Develop effective, repeatable calibration procedures for all sensor types.

Auto-Calibration Implementation: Create sensors that automatically calibrate or maintain calibration.

Field Calibration Techniques: Master quick, effective calibration approaches for competition use.

Calibration Tracking Systems: Develop methods to detect calibration drift during operation.

Lighting Variation Testing: Quantify and improve sensor performance across lighting conditions.

Temperature Sensitivity Analysis: Understand and mitigate temperature effects on sensor performance.

Interference Testing: Identify and address interference vulnerabilities in sensor systems.

Environmental Chamber Testing: Validate sensor performance across all potential operating conditions.

Unit Testing Implementation: Create effective unit testing procedures for all sensor types.

Integration Testing Methods: Develop effective testing of sensor interactions and combined performance.

Field Testing Procedures: Create comprehensive field testing protocols that validate competition readiness.

Corner Case Identification: Systematically identify and test edge cases for all sensor systems.

Pre-Competition Checklist Development: Develop systematic pre-match sensor validation protocols.

Diagnostic Tool Implementation: Create effective diagnostic capabilities for field troubleshooting.

Spare Sensor Strategy: Develop comprehensive spare part and replacement strategies.

Quick-Swap Design Implementation: Create sensor mounting and connection systems for rapid field replacement.