Distributed Control Prototyping Framework

---
name: distributed-control-prototyping-framework
description: >
  Prototyping framework for distributed control of multi-robot systems using SPMD
  (Single Program, Multiple Data) paradigm. Emulates distributed control on a single
  computer, bridging theory and practical testing. Each core runs the same algorithm
  with local states and neighbor-to-neighbor communication. Use when: prototyping
  distributed control algorithms, testing multi-robot coordination, emulating distributed
  systems on single machine, SPMD paradigm for robotics, distributed optimization
  validation, or bridge theory-to-practice for multi-agent control.
  Keywords: SPMD, distributed control, multi-robot, prototyping, emulation, neighbor
  communication, distributed optimization.
---

# Distributed Control Prototyping Framework

SPMD-based framework for prototyping and testing distributed control algorithms
for multi-robot systems on a single computer.

## Core Architecture: SPMD Paradigm

```
┌─────────┐    ┌─────────┐    ┌─────────┐
│ Core 0  │◄──►│ Core 1  │◄──►│ Core 2  │
│ Robot 0 │    │ Robot 1 │    │ Robot 2 │
│ state₀  │    │ state₁  │    │ state₂  │
└────┬────┘    └────┬────┘    └────┬────┘
     └──────────────┼──────────────┘
              Neighbor Communication
```

### Design Principles
- **Single Program**: All cores execute identical control algorithm
- **Multiple Data**: Each core maintains local state (position, velocity, sensor data)
- **Neighbor Communication**: Only exchange data with defined neighbors
- **Local Decision Making**: No global state required

## Implementation Patterns

### Robot Agent Structure
```python
class DistributedRobot:
    def __init__(self, robot_id, neighbors):
        self.id = robot_id
        self.neighbors = neighbors  # IDs of communication neighbors
        self.local_state = {}       # Position, velocity, sensors
        self.boundary_data = {}     # Latest data from neighbors
    
    def sense(self):
        """Read local sensors (position, IMU, etc.)"""
        pass
    
    def communicate(self):
        """Send local state to neighbors, receive their state"""
        pass
    
    def compute_control(self):
        """Compute local control action using local + neighbor data"""
        pass
    
    def actuate(self, control):
        """Apply control to local robot"""
        pass
    
    def step(self):
        self.sense()
        self.communicate()
        self.compute_control()
        self.actuate(control)
```

### Communication Abstraction
```python
# Emulated (single machine, shared memory)
class SharedMemoryBus:
    def send(self, sender_id, data):
        for receiver_id in neighbors[sender_id]:
            buffers[receiver_id][sender_id] = data
    
    def receive(self, receiver_id):
        return buffers[receiver_id]

# Hardware (ROS2, DDS, or custom protocol)
class ROS2Communication:
    def send(self, sender_id, data):
        publisher.publish(data)
    
    def receive(self, receiver_id):
        return latest_from_subscriber
```

### Distributed Optimization Algorithms
```python
# Consensus-based coordination
def distributed_consensus(robot, consensus_var, step_size):
    """Distributed average consensus"""
    local_val = robot.local_state[consensus_var]
    neighbor_vals = robot.boundary_data.get(consensus_var, {})
    
    # Weighted average with neighbors
    avg = local_val
    for nid, nval in neighbor_vals.items():
        avg += step_size * (nval - local_val)
    
    return avg

# Distributed MPC
def distributed_mpc(robot, horizon, neighbor_trajectories):
    """Local MPC with neighbor trajectory predictions"""
    predictions = neighbor_trajectories  # From communication
    # Solve local optimization with collision avoidance constraints
    return solve_local_mpc(robot, predictions, horizon)

# Formation control
def distributed_formation(robot, desired_shape, neighbors_state):
    """Maintain formation using only local neighbor info"""
    errors = []
    for nid, nstate in neighbors_state.items():
        desired_rel = desired_shape[robot.id][nid]
        actual_rel = nstate.position - robot.local_state.position
        errors.append(actual_rel - desired_rel)
    return sum(errors)
```

## Framework Workflow

### 1. Algorithm Development
- Write control algorithm in SPMD style
- Test on emulated framework (single machine)
- Iterate rapidly without hardware

### 2. Emulation Phase
```bash
# Launch emulated multi-robot system
python emulate.py --num-robots 4 --algorithm consensus.py
```

### 3. Hardware Deployment
- Same algorithm code, swap communication layer
- Deploy to physical robots
- Minimal code changes required

### 4. Validation
- Compare emulated vs real trajectories
- Analyze communication latency impact
- Verify convergence properties

## Verification Checklist
- [ ] Algorithm works in emulated environment
- [ ] Communication latency model matches hardware
- [ ] Neighbor topology correctly configured
- [ ] Local control stability proven
- [ ] Network partition handling tested
- [ ] Scaling behavior validated (4 → N robots)

## Key Advantages
- **Rapid prototyping**: Test algorithms without hardware
- **Same code path**: Emulation → deployment with minimal changes
- **Scalable testing**: Emulate 100+ robots on single machine
- **Reproducible**: Deterministic emulation for debugging

## Related Methods
- See `distributed-quantum-control-systems` for distributed system patterns
- See `density-driven-multi-agent-control` for multi-agent coordination
- See `complex-valued-gnn-control` for graph-based control