Robotics Integration Capabilities¶

Overview¶

AIDDDMAP's robotics integration system provides comprehensive capabilities for controlling, monitoring, and managing robotic systems. The platform supports various robot types, sensor integration, motion planning, and real-time control.

Core Features¶

1. Robot Control¶

Motion Planning
Trajectory Generation
Inverse Kinematics
Force Control

2. Sensing Systems¶

Camera Integration
LiDAR Processing
Force/Torque Sensing
Environmental Sensors

Path Planning
SLAM Implementation
Obstacle Avoidance
Map Generation

4. Task Automation¶

Task Planning
Sequence Execution
Error Recovery
Safety Monitoring

Implementation¶

1. Robot Configuration¶

interface RobotConfig {
  type: RobotType;
  model: string;
  controllers: ControllerConfig[];
  sensors: SensorConfig[];
  safety: SafetyConfig;
}

interface ControllerConfig {
  type: ControllerType;
  parameters: ControlParameters;
  constraints: ControlConstraints;
}

enum RobotType {
  MANIPULATOR = "manipulator",
  MOBILE = "mobile",
  COLLABORATIVE = "collaborative",
  CUSTOM = "custom",
}

2. Motion Control¶

interface MotionController {
  planning: PlanningConfig;
  execution: ExecutionConfig;
  monitoring: MonitoringConfig;
}

interface PlanningConfig {
  algorithm: string;
  constraints: MotionConstraints;
  optimization: OptimizationParams;
}

interface ExecutionConfig {
  mode: "position" | "velocity" | "torque";
  control: ControlParams;
  safety: SafetyChecks;
}

3. Sensor Processing¶

interface SensorProcessor {
  sensors: SensorConfig[];
  fusion: FusionConfig;
  calibration: CalibrationConfig;
}

interface SensorConfig {
  type: SensorType;
  parameters: SensorParams;
  processing: ProcessingConfig;
}

interface FusionConfig {
  algorithm: string;
  inputs: string[];
  output: OutputConfig;
}

Robot Control¶

1. Motion Planning¶

interface MotionPlanner {
  algorithm: PlanningAlgorithm;
  constraints: PlanningConstraints;
  optimization: OptimizationConfig;
}

interface PlanningConstraints {
  joint: JointConstraints;
  cartesian: CartesianConstraints;
  dynamic: DynamicConstraints;
}

interface OptimizationConfig {
  objective: string;
  weights: number[];
  constraints: OptConstraints[];
}

2. Task Execution¶

interface TaskExecutor {
  sequence: TaskSequence;
  monitoring: MonitorConfig;
  recovery: RecoveryConfig;
}

interface TaskSequence {
  tasks: Task[];
  transitions: Transition[];
  validation: ValidationConfig;
}

Integration Examples¶

1. Robot Control System¶

// Configure robot controller
const robotController = new RobotController({
  robot: {
    type: "manipulator",
    model: "ur5e",
    dof: 6,
  },
  control: {
    mode: "position",
    rate: 125,
    interpolation: "cubic",
  },
  safety: {
    limits: {
      joint: {
        position: [-Math.PI, Math.PI],
        velocity: [-3.14, 3.14],
        acceleration: [-10, 10],
      },
      cartesian: {
        position: {
          x: [-1, 1],
          y: [-1, 1],
          z: [0, 2],
        },
        orientation: {
          rx: [-Math.PI, Math.PI],
          ry: [-Math.PI, Math.PI],
          rz: [-Math.PI, Math.PI],
        },
      },
    },
    collision: {
      enabled: true,
      margin: 0.1,
    },
  },
});

// Execute motion
const motion = await robotController.executeMotion({
  type: "joint",
  points: [
    { position: [0, -1.57, 1.57, -1.57, -1.57, 0] },
    { position: [1.57, -1.57, 1.57, -1.57, -1.57, 0] },
  ],
  parameters: {
    velocity: 0.5,
    acceleration: 0.5,
  },
});

