UAV + UGV Cooperative Control Simulation with ROS | Distributed Consensus, Hybrid Control, Semantic Segmentation Map
Budget: $30 – $250 USD
UAV, UGV, ROS, Gazebo, Distributed Consensus, Hybrid Control, Semantic Segmentation, Cooperative Tracking, Obstacle Avoidance
1. ROS
2. Gazebo / Simulation （ Gazebo Simulation Robotics Simulation）
3. Robotics （UAV、UGV）
4. Python
5. C++ Programming （ROS ）
6. Computer Vision
7. Deep Learning
8. Control Engineering / Control System Design
9. Matlab/Simulink
10. LaTeX / Technical Writing
We are looking for an experienced ROS / Gazebo / multi-agent robotics developer or researcher to implement a UAV + UGV hybrid cooperative control system simulation.
The project involves modeling, ROS-based simulation, distributed consensus algorithms, hybrid centralized-distributed control, semantic segmentation map integration, cooperative tracking, and obstacle avoidance.
You should be comfortable with:
• ROS / Gazebo / UE simulation (multi-agent UAV & UGV)
• Distributed consensus algorithms and information fusion
• Hybrid control strategies (centralized planning + distributed local obstacle avoidance)
• Semantic segmentation map integration for UAV-UGV cooperation
• Data visualization (trajectory plots, error curves, convergence analysis, radar charts, heat maps, etc.)
• Writing experimental reports (10,000+ words) with matched figures and text
This is a research-level task, not a toy project. Packaged runnable source code + detailed written report are required. Strong background in robotics, control theory, ROS simulation, and multi-agent systems is preferred.
At the end of this post, please find the full client requirements for deliverables, experiments, and deadlines.
Client Requirements ：
1. Overall Goal
• Complete a UAV (unmanned aerial vehicle) + UGV (unmanned ground vehicle) hybrid cooperative control system experiment, including modeling, experimental verification, and result analysis.
• The final deliverable should be a written experimental report of no less than 10,000 words, with matched figures and text.
2. Core Experimental Content
• Experiment 1: Cooperative Consistency Verification
UAV performs global detection, UGV performs local tracking, verifying the convergence of the distributed consensus algorithm and the effectiveness of the information fusion mechanism.
Expected outputs include: trajectory consistency plots, error curves, convergence analysis plots, heat maps, etc.
• Experiment 2: Hybrid Control Strategy Verification (Centralized + Distributed)
In complex environments, verify the task switching and cooperative performance of UAV and UGV under centralized global optimal planning and distributed local obstacle avoidance.
Expected outputs include: obstacle avoidance trajectory plots, task completion time comparison, cooperation error analysis, performance radar charts, mode switching timelines, etc.
• Additional Experiment
UAV first collects a global drivable-area map with semantic segmentation and sends it to UGV.
UGV moves within the drivable area, and if new unmarked obstacles are found, it feeds back to UAV to update the map.
This part only requires experimental design and result comparison figures, no text description needed. Reference code repository: KumarRobotics/lang-air-ground-teaming.
(Example reference figures already provided by the client, such as UAV Semantic Segmentation image.)
3. Deliverables
• Packaged source code files (runnable)
• Submit an experimental report (≥10,000 words),
including theoretical derivation + experimental verification, with both figures and text, and experimental result analysis.
• Figures and text must correspond, and experimental results must be visualized (trajectory plots, error curves, performance comparison charts, etc.).
• Deadline: deliver before September 18, 2025.
1. ROS
2. Gazebo / Simulation （ Gazebo Simulation Robotics Simulation）
3. Robotics （UAV、UGV）
4. Python
5. C++ Programming （ROS ）
6. Computer Vision
7. Deep Learning
8. Control Engineering / Control System Design
9. Matlab/Simulink
10. LaTeX / Technical Writing
We are looking for an experienced ROS / Gazebo / multi-agent robotics developer or researcher to implement a UAV + UGV hybrid cooperative control system simulation.
The project involves modeling, ROS-based simulation, distributed consensus algorithms, hybrid centralized-distributed control, semantic segmentation map integration, cooperative tracking, and obstacle avoidance.
You should be comfortable with:
• ROS / Gazebo / UE simulation (multi-agent UAV & UGV)
• Distributed consensus algorithms and information fusion
• Hybrid control strategies (centralized planning + distributed local obstacle avoidance)
• Semantic segmentation map integration for UAV-UGV cooperation
• Data visualization (trajectory plots, error curves, convergence analysis, radar charts, heat maps, etc.)
• Writing experimental reports (10,000+ words) with matched figures and text
This is a research-level task, not a toy project. Packaged runnable source code + detailed written report are required. Strong background in robotics, control theory, ROS simulation, and multi-agent systems is preferred.
At the end of this post, please find the full client requirements for deliverables, experiments, and deadlines.
Client Requirements ：
1. Overall Goal
• Complete a UAV (unmanned aerial vehicle) + UGV (unmanned ground vehicle) hybrid cooperative control system experiment, including modeling, experimental verification, and result analysis.
• The final deliverable should be a written experimental report of no less than 10,000 words, with matched figures and text.
2. Core Experimental Content
• Experiment 1: Cooperative Consistency Verification
UAV performs global detection, UGV performs local tracking, verifying the convergence of the distributed consensus algorithm and the effectiveness of the information fusion mechanism.
Expected outputs include: trajectory consistency plots, error curves, convergence analysis plots, