# Robotics and Simulation Engineering - Processes Backlog

This document contains identified processes, workflows, and methodologies specific to Robotics and Simulation Engineering that can be implemented as Babysitter SDK orchestration processes.

## Implementation Guidelines

Each process should be implemented following the Babysitter SDK patterns:
- **Process file**: `processes/[process-name].js` or `processes/[process-name]/index.js`
- **JSDoc required**: `@process`, `@description`, `@inputs`, `@outputs`
- **Export pattern**: `export async function process(inputs, ctx) { ... }`
- **Task definitions**: Use `defineTask` from `@a5c-ai/babysitter-sdk`
- **Breakpoints**: Use `ctx.breakpoint()` for human approval gates
- **Parallel execution**: Use `ctx.parallel.all()` for independent tasks

---

## Process Categories

### Robot Development Lifecycle

#### 1. Robot System Design and Requirements
**Description**: Define comprehensive requirements and design specifications for a robotic system from mission objectives to hardware/software architecture

**Key Activities**:
- Define mission profile and operational requirements
- Establish performance metrics (speed, accuracy, reliability, autonomy)
- Identify environmental constraints (terrain, weather, obstacles)
- Specify sensor and actuator requirements
- Design mechanical architecture and kinematics
- Plan software architecture (perception, planning, control)
- Define safety requirements and certifications
- Create system integration plan

**References**:
- https://www.iso.org/committee/5915511.html (ISO TC 299 Robotics)
- https://www.iso.org/standard/51330.html (ISO 10218 Robot Safety)
- Modern Robotics textbook: http://modernrobotics.org/

**Estimated Complexity**: Very High

---

#### 2. Robot URDF/SDF Model Creation
**Description**: Create accurate robot models in URDF/SDF format for simulation and visualization

**Key Activities**:
- Design robot kinematic chain and joint hierarchy
- Create or import 3D meshes (visual and collision geometry)
- Define link properties (mass, inertia, dimensions)
- Configure joint types, limits, and dynamics
- Set up sensor models (cameras, LiDAR, IMU)
- Add visual properties (colors, textures)
- Validate model in RViz/Gazebo
- Optimize collision geometry for performance
- Document model parameters and conventions

**References**:
- http://wiki.ros.org/urdf/Tutorials
- http://sdformat.org/
- https://wiki.ros.org/xacro

**Estimated Complexity**: Medium

---

#### 3. Robot Calibration Workflow
**Description**: Systematic calibration of robot sensors, actuators, and kinematic parameters

**Key Activities**:
- Perform intrinsic camera calibration (focal length, distortion)
- Conduct extrinsic sensor calibration (sensor-to-sensor transforms)
- Calibrate robot kinematic parameters (DH parameters)
- Validate sensor-to-robot base transforms
- Calibrate IMU biases and scale factors
- Perform hand-eye calibration (camera-to-end-effector)
- Validate calibration accuracy with test scenarios
- Document calibration procedures and results
- Create recalibration schedule

**References**:
- https://wiki.ros.org/camera_calibration
- https://github.com/ethz-asl/kalibr
- http://wiki.ros.org/robot_calibration

**Estimated Complexity**: High

---

### Simulation Development

#### 4. Gazebo Simulation Environment Setup
**Description**: Create high-fidelity simulation environment using Gazebo for robot testing

**Key Activities**:
- Design world file with terrain and obstacles
- Import or create 3D models of environment
- Configure physics engine parameters (gravity, friction, solver)
- Add lighting and environmental effects
- Implement sensor plugins (camera, LiDAR, depth, IMU)
- Create dynamic objects and actors
- Set up multiple robot instances (if needed)
- Optimize simulation performance
- Validate physics accuracy

**References**:
- https://gazebosim.org/docs
- http://wiki.ros.org/gazebo_ros_pkgs
- https://classic.gazebosim.org/tutorials