2. Sensor Integration¶

// Configure sensor system
const sensorSystem = new SensorSystem({
  sensors: [
    {
      type: "camera",
      config: {
        resolution: [1920, 1080],
        fps: 30,
        format: "rgb",
      },
      processing: {
        calibration: true,
        undistortion: true,
      },
    },
    {
      type: "lidar",
      config: {
        channels: 16,
        range: 100,
        frequency: 10,
      },
      processing: {
        filtering: true,
        registration: true,
      },
    },
    {
      type: "force",
      config: {
        axes: 6,
        range: [-100, 100],
        rate: 1000,
      },
      processing: {
        filtering: true,
        calibration: true,
      },
    },
  ],
  fusion: {
    algorithm: "kalman",
    rate: 100,
    alignment: true,
  },
});

// Process sensor data
const sensorData = await sensorSystem.process({
  timestamp: Date.now(),
  requirements: {
    camera: true,
    lidar: true,
    force: true,
  },
});

// Configure navigation system
const navigator = new RobotNavigator({
  mapping: {
    type: "slam",
    algorithm: "rtab-map",
    resolution: 0.05,
  },
  planning: {
    global: {
      algorithm: "rrt*",
      optimization: true,
    },
    local: {
      algorithm: "dwa",
      frequency: 20,
    },
  },
  control: {
    type: "differential",
    parameters: {
      maxVelocity: 1.0,
      maxRotation: 1.0,
    },
  },
});

// Navigate to goal
const path = await navigator.planPath({
  start: currentPose,
  goal: targetPose,
  constraints: {
    clearance: 0.5,
    maxTime: 30,
  },
});

Performance Tuning and Optimization¶

interface PerformanceTuner {
  policies: TuningPolicy[];
  parameters: TuningParameter[];
  optimization: OptimizationConfig;
}

Resource Management and Allocation¶

interface ResourceManager {
  cpu: CPUConfig;
  memory: MemoryConfig;
  network: NetworkConfig;
}

System Performance Guidelines¶

Real-time Control
Optimize control loops
Manage resources efficiently
Monitor timing constraints
Handle errors gracefully
Resource Optimization
Implement proper limits
Monitor system state
Handle emergencies
Regular validation
Integration Best Practices
Proper calibration
Sensor fusion
Error handling
System monitoring

Best Practices¶

1. Safety¶

Implement proper limits
Monitor system state
Handle emergencies
Regular validation

2. Performance¶

Optimize control loops
Manage resources
Monitor timing
Handle errors

3. Integration¶

Proper calibration
Sensor fusion
Error handling
System monitoring

Future Enhancements¶

Planned Features
Advanced path planning
Learning from demonstration
Multi-robot coordination
Cloud robotics integration
Research Areas
Reinforcement learning
Adaptive control
Human-robot interaction
Swarm robotics

Swarm Coordination¶

Swarm Coordination Overview¶

The SwarmCoordinator class provides robust capabilities for managing multiple robots in formation, handling their movement, and coordinating swarm-wide tasks.

Key Features¶

Formation control with multiple shape options (line, triangle, square, custom)
Dynamic robot addition and removal
Encrypted communication between swarm members
Real-time position updates and state management

Code Examples¶

Moving Robots in Formation¶

// Initialize the swarm coordinator
const config: SwarmConfig = {
  teamSize: 3,
  communicationRange: 10,
  formationShape: "triangle",
  encryptionEnabled: true,
};
const coordinator = new SwarmCoordinator(rosManager, config);

// Add robots to the swarm
await coordinator.addRobot("robot1", {
  id: "robot1",
  position: { x: 0, y: 0, theta: 0 },
  velocity: { linear: 0, angular: 0 },
  batteryLevel: 100,
  status: "active",
});

// Update formation
await coordinator.updateFormation({
  center: { x: 0, y: 0 },
  orientation: 0,
  shape: "triangle",
  scale: 1.0,
});

Implementation Notes¶

Robot movement is handled through the moveRobotToPosition method which uses ROS navigation
Unused parameters in methods are prefixed with underscore (e.g., _robotId) following TypeScript best practices
Formation positions are calculated based on the selected shape and number of robots
Error handling includes validation of robot existence and movement constraints

Swarm Coordination Best Practices¶

Always initialize the swarm before adding robots
Check swarm state after critical operations
Handle cleanup properly when shutting down
Follow the naming conventions for unused variables