RSI-PI/ROADMAP.md

RSIPI Improvement Roadmap

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

Status: Phase 1 ✅ Complete | Phase 5 ✅ Complete | Phase 2-4, 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 (PLANNED)

Objective: Ensure rock-solid network communication and diagnostics

Planned Tasks:

1. Implement timing instrumentation (latency, jitter, cycle time tracking)
2. Add watchdog timer for communication loss detection
3. Implement network quality monitoring (packet loss, IPOC gaps, buffer health)
4. Optimize CSV logging to prevent timing impact
5. Add auto-reconnection with graceful recovery
6. Run 24-hour echo server stability test and collect metrics

Expected Deliverables:

• Fully implemented DiagnosticsAPI namespace
• Real-time network health monitoring
• Automatic recovery from network failures
• Performance metrics dashboard
• Stability test report

Target Namespace:

• api.diagnostics.get_stats()
• api.diagnostics.get_timing()
• api.diagnostics.is_healthy()
• api.diagnostics.get_network_quality()
• api.diagnostics.start_watchdog()

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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 (Planned):

• Real-time network quality monitoring
• Automatic recovery from network failures
• <5ms average latency, <2ms jitter
• 24-hour stability test passes
• Comprehensive diagnostics dashboard

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 5: ✅ Complete (January 16, 2026)
• Phase 2: 📋 Next priority
• Phase 3: 📋 After Phase 2
• 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 16, 2026