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

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

# 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

# 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

# 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