RSI-PI/ROADMAP.md

RSIPI Improvement Roadmap

Goal: Transform RSIPI into publication-quality research software for industrial robot control

Status: Phase 1 ✅ Complete | Phase 2 ✅ Complete | Phase 5 ✅ Complete | Phase 3-4, 6 📋 Planned

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Overview

Six-phase improvement plan to make RSIPI world-class Python library for KUKA RSI control, suitable for publication in robotics research papers and industrial applications.

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✅ Phase 1: Code Quality Foundation (COMPLETE)

Objective: Establish professional code quality baseline

Completed Tasks:

• ✅ Add comprehensive type hints to all core modules (500+ annotations)
• ✅ Create custom exception hierarchy (20+ specialized exceptions)
• ✅ Replace all print() statements with proper logging
• ✅ Add comprehensive docstrings with Args/Returns/Raises sections
• ✅ Improve error handling with exception chaining

Files Modified:

• rsi_client.py - State machine with typed exceptions
• network_handler.py - CSV logging and UDP communication
• config_parser.py - XML parsing with proper exception handling
• safety_manager.py - Safety validation with typed limits
• exceptions.py - NEW comprehensive exception hierarchy

Commit: 50e6df9 (January 16, 2026)

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✅ Phase 2: Network Reliability (COMPLETE)

Objective: Ensure rock-solid network communication and diagnostics

Completed Tasks:

• ✅ Implement timing instrumentation (latency, jitter, cycle time tracking)
• ✅ Add watchdog timer for communication loss detection
• ✅ Implement network quality monitoring (packet loss, IPOC gaps, buffer health)
• ✅ Optimize CSV logging to prevent timing impact (batched updates every 100 cycles)
• ✅ Add auto-reconnection with graceful recovery
• ✅ Create 24-hour stability test infrastructure

Deliverables:

• Fully implemented DiagnosticsAPI namespace
• Real-time network health monitoring with TimingMetrics class
• Automatic recovery from network failures via AutoReconnectManager
• Comprehensive metrics tracking (cycle time, jitter, packet loss, IPOC gaps)
• 24-hour stability test script with JSON reporting

Files Created/Modified:

• timing_metrics.py - NEW TimingMetrics and NetworkQualityMonitor classes
• auto_reconnect.py - NEW AutoReconnectManager with retry strategies
• network_handler.py - Integrated timing metrics into real-time loop
• rsi_client.py - Added shared metrics dict and auto-reconnect support
• diagnostics_api.py - Fully implemented (was placeholder)
• tests/stability_test.py - NEW 24-hour stability test script

API Methods:

• api.diagnostics.get_stats() - Comprehensive network and performance statistics
• api.diagnostics.get_timing() - Timing-specific metrics
• api.diagnostics.is_healthy() - Overall system health check
• api.diagnostics.get_network_quality() - Network quality metrics
• api.diagnostics.check_watchdog() - Watchdog timeout status
• api.diagnostics.format_stats() - Human-readable statistics

Commits:

• 6e8ea2e - Timing instrumentation and diagnostics (January 17, 2026)
• bb65500 - Auto-reconnection and stability testing (January 17, 2026)

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📋 Phase 3: KRL Coordination (PLANNED)

Objective: Seamless Python-KRL coordination and communication

Planned Tasks:

1. Implement high-level Digital I/O API (set_output, get_input, pulse)
2. Add KRL state coordination helpers (wait_for_signal, signal_complete)
3. Implement parameter passing via Tech variables
4. Create KRL code templates for all coordination scenarios
5. Enhance inject_rsi_to_krl with coordination boilerplate options

Expected Deliverables:

• Enhanced IOAPI with high-level I/O methods
• Enhanced KRLAPI with coordination helpers
• KRL template library for common patterns
• Example coordination workflows
• Documentation on Python-KRL handshaking

Target Methods:

• api.io.set_output(channel, value)
• api.io.get_input(channel)
• api.io.pulse(channel, duration)
• api.krl.wait_for_signal(channel, timeout)
• api.krl.signal_complete(channel)
• api.krl.write_param(slot, value)
• api.krl.read_param(slot)

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📋 Phase 4: Advanced Motion Control (PLANNED)

Objective: Professional-grade trajectory planning and execution

Planned Tasks:

1. Implement velocity profiling (trapezoidal, S-curve)
2. Add coordinate frame transformation helpers
3. Implement motion primitives (arc, circle, spiral)
4. Add path blending for smooth transitions

Expected Deliverables:

• Enhanced MotionAPI with advanced planning
• Velocity profiling algorithms
• Geometric motion primitives
• Path blending for continuous motion
• Motion planning examples

Target Methods:

• api.motion.generate_velocity_profile(trajectory, profile='trapezoidal')
• api.motion.generate_arc(center, radius, start_angle, end_angle)
• api.motion.generate_circle(center, radius)
• api.motion.generate_spiral(center, radius, pitch)
• api.motion.blend_trajectories(traj1, traj2, blend_radius)
• api.motion.transform_coordinates(pose, frame='BASE')

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✅ Phase 5: API Restructuring (COMPLETE)

Objective: Clean, namespaced API architecture

Completed Tasks:

• ✅ Create SafetyAPI namespace class
• ✅ Create IOAPI namespace class
• ✅ Create MonitoringAPI namespace class
• ✅ Create LoggingAPI namespace class
• ✅ Create KRLAPI namespace class
• ✅ Create ToolsAPI namespace class
• ✅ Create VizAPI namespace class
• ✅ Create MotionAPI namespace class
• ✅ Create DiagnosticsAPI placeholder class
• ✅ Restructure RSIAPI as orchestrator with namespace properties

New Namespace Structure:

api = RSIAPI('RSI_EthernetConfig.xml')
api.motion      # Motion control
api.io          # Digital I/O
api.krl         # KRL manipulation
api.safety      # Safety management
api.monitoring  # Live data access
api.logging     # CSV logging
api.diagnostics # Network diagnostics
api.viz         # Visualization
api.tools       # Utilities

Breaking Changes:

• No backward compatibility (clean slate)
• Old API completely replaced with namespaced structure

Files Created:

• motion_api.py, io_api.py, krl_api.py, safety_api.py
• monitoring_api.py, logging_api.py, diagnostics_api.py
• viz_api.py, tools_api.py

Commit: 50e6df9 (January 16, 2026)

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📋 Phase 6: Validation & Benchmarking (PLANNED)

Objective: Prove production-readiness and publish results

Planned Tasks:

1. Create performance benchmark suite (vs ROS, vs KUKA SDK)
2. Run long-duration stability tests with real robot
3. Document example applications and use cases

Expected Deliverables:

• Benchmark comparison report (RSIPI vs ROS vs KUKA SDK)
• 24-hour+ stability test results
• Latency/jitter performance analysis
• Example applications repository
• Use case documentation
• Publication-ready performance data

Benchmark Metrics:

• Communication latency (round-trip time)
• Jitter and timing variance
• Maximum sustainable update rate
• CPU/memory overhead comparison
• Reliability (packet loss, connection uptime)

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Project Structure After All Phases

rsi-pi/
├── src/RSIPI/
│   ├── rsi_api.py              # Main orchestrator
│   ├── rsi_client.py           # Core RSI client
│   ├── motion_api.py           # Motion control namespace
│   ├── io_api.py               # Digital I/O namespace
│   ├── krl_api.py              # KRL manipulation namespace
│   ├── safety_api.py           # Safety management namespace
│   ├── monitoring_api.py       # Monitoring namespace
│   ├── logging_api.py          # CSV logging namespace
│   ├── diagnostics_api.py      # Network diagnostics namespace
│   ├── viz_api.py              # Visualization namespace
│   ├── tools_api.py            # Utilities namespace
│   ├── network_handler.py      # UDP communication
│   ├── config_parser.py        # XML config parsing
│   ├── safety_manager.py       # Safety validation
│   ├── exceptions.py           # Exception hierarchy
│   ├── xml_handler.py          # XML generation
│   ├── trajectory_planner.py   # Trajectory generation
│   ├── static_plotter.py       # Static plots
│   ├── live_plotter.py         # Live plots
│   ├── krl_to_csv_parser.py    # KRL parsing
│   ├── inject_rsi_to_krl.py    # KRL injection
│   └── kuka_visualiser.py      # Visualization
├── tests/                      # Test suite
├── examples/                   # Example applications
├── benchmarks/                 # Performance benchmarks
├── docs/                       # Documentation
├── README.md
└── RSIPI_ROADMAP.md           # This file

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Success Criteria

Phase 1 & 5 (Complete):

• ✅ 500+ type annotations across codebase
• ✅ 20+ custom exceptions with proper hierarchy
• ✅ Zero print() statements (all logging)
• ✅ Comprehensive docstrings on all public methods
• ✅ 9 namespaced API classes with clean separation
• ✅ Professional API design pattern

Phase 2 (Complete):

• ✅ Real-time network quality monitoring
• ✅ Automatic recovery from network failures
• ✅ Comprehensive diagnostics dashboard
• ✅ TimingMetrics tracking (cycle time, jitter, packet loss)
• ✅ AutoReconnectManager with configurable retry strategies
• ✅ 24-hour stability test infrastructure
• ⏳ Run actual 24-hour stability test (pending hardware)

Phase 3 (Planned):

• High-level I/O API with pulse generation
• Python-KRL coordination patterns documented
• Tech variable parameter passing working
• KRL template library created
• Example coordination workflows

Phase 4 (Planned):

• Trapezoidal and S-curve velocity profiles
• Arc, circle, spiral motion primitives
• Path blending with configurable blend radius
• Coordinate frame transformations
• Smooth continuous motion demonstrated

Phase 6 (Planned):

• Performance benchmarks vs ROS/KUKA SDK
• Publication-ready data and graphs
• Long-duration stability proven
• Multiple example applications
• Use cases documented

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Timeline

• Phase 1: ✅ Complete (January 16, 2026)
• Phase 2: ✅ Complete (January 17, 2026)
• Phase 5: ✅ Complete (January 16, 2026)
• Phase 3: 📋 Next priority
• Phase 4: 📋 After Phase 3
• Phase 6: 📋 Final validation

Approach: "Get it right the first time" - complete each phase fully before moving to the next.

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Research Publication Goal

Target: High-quality research paper demonstrating RSIPI as lightweight, high-performance alternative to ROS for KUKA robot control in drilling/manufacturing applications.

Key Points:

• Python-based, easy to integrate
• ~250Hz update rate, <5ms latency
• Industrial-grade reliability
• Comprehensive safety features
• Minimal dependencies
• Professional API design
• Proven stability (24-hour tests)
• Benchmarked against ROS

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Notes

• No backward compatibility - clean slate design
• Focus on quality over speed
• All features properly documented
• Type-safe with comprehensive testing
• Suitable for industrial research applications
• Designed for drilling PhD research (but general-purpose)

Last Updated: January 17, 2026