Kasper Rømer Grøntved

Phd-Candidate @ UAS Center, MMMI, University of Southern Denmark

9 minute read

The Final Piece: Intelligent Operation Management

You’ve mastered path planning, task coordination, communication, and testing. Now it’s time to bring it all together with Agent-DSL - a domain-specific language that defines intelligent, safety-critical behavior for your autonomous robot operations.

Why Agent-DSL?

The Challenge of Complex Operations

Real-world robot missions involve:

  • Safety constraints: Geofencing, emergency procedures, weather limits
  • Operational requirements: Battery management, communication protocols
  • Regulatory compliance: Airspace restrictions, flight permissions
  • Multi-agent coordination: Operation-level planning and monitoring

Traditional approaches require:

  • Hardcoded safety logic scattered across multiple files
  • Manual configuration for each mission
  • Complex integration between safety systems and mission planning
  • Error-prone parameter management

Agent-DSL Solution

Agent-DSL provides:

  • Declarative specification of agent behavior and constraints
  • Automatic code generation for ROS2 nodes and safety systems
  • Centralized safety management with emergency protocols
  • Operation-level coordination with multi-agent planning
  • PX4 integration for realistic simulation and deployment

Understanding the Agent-DSL Architecture

Core Components

Agent Definition → Code Generator → ROS2 System
     ↓               ↓              ↓
   Safety Rules → Python Classes → Safety Monitors
     ↓               ↓              ↓
  Operations → Message Types → Mission Coordinators
  1. Agent Specification: Declarative behavior definition
  2. Safety Constraints: Emergency and contingency handling
  3. Operation Planning: Multi-agent mission coordination
  4. Code Generation: Automatic ROS2 integration

Setting Up Agent-DSL

Prerequisites

# Install ROS2 Humble
sudo apt update
sudo apt install ros-humble-desktop

# Install Python dependencies
pip install textx jinja2 pyyaml

Installation

# Create ROS2 workspace
mkdir -p ~/workspace/mdsd_ws/src
cd ~/workspace/mdsd_ws/src

# Clone Agent-DSL repository
git clone <agent-dsl-repo>
cd mdsd-1

# Generate code for your first agent
./generate.zsh quad0

# Build workspace
cd ../..
colcon build --symlink-install
source install/setup.bash

Your First Agent Definition

Basic Agent Structure

Create agents/quad0.agent:

import simulation [id 0 at 0,0 with battery simulation enabled]
import airspace [EKOD,EKCP,EKBI]

volumes: 
  flight_geometry: [[10.34261,55.48038],[10.32339,55.47399],[10.32560,55.47156]]
  contingency_volume: [[10.38002,55.47930],[10.36054,55.51121],[10.27119,55.48253]]
  air_risk_buffer: [[10.25000,55.38833],[10.14083,55.48833],[10.40000,55.57167]]
end volumes

agent quad0: multicopter with camera and lidar
  id: 0
  flight_time: 40 minutes

  beat:
    heartbeat every 1 second
    metabeat every 1 minutes
  end beat

  inform:
    notice every 10 seconds
    warning every 5 seconds
    alarm every 1 second
  end inform
end agent

emergency:
  volume: agent out of flight_geometry activate rtl
  component: battery level lower than 5 percent activate fts
  component: battery level lower than 15 percent activate land
end emergency

contingency:
  volume: aircraft within air_risk_buffer activate rtl
  component: battery level lower than 30 percent activate rtl
  component: battery level lower than 40 percent activate warning
  component: c2 package_loss higher than 15 percent activate warning
end contingency

Understanding the Syntax

Import Statements:

  • simulation: Enables simulation mode with battery modeling
  • airspace: Defines allowed airspace zones

Volumes: Geographic boundaries for operations

  • flight_geometry: Normal operating area
  • contingency_volume: Extended operational area
  • air_risk_buffer: Safety buffer zone

Agent Configuration:

  • Platform type (multicopter) and sensors (camera, lidar)
  • Flight duration and operational parameters
  • Communication heartbeat intervals

Safety Systems:

  • emergency: Critical situations requiring immediate action
  • contingency: Degraded conditions requiring modified behavior

Safety-First Design Philosophy

Emergency Protocols

Agent-DSL implements a hierarchical safety system:

Emergency (Critical) → Immediate Action (RTL, FTS, Land)
    ↓
Contingency (Degraded) → Modified Operation (Warning, RTL)
    ↓
Normal Operations → Standard Mission Execution

Example Emergency Scenarios

emergency:
  // Geographic constraints
  volume: agent out of flight_geometry activate rtl
  
  // System health
  component: battery level lower than 5 percent activate fts
  component: gps accuracy worse than 5 meters activate land
  component: motor temperature higher than 85 celsius activate land
  
  // Communication
  component: c2 package_loss higher than 90 percent activate fts
  
  // Weather
  environment: wind speed higher than 15 mps activate rtl
end emergency

Contingency Management

contingency:
  // Preemptive actions
  component: battery level lower than 30 percent activate rtl
  component: c2 package_loss higher than 15 percent activate warning
  
  // Performance degradation
  component: mission progress lower than 50 percent activate replanning
  
  // Environmental factors
  environment: visibility lower than 100 meters activate slow_mode
end contingency

Multi-Agent Operation Planning

Operation Definition

Create operations/search_rescue.op:

bounding_area = POLYGON([10.325,55.472],[10.324,55.473],[10.322,55.472])
search_area = POLYGON([10.323,55.472],[10.324,55.472],[10.325,55.472])

Operation WildernessSearchAndRescue using 3 uavs within bounding_area:
    monitor search_area priority high
    monitor search_perimeter priority medium
    establish communication_relay at center
    maintain minimum_separation 50 meters
end

Advanced Operations

Operation CoordinatedInspection using 5 uavs within inspection_zone:
    // Sequential tasks
    sequence:
        survey area_north with 2 uavs
        wait_for completion
        survey area_south with 3 uavs
    end sequence
    
    // Parallel tasks  
    parallel:
        establish overwatch at high_altitude with 1 uav
        perform detailed_scan with remaining uavs
    end parallel
    
    // Conditional logic
    if weather_degraded:
        reduce team_size to 3
        increase separation to 100 meters
    end if
end

Code Generation and Integration

Generated Artifacts

When you run ./generate.zsh quad0, Agent-DSL creates:

generated/
├── ros2_packages/
│   ├── quad0_interfaces/          # Custom message types
│   ├── quad0_safety/             # Safety monitoring nodes  
│   ├── quad0_coordination/       # Mission coordination
│   └── quad0_launch/             # Launch files
├── python_classes/
│   ├── Agent.py                  # Agent behavior class
│   ├── SafetyMonitor.py         # Safety system
│   └── OperationManager.py      # Operation coordination
└── config/
    ├── safety_params.yaml       # Safety thresholds
    ├── mission_config.yaml      # Mission parameters
    └── px4_config.yaml          # PX4 integration

ROS2 Integration

The generated system creates these ROS2 topics:

# Safety monitoring
/quad0/safety/battery_status
/quad0/safety/position_status  
/quad0/safety/communication_status

# Mission coordination
/operation/task_allocation
/operation/agent_status
/operation/emergency_broadcast

# Agent communication
/quad0/heartbeat
/quad0/mission_status
/quad0/sensor_data

Launching Your Agent System

Single Agent Launch

# Launch the complete agent system
ros2 launch mdsd_ros_pkg quad0_launch.py

# Or launch components individually:
ros2 run quad0_safety safety_monitor
ros2 run quad0_coordination mission_coordinator  
ros2 run quad0_interfaces heartbeat_publisher

Multi-Agent Operation

# Launch operation planner
ros2 run operation_planning planner_node.py

# Launch individual agents
ros2 launch mdsd_ros_pkg quad0_launch.py &
ros2 launch mdsd_ros_pkg quad1_launch.py &
ros2 launch mdsd_ros_pkg quad2_launch.py &

# Monitor operation status
ros2 topic echo /operation/status

PX4 Simulation Integration

Setting Up PX4

# Clone and setup PX4 (if not already done)
cd ~/
git clone https://github.com/PX4/PX4-Autopilot.git --recursive
bash ./PX4-Autopilot/Tools/setup/ubuntu.sh

# Add to ~/.bashrc
export PX4_DIR=~/PX4-Autopilot

# Build simulation
cd $PX4_DIR
HEADLESS=1 make px4_sitl gz_x500

Agent-DSL PX4 Integration

# Launch PX4 simulation with Agent-DSL
ros2 launch quad0_simulation px4_agent_sim.launch.py

# This automatically:
# 1. Starts PX4 SITL simulation
# 2. Spawns the agent in Gazebo
# 3. Launches Agent-DSL safety systems
# 4. Connects to mission planning

Advanced Agent Behaviors

Adaptive Mission Planning

agent adaptive_searcher: multicopter with thermal_camera
  id: 1
  
  behavior:
    // Dynamic replanning based on findings
    on victim_detected:
      broadcast location to team
      request additional_resources
      expand search_radius by 200 meters
    end on
    
    // Adaptive search patterns
    if probability_map_updated:
      recalculate optimal_path
      coordinate with team_members
    end if
  end behavior
end agent

Environmental Adaptation

agent weather_adaptive: multicopter with camera and lidar
  id: 2
  
  environmental_response:
    wind_speed > 10 mps: reduce_altitude to 30 meters
    visibility < 100 meters: activate_obstacle_avoidance_mode
    temperature < -10 celsius: enable_cold_weather_mode
    precipitation_detected: return_to_base unless critical_mission
  end environmental_response
end agent

Team Coordination Behaviors

agent team_leader: multicopter with communication_relay
  id: 0
  role: coordinator
  
  team_management:
    monitor all_team_members every 5 seconds
    
    if member_offline > 30 seconds:
      redistribute_tasks among remaining_members
      notify_human_operator
    end if
    
    if mission_efficiency < 70 percent:
      trigger_replanning
      adjust_team_formation
    end if
  end team_management
end agent

Safety System Deep Dive

Battery Management

battery_management:
  // Critical levels
  emergency_level: 5 percent
  return_home_level: 15 percent
  conservative_mode_level: 30 percent
  
  // Adaptive thresholds based on mission
  if distance_to_base > 2 kilometers:
    increase return_home_level to 25 percent
  end if
  
  // Weather considerations  
  if wind_speed > 8 mps:
    increase all_levels by 10 percent
  end if
end battery_management

Communication Monitoring

communication_health:
  heartbeat_timeout: 5 seconds
  data_loss_threshold: 15 percent
  signal_strength_minimum: -80 dBm
  
  degradation_response:
    packet_loss > 10 percent: switch_to_low_bandwidth_mode
    signal_strength < -85 dBm: move_toward_base_station
    heartbeat_lost: activate_emergency_protocol
  end degradation_response
end communication_health

Geofencing Integration

geofencing:
  // Multiple fence types
  hard_fence: flight_geometry  // Never cross
  soft_fence: contingency_volume  // Warning zone
  
  violation_response:
    hard_fence_breach: immediate_rtl
    soft_fence_approach: reduce_speed and alert_operator
    
    // Adaptive fencing
    if weather_deteriorating:
      shrink_soft_fence by 20 percent
    end if
  end violation_response
end geofencing

Integration with Previous Tools

Complete System Integration

# Example integration with your full robot stack
class IntegratedMissionSystem:
    def __init__(self):
        # Agent-DSL safety and coordination
        self.agent_dsl = AgentDSLInterface()
        
        # SwarmTalk communication
        self.communication = SwarmTalkManager()
        
        # TrajAllocPy task allocation
        self.task_allocator = TaskAllocationManager()
        
        # TrajGenPy path planning
        self.path_planner = PathPlanningManager()
        
        # SARenv testing framework
        self.evaluator = SARenvEvaluator()
        
    def execute_mission(self, mission_definition):
        # 1. Parse mission using Agent-DSL
        mission_plan = self.agent_dsl.parse_mission(mission_definition)
        
        # 2. Generate tasks using TrajGenPy
        coverage_tasks = self.path_planner.generate_coverage_tasks(mission_plan.area)
        
        # 3. Allocate tasks using TrajAllocPy
        task_allocation = self.task_allocator.allocate_tasks(coverage_tasks)
        
        # 4. Monitor execution with safety systems
        safety_monitor = self.agent_dsl.create_safety_monitor()
        
        # 5. Execute with SwarmTalk coordination
        return self.communication.execute_coordinated_mission(
            task_allocation, safety_monitor
        )

Configuration Management

# Complete system configuration
integrated_mission:
  # Agent-DSL settings
  safety:
    emergency_protocols: enabled
    geofencing: strict
    communication_monitoring: enabled
    
  # TrajGenPy settings  
  path_planning:
    coverage_type: boustrophedon
    overlap_percentage: 20
    altitude: 50
    
  # TrajAllocPy settings
  task_allocation:
    algorithm: cbba
    max_iterations: 100
    convergence_threshold: 0.01
    
  # SwarmTalk settings
  communication:
    heartbeat_interval: 1.0
    message_retry_count: 3
    network_timeout: 5.0
    
  # SARenv settings
  evaluation:
    scenario_size: medium
    victim_count: 2
    terrain_type: mountainous

Monitoring and Debugging

Real-time System Monitoring

# Monitor all safety systems
ros2 topic echo /system/safety_status

# Monitor mission progress
ros2 topic echo /operation/mission_progress

# Monitor communication health
ros2 topic echo /swarmtalk/network_status

# Monitor task allocation
ros2 topic echo /task_allocation/assignments

Debugging Agent Behavior

# Enable debug mode in Agent-DSL
ros2 param set /quad0/safety_monitor debug_mode true

# View generated safety rules
cat generated/config/safety_params.yaml

# Monitor safety state transitions
ros2 topic echo /quad0/safety/state_transitions

Best Practices for Agent-DSL

1. Safety-First Development

  • Always define emergency protocols before normal operations
  • Test safety systems in simulation extensively
  • Use conservative thresholds initially, tune based on experience
  • Implement graceful degradation for all failure modes

2. Modular Operation Design

// Break complex operations into modular components
Operation ComplexMission:
    include StandardSafetyProtocols
    include CommunicationManagement
    include TaskCoordination
    
    // Mission-specific logic
    sequence:
        call PreflightChecks
        call AreaSurvey  
        call DataCollection
        call PostflightProcedures
    end sequence
end

3. Testing Strategy

# 1. Unit test individual components
ros2 test quad0_safety

# 2. Integration testing  
ros2 launch test_integration.launch.py

# 3. Simulation validation
ros2 launch px4_simulation_test.launch.py

# 4. SARenv evaluation
python run_sarenv_evaluation.py --agent-config quad0.agent

4. Documentation and Version Control

  • Version control all agent definitions
  • Document safety rationale for each constraint
  • Maintain change logs for safety-critical modifications
  • Peer review all safety system changes

Deployment Checklist

Before deploying Agent-DSL systems:

Pre-flight Verification

# 1. Verify code generation
./generate.zsh quad0
colcon build --symlink-install

# 2. Check safety configurations
ros2 param dump /quad0/safety_monitor > safety_verification.yaml

# 3. Test emergency procedures
ros2 service call /quad0/test_emergency_rtl

# 4. Validate geofencing
ros2 service call /quad0/test_geofence_boundary

# 5. Communication check
ros2 topic pub /test_communication std_msgs/String "data: 'test'"

Field Deployment

  1. Start with reduced operational area
  2. Test all emergency procedures
  3. Validate communication range
  4. Monitor safety system performance
  5. Gradually expand operational envelope

Next Steps and Community

Advanced Topics

  • Custom safety constraints for specialized applications
  • Multi-domain operations (air, ground, marine)
  • Integration with external systems (air traffic control, weather services)
  • Machine learning integration for adaptive behavior

Contributing to Agent-DSL

  • Extend the grammar for new agent types
  • Add safety constraint templates for common scenarios
  • Improve code generation efficiency
  • Contribute simulation scenarios

Getting Help

  • Documentation: Check the Agent-DSL wiki
  • Issues: Report bugs and feature requests on GitHub
  • Community: Join the Agent-DSL discussion forum
  • Support: Contact the development team

Conclusion: Your Complete Robot System

Congratulations! You now have the complete toolkit for building sophisticated, safe, and coordinated robot teams:

  1. TrajGenPy: Efficient coverage path planning
  2. TrajAllocPy: Intelligent multi-robot task allocation
  3. SwarmTalk: Reliable robot-to-robot communication
  4. SARenv: Comprehensive testing and evaluation
  5. Agent-DSL: Safe and intelligent operation management

This powerful combination enables you to tackle real-world robotics challenges with confidence, safety, and efficiency.

Ready to deploy your intelligent robot team? Start with simple missions and gradually build complexity as you gain experience with each component. The future of autonomous robotics is in your hands!

Updated:

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