**Estimated Complexity**: High

---

#### 5. Isaac Sim Photorealistic Simulation
**Description**: Build GPU-accelerated photorealistic simulation environment using NVIDIA Isaac Sim

**Key Activities**:
- Set up Isaac Sim environment and USD assets
- Import robot models (URDF to USD conversion)
- Design photorealistic environments with ray tracing
- Configure physics simulation (PhysX)
- Implement synthetic data generation (RGB, depth, segmentation, bounding boxes)
- Add domain randomization (lighting, textures, poses)
- Create simulation scenarios and test cases
- Optimize GPU performance
- Export synthetic datasets for training

**References**:
- https://developer.nvidia.com/isaac-sim
- https://docs.omniverse.nvidia.com/isaacsim/latest/
- https://developer.nvidia.com/isaac-ros

**Estimated Complexity**: Very High

---

#### 6. Digital Twin Development
**Description**: Create accurate digital twin of physical robot for development and testing

**Key Activities**:
- Measure and model physical robot accurately
- Implement bidirectional robot-sim communication
- Synchronize robot state (joint positions, sensor data)
- Validate simulation fidelity against real robot
- Implement hardware-in-the-loop (HIL) testing
- Create sim-to-real transfer validation tests
- Monitor and reduce reality gap
- Document discrepancies and limitations

**References**:
- https://arxiv.org/abs/2011.12820 (Reality Gap)
- https://ieeexplore.ieee.org/document/9387482 (Digital Twins)

**Estimated Complexity**: Very High

---

#### 7. Synthetic Data Generation Pipeline
**Description**: Automated pipeline for generating synthetic training data for perception models

**Key Activities**:
- Design data generation scenarios
- Implement domain randomization (appearance, lighting, camera pose)
- Configure randomized object placement
- Generate diverse sensor data (RGB, depth, point clouds, thermal)
- Create ground truth annotations (segmentation, bounding boxes, keypoints)
- Balance dataset across scenarios and conditions
- Validate data quality and diversity
- Export datasets in standard formats (COCO, KITTI, etc.)
- Measure domain gap metrics

**References**:
- https://arxiv.org/abs/1703.06907 (Domain Randomization)
- https://arxiv.org/abs/1804.06516 (Synthetic Data for Vision)
- https://developer.nvidia.com/isaac-sim

**Estimated Complexity**: High

---

#### 8. Simulation Performance Optimization
**Description**: Optimize simulation speed and scalability for large-scale testing

**Key Activities**:
- Profile simulation bottlenecks (rendering, physics, sensors)
- Optimize collision geometry (convex decomposition, simplified meshes)
- Implement level-of-detail (LOD) rendering
- Parallelize multiple simulation instances
- Leverage GPU acceleration
- Reduce sensor update rates strategically
- Implement headless simulation mode
- Measure and benchmark performance improvements

**References**:
- https://gazebosim.org/docs/latest/performance_tips
- https://docs.omniverse.nvidia.com/isaacsim/latest/manual_isaac_sim_performance.html

**Estimated Complexity**: Medium

---

### Perception and SLAM

#### 9. Visual SLAM Implementation
**Description**: Implement and tune visual SLAM system for robot localization and mapping

**Key Activities**:
- Select SLAM algorithm (ORB-SLAM3, RTAB-Map, Cartographer)
- Configure camera parameters and calibration
- Set up visual feature detection and tracking
- Implement loop closure detection
- Tune SLAM parameters (feature thresholds, keyframe selection)
- Add visual-inertial fusion (if IMU available)
- Test in diverse environments (indoor, outdoor, lighting variations)
- Evaluate accuracy (trajectory error, map quality)
- Optimize for real-time performance

**References**:
- https://github.com/UZ-SLAMLab/ORB_SLAM3
- https://github.com/introlab/rtabmap_ros
- https://google-cartographer-ros.readthedocs.io/

