Skip to content

ROADMAP.md

Latest commit

 

History

History
346 lines (275 loc) · 13.3 KB

ROADMAP.md

File metadata and controls

346 lines (275 loc) · 13.3 KB

RSIPI Improvement Roadmap

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

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

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.

✅ 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)

✅ 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)

✅ Phase 3: KRL Coordination (COMPLETE)

Objective: Seamless Python-KRL coordination and communication

Completed Tasks:

  • ✅ Implement high-level Digital I/O API (set_output, get_input, pulse)
  • ✅ Add KRL state coordination helpers (wait_for_signal, signal_complete)
  • ✅ Implement parameter passing via Tech variables (write_param, read_param)
  • ✅ Create KRL code templates for all coordination scenarios (3 templates)
  • ✅ Create Python coordination example workflows (3 examples)

Deliverables:

  • Enhanced IOAPI with high-level I/O methods
  • Enhanced KRLAPI with coordination helpers
  • KRL template library (basic_handshake, parameter_passing, state_machine)
  • Python coordination examples (3 production-ready scripts)
  • Comprehensive documentation with KRL code examples

Files Created/Modified:

  • io_api.py - Added set_output(), get_input(), pulse() methods
  • krl_api.py - Added wait_for_signal(), signal_complete(), write_param(), read_param()
  • templates/krl/ - 3 KRL templates + README with coordination patterns
  • examples/coordination/ - 3 Python examples + README with usage guide

API Methods:

  • api.io.set_output(channel, value) - Set digital output by channel
  • api.io.get_input(channel) - Read digital input by channel
  • api.io.pulse(channel, duration) - Generate timed output pulse
  • api.krl.wait_for_signal(channel, timeout) - Wait for KRL I/O signal
  • api.krl.signal_complete(channel) - Signal KRL completion
  • api.krl.write_param(slot, value) - Write to Tech.C (Python → KRL)
  • api.krl.read_param(slot) - Read from Tech.T (KRL → Python)

Commit: 6e0b87b (January 17, 2026)

✅ Phase 4: Advanced Motion Control (COMPLETE)

Objective: Professional-grade trajectory planning and execution

Completed Tasks:

  • ✅ Implement velocity profiling (trapezoidal, S-curve)
  • ✅ Add coordinate frame transformation helpers
  • ✅ Implement motion primitives (arc, circle, spiral)
  • ✅ Add path blending for smooth transitions
  • ✅ Create comprehensive motion planning examples (5 examples)
  • ✅ Document all features with application use cases

Deliverables:

  • Enhanced MotionAPI with 5 new advanced planning methods
  • Velocity profiling algorithms (trapezoidal and S-curve)
  • Geometric motion primitives (arc, circle, spiral)
  • Path blending with cubic Hermite spline interpolation
  • Coordinate transformations between BASE/WORLD/TOOL/WORK frames
  • 5 production-ready motion planning examples
  • Comprehensive documentation (584-line README.md)

Files Created/Modified:

  • motion_api.py - Added 5 static methods + 4 helper functions (~550 lines)
  • examples/advanced_motion/01_velocity_profiles.py - NEW (234 lines)
  • examples/advanced_motion/02_geometric_primitives.py - NEW (225 lines)
  • examples/advanced_motion/03_path_blending.py - NEW (253 lines)
  • examples/advanced_motion/04_coordinate_transforms.py - NEW (284 lines)
  • examples/advanced_motion/05_combined_motion.py - NEW (336 lines)
  • examples/advanced_motion/README.md - NEW comprehensive guide (584 lines)
  • PHASE_4_SUMMARY.md - NEW detailed implementation documentation

API Methods:

  • api.motion.generate_velocity_profile(trajectory, max_velocity, max_acceleration, profile)
  • api.motion.generate_arc(center, radius, start_angle, end_angle, steps, plane)
  • api.motion.generate_circle(center, radius, steps, plane)
  • api.motion.generate_spiral(center, start_radius, end_radius, pitch, revolutions, steps, plane, axis)
  • api.motion.blend_trajectories(traj1, traj2, blend_radius, blend_steps)
  • api.motion.transform_coordinates(pose, from_frame, to_frame, frame_offset)

Commit: cc19e10 (January 17, 2026)

✅ 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)

📋 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)

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

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 (Complete):

  • ✅ High-level I/O API with pulse generation (set_output, get_input, pulse)
  • ✅ Python-KRL coordination patterns documented (templates/krl/README.md)
  • ✅ Tech variable parameter passing working (write_param, read_param)
  • ✅ KRL template library created (3 templates with full workflows)
  • ✅ Example coordination workflows (3 Python examples with documentation)

Phase 4 (Complete):

  • ✅ Trapezoidal and S-curve velocity profiles implemented
  • ✅ Arc, circle, spiral motion primitives created
  • ✅ Path blending with cubic interpolation and configurable blend radius
  • ✅ Coordinate frame transformations (BASE/WORLD/TOOL/WORK)
  • ✅ Smooth continuous motion demonstrated in examples
  • ✅ 5 comprehensive production-ready examples
  • ✅ 584-line documentation guide created

Phase 6 (Planned):

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

Timeline

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

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

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

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
⚡