**Estimated Complexity**: Very High

---

#### 10. LiDAR-Based Mapping and Localization
**Description**: Implement 3D LiDAR SLAM for robust localization in GPS-denied environments

**Key Activities**:
- Select LiDAR SLAM framework (LOAM, LeGO-LOAM, LIO-SAM)
- Configure LiDAR sensor parameters
- Implement point cloud preprocessing (filtering, downsampling)
- Set up scan matching and registration
- Implement pose graph optimization
- Add IMU integration for odometry
- Test in challenging scenarios (dynamic objects, large-scale)
- Evaluate mapping accuracy and consistency
- Optimize computational efficiency

**References**:
- https://github.com/TixiaoShan/LIO-SAM
- https://github.com/HKUST-Aerial-Robotics/A-LOAM
- https://pointclouds.org/

**Estimated Complexity**: Very High

---

#### 11. Object Detection and Recognition Pipeline
**Description**: Develop perception pipeline for detecting and recognizing objects in robot's environment

**Key Activities**:
- Select detection model (YOLO, Detectron2, EfficientDet)
- Collect or generate training dataset
- Train/fine-tune model on target objects
- Optimize model for edge deployment (TensorRT, ONNX)
- Integrate with ROS perception pipeline
- Implement 3D bounding box estimation
- Add object tracking over time
- Test in varied lighting and occlusion conditions
- Measure detection accuracy (mAP, latency)

**References**:
- https://github.com/ultralytics/ultralytics (YOLOv8)
- https://github.com/facebookresearch/detectron2
- https://developer.nvidia.com/deepstream-sdk

**Estimated Complexity**: High

---

#### 12. Sensor Fusion Framework
**Description**: Implement multi-sensor fusion for robust state estimation

**Key Activities**:
- Design sensor fusion architecture
- Implement Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF)
- Fuse data from IMU, wheel odometry, GPS, and vision
- Handle sensor delays and asynchronous measurements
- Implement outlier rejection
- Tune filter parameters (process noise, measurement noise)
- Validate against ground truth
- Test under sensor degradation scenarios
- Monitor and log fusion performance

**References**:
- http://wiki.ros.org/robot_localization
- Probabilistic Robotics book: https://mitpress.mit.edu/9780262201629/
- https://github.com/ethz-asl/kalibr

**Estimated Complexity**: Very High

---

### Motion Planning and Control

#### 13. Path Planning Algorithm Implementation
**Description**: Implement and tune path planning algorithms for navigation

**Key Activities**:
- Select planning algorithm (A*, RRT, RRT*, Hybrid A*)
- Implement cost map representation
- Configure planner parameters (resolution, search radius)
- Add dynamic obstacle avoidance
- Implement path smoothing and optimization
- Handle planning failures gracefully
- Test in complex environments (narrow passages, dynamic obstacles)
- Measure planning time and path quality
- Optimize computational performance

**References**:
- https://ompl.kavrakilab.org/
- http://wiki.ros.org/move_base
- https://navigation.ros.org/ (Nav2)

**Estimated Complexity**: High

---

#### 14. MPC Controller Design
**Description**: Design and implement Model Predictive Control for robot trajectory tracking

**Key Activities**:
- Derive robot kinematic or dynamic model
- Formulate MPC optimization problem (cost function, constraints)
- Implement MPC solver (CasADi, ACADO, CVXPY)
- Tune MPC parameters (prediction horizon, weights)
- Add constraint handling (velocity limits, collision avoidance)
- Test tracking performance on reference trajectories
- Validate in simulation and hardware
- Optimize solver runtime for real-time execution
- Compare with baseline controllers (PID, LQR)

**References**:
- https://web.casadi.org/
- https://acado.github.io/
- https://cvxpy.org/

**Estimated Complexity**: Very High

---

#### 15. Manipulation Planning with MoveIt
**Description**: Configure MoveIt for robotic arm motion planning and manipulation

**Key Activities**:
- Set up MoveIt configuration package
- Configure robot kinematics (IK solver)
- Define planning groups and end effectors
- Set up collision checking
- Configure motion planning pipeline (OMPL planners)
- Implement grasp planning
- Add perception integration (3D object pose estimation)
- Test pick-and-place operations
- Tune planning parameters for success rate and speed

**References**:
- https://moveit.ros.org/
- https://ros-planning.github.io/moveit_tutorials/
- https://github.com/ros-planning/moveit2

**Estimated Complexity**: High

---

#### 16. Trajectory Optimization
**Description**: Implement trajectory optimization for smooth and efficient robot motion

**Key Activities**:
- Formulate trajectory optimization problem
- Define cost function (smoothness, time, energy)
- Add kinematic and dynamic constraints
- Implement optimization solver (TrajOpt, CHOMP, STOMP)
- Handle obstacles and collision avoidance
- Optimize trajectory timing and velocity profiles
- Test on complex motion scenarios
- Validate feasibility on physical robot
- Measure trajectory quality metrics

**References**:
- http://rll.berkeley.edu/trajopt/
- https://github.com/ros-planning/moveit/tree/master/moveit_planners/chomp
- https://ros-planning.github.io/moveit_tutorials/doc/stomp_planner/stomp_planner_tutorial.html

**Estimated Complexity**: Very High

---

### Autonomous Navigation

#### 17. Nav2 Navigation Stack Setup
**Description**: Configure ROS 2 Nav2 for autonomous mobile robot navigation

**Key Activities**:
- Set up Nav2 navigation stack
- Configure behavior trees for navigation logic
- Implement costmap layers (static, obstacle, inflation)
- Configure local and global planners
- Set up recovery behaviors
- Implement waypoint following
- Add dynamic obstacle avoidance
- Test in simulation and real environments
- Tune parameters for optimal navigation

**References**:
- https://navigation.ros.org/
- https://github.com/ros-planning/navigation2
- https://docs.nav2.org/tutorials/

**Estimated Complexity**: High

---

#### 18. Dynamic Obstacle Avoidance
**Description**: Implement real-time obstacle avoidance for mobile robots

**Key Activities**:
- Select avoidance algorithm (DWA, TEB, MPC-based)
- Integrate with perception system (laser scan, point cloud)
- Configure velocity constraints and dynamics
- Implement prediction for moving obstacles
- Add social navigation behaviors (for human environments)
- Test with dynamic obstacles at various speeds
- Validate safety margins and collision avoidance
- Optimize replanning frequency
- Measure navigation success rate

**References**:
- https://wiki.ros.org/dwa_local_planner
- https://wiki.ros.org/teb_local_planner
- https://arxiv.org/abs/2103.14750 (Social Navigation)

**Estimated Complexity**: High

---

#### 19. Autonomous Exploration and Mapping
**Description**: Implement frontier-based autonomous exploration for unknown environments

**Key Activities**:
- Implement frontier detection algorithm
- Prioritize frontiers by information gain
- Integrate with SLAM system
- Plan safe paths to exploration targets
- Handle exploration in multi-story or complex environments
- Add loop closure for map consistency
- Implement exploration termination criteria
- Test in diverse environments (indoor, outdoor, structured, unstructured)
- Measure exploration efficiency (coverage, time)

**References**:
- https://github.com/RobustFieldAutonomyLab/explore_lite
- https://wiki.ros.org/frontier_exploration
- https://ieeexplore.ieee.org/document/613851 (Frontier-based Exploration)

**Estimated Complexity**: Very High

---

### Learning-Based Robotics

#### 20. Reinforcement Learning for Robot Control
**Description**: Train RL agent for robot control task using simulation

**Key Activities**:
- Define task and reward function
- Set up simulation environment (Gym/Gymnasium interface)
- Select RL algorithm (PPO, SAC, TD3)
- Implement observation and action spaces
- Design reward shaping strategy
- Train agent in simulation (with parallelization)
- Monitor training progress (reward curves, success rate)
- Validate trained policy
- Implement sim-to-real transfer techniques (domain randomization)
- Test on physical robot

**References**:
- https://stable-baselines3.readthedocs.io/
- https://arxiv.org/abs/1804.10332 (Sim-to-Real Locomotion)
- https://sites.google.com/view/simtoreal

**Estimated Complexity**: Very High

---

#### 21. Imitation Learning from Demonstrations
**Description**: Train robot policy from human demonstrations using behavior cloning or inverse RL

**Key Activities**:
- Collect expert demonstrations (teleoperation, motion capture)
- Preprocess and augment demonstration data
- Design neural network architecture
- Implement behavior cloning training
- Add DAgger for iterative improvement (optional)
- Validate learned policy in simulation
- Measure imitation accuracy
- Fine-tune with RL (if needed)
- Deploy to physical robot

**References**:
- https://arxiv.org/abs/1011.0686 (DAgger)
- https://sites.google.com/view/deep-imitation-learning
- https://rll.berkeley.edu/

**Estimated Complexity**: Very High

---

#### 22. Neural Network Model Optimization for Edge Deployment
**Description**: Optimize ML models for deployment on robot edge devices (Jetson, embedded)

**Key Activities**:
- Profile model inference performance
- Quantize model (INT8, FP16)
- Apply pruning and knowledge distillation
- Convert to optimized format (TensorRT, ONNX)
- Validate accuracy after optimization
- Benchmark latency and throughput on target hardware
- Optimize preprocessing pipeline
- Implement model versioning and updates
- Deploy with ROS integration

**References**:
- https://developer.nvidia.com/tensorrt
- https://onnx.ai/
- https://pytorch.org/docs/stable/quantization.html

**Estimated Complexity**: High

---

### Hardware Integration and Testing

#### 23. Hardware-in-the-Loop (HIL) Testing
**Description**: Set up HIL testing framework to validate algorithms with real sensors and actuators

**Key Activities**:
- Design HIL architecture (simulation + hardware)
- Implement real-time bridge between simulation and hardware
- Connect physical sensors to simulation
- Connect simulated actuators to physical motors
- Synchronize timing between sim and hardware
- Validate sensor data flow
- Test control loops with real actuators
- Identify timing and latency issues
- Document HIL test procedures

**References**:
- https://www.ni.com/en-us/innovations/white-papers/13/hardware-in-the-loop-hil-simulation.html
- http://wiki.ros.org/gazebo_ros_pkgs

**Estimated Complexity**: Very High

---

#### 24. Robot Bring-Up and Integration Testing
**Description**: Systematic bring-up of physical robot with full software stack integration

**Key Activities**:
- Assemble and wire robot hardware
- Flash firmware to microcontrollers
- Test low-level drivers (motors, sensors, communication)
- Bring up ROS nodes incrementally
- Validate sensor data streams
- Test actuator commands and responses
- Verify safety systems (emergency stop, watchdogs)
- Conduct sensor-actuator loop tests
- Run integration test suite
- Document hardware configuration

**References**:
- http://wiki.ros.org/robot_upstart
- https://docs.ros.org/en/humble/Tutorials.html

**Estimated Complexity**: Very High

---

#### 25. Field Testing and Validation
**Description**: Conduct comprehensive field testing in target operational environment

**Key Activities**:
- Define test scenarios and success criteria
- Prepare test environment (indoor, outdoor, structured, unstructured)
- Conduct safety risk assessment
- Execute test scenarios systematically
- Log sensor data and system performance
- Monitor for failures and edge cases
- Iterate on failures and retesting
- Measure performance metrics (mission success, reliability, MTBF)
- Document lessons learned
- Update requirements and design based on results

**References**:
- https://www.iso.org/standard/62996.html (ISO/TS 15066 Collaborative Robots)
- https://sre.google/sre-book/testing-reliability/

**Estimated Complexity**: Very High

---

### Advanced Topics

#### 26. Multi-Robot Coordination
**Description**: Implement coordination and task allocation for robot fleets

**Key Activities**:
- Design multi-robot coordination architecture
- Implement task allocation algorithm (auction-based, optimization-based)
- Set up inter-robot communication (ROS multi-master, DDS)
- Implement conflict resolution (path deconfliction)
- Add fleet management and monitoring
- Implement collaborative SLAM (if needed)
- Test with multiple simulated robots
- Validate coordination efficiency
- Deploy to physical robot fleet

**References**:
- https://arxiv.org/abs/2203.08975 (Multi-Agent RL with Communication)
- https://github.com/robofleet/robofleet
- https://github.com/osrf/free_fleet

**Estimated Complexity**: Very High

---

#### 27. Human-Robot Interaction (HRI) Interface
**Description**: Develop intuitive interfaces for human-robot interaction and collaboration

**Key Activities**:
- Design interaction modalities (voice, gesture, GUI, AR/VR)
- Implement natural language interface
- Add gesture recognition
- Create intuitive GUI/dashboard
- Implement teleoperation interface
- Add haptic feedback (if applicable)
- Conduct user studies and usability testing
- Iterate based on user feedback
- Ensure accessibility compliance

**References**:
- http://wiki.ros.org/rqt
- https://www.ros.org/reps/rep-0149.html (ROS HRI)
- https://ieeexplore.ieee.org/document/9387482

**Estimated Complexity**: High

---

#### 28. Safety System Validation and Certification
**Description**: Validate robot safety systems and prepare for certification (ISO 13482, ISO 10218)

**Key Activities**:
- Conduct hazard analysis (HAZOP, FMEA)
- Identify safety-critical functions
- Implement safety-rated hardware (emergency stops, safety PLCs)
- Design safety software architecture (redundancy, watchdogs)
- Conduct safety system testing (fault injection)
- Validate risk reduction measures
- Prepare safety documentation for certification
- Work with certification body
- Conduct final safety validation

**References**:
- https://www.iso.org/standard/53820.html (ISO 13482 Personal Care Robots)
- https://www.iso.org/standard/51330.html (ISO 10218 Industrial Robots)
- https://ul.com/standards/3100

**Estimated Complexity**: Very High

---

#### 29. Sim-to-Real Transfer Validation
**Description**: Systematically validate and minimize reality gap for sim-to-real transfer

**Key Activities**:
- Measure simulation vs. real-world performance gap
- Identify sources of domain gap (physics, sensors, dynamics)
- Implement domain randomization in simulation
- Apply domain adaptation techniques
- Use system identification to refine simulation parameters
- Conduct ablation studies (what randomization helps)
- Validate on multiple physical robots
- Iterate on simulation fidelity
- Document transfer methodology
- Measure final sim-to-real performance

**References**:
- https://arxiv.org/abs/1703.06907 (Domain Randomization)
- https://arxiv.org/abs/2011.12820 (Reality Gap)
- https://sites.google.com/view/simtoreal

**Estimated Complexity**: Very High

---

#### 30. Robot Fleet Management System
**Description**: Build centralized system for managing, monitoring, and coordinating robot fleets

**Key Activities**:
- Design fleet management architecture
- Implement robot registration and discovery
- Create centralized monitoring dashboard
- Add remote telemetry and logging
- Implement task scheduling and dispatching
- Add fleet-wide software updates (OTA)
- Monitor robot health and diagnostics
- Implement alert and incident management
- Add analytics and reporting
- Scale to handle large fleets

**References**:
- https://github.com/robofleet/robofleet
- https://github.com/osrf/free_fleet
- https://aws.amazon.com/robomaker/

**Estimated Complexity**: Very High

---

## Implementation Priority

### Phase 1: Foundation (High Priority)
1. Robot System Design and Requirements
2. Gazebo Simulation Environment Setup
3. Robot URDF/SDF Model Creation
4. Path Planning Algorithm Implementation
5. Nav2 Navigation Stack Setup

### Phase 2: Perception and Control (High Priority)
6. Visual SLAM Implementation
7. LiDAR-Based Mapping and Localization
8. Object Detection and Recognition Pipeline
9. MPC Controller Design
10. Robot Calibration Workflow

### Phase 3: Advanced Simulation (Medium Priority)
11. Isaac Sim Photorealistic Simulation
12. Digital Twin Development
13. Synthetic Data Generation Pipeline
14. Sim-to-Real Transfer Validation
15. Simulation Performance Optimization

### Phase 4: Autonomy and Intelligence (Medium Priority)
16. Sensor Fusion Framework
17. Dynamic Obstacle Avoidance
18. Autonomous Exploration and Mapping
19. Manipulation Planning with MoveIt
20. Trajectory Optimization

### Phase 5: Learning and AI (Advanced Priority)
21. Reinforcement Learning for Robot Control
22. Imitation Learning from Demonstrations
23. Neural Network Model Optimization for Edge Deployment

### Phase 6: Integration and Deployment (Advanced Priority)
24. Hardware-in-the-Loop (HIL) Testing
25. Robot Bring-Up and Integration Testing
26. Field Testing and Validation
27. Safety System Validation and Certification

### Phase 7: Fleet and Collaboration (Specialized Priority)
28. Multi-Robot Coordination
29. Robot Fleet Management System
30. Human-Robot Interaction (HRI) Interface

---

## Process Patterns

### Common Task Types
- **Requirements Analysis**: Define mission objectives, constraints, success criteria
- **Design**: Architectural design, algorithm selection, system modeling
- **Implementation**: Code development, ROS node creation, configuration
- **Simulation**: Test in Gazebo/Isaac Sim, validate behavior
- **Calibration**: Sensor calibration, parameter tuning, system identification
- **Testing**: Unit tests, integration tests, field tests
- **Optimization**: Performance profiling, algorithm tuning, resource optimization
- **Validation**: Verify against requirements, measure metrics, conduct experiments
- **Documentation**: Technical docs, user guides, runbooks, calibration procedures

### Common Breakpoints (Human Approval Gates)
- Safety review before hardware testing
- Design review for robot architecture
- Calibration validation before deployment
- Performance validation against requirements
- Go/no-go decision for field testing
- Review of trained ML models before deployment
- Approval for multi-robot coordination tests
- Certification readiness review

### Parallel Execution Opportunities
- Multiple simulation scenarios running concurrently
- Parallel training of ML models with different hyperparameters
- Independent sensor calibration tasks
- Simultaneous testing of multiple navigation algorithms
- Parallel data collection from multiple robots
- Concurrent development of perception and control modules

---

## Specialized Considerations

### Real-Time Performance
- Many robotics processes require hard real-time guarantees
- Control loops must meet strict timing deadlines (typically 100Hz-1kHz)
- Consider RT_PREEMPT kernel for ROS 2 deployments
- Profile and optimize critical paths

### Safety-Critical Systems
- Robot safety must be validated at every stage
- Implement layered safety architecture (hardware + software)
- Conduct hazard analysis and risk assessment
- Follow ISO safety standards (10218, 13482, 15066)
- Test emergency stop and fail-safe behaviors

### Sim-to-Real Gap
- Minimize reality gap through accurate simulation
- Use domain randomization for robust policies
- Validate simulation fidelity continuously
- Implement incremental hardware testing

### Hardware Constraints
- Embedded systems have limited compute, memory, power
- Optimize algorithms for edge deployment
- Consider model compression and quantization
- Profile resource usage continuously

---

**Created**: 2026-01-23
**Version**: 1.0.0
**Status**: Phase 2 - Processes Identified
**Next Step**: Phase 3 - Implement process JavaScript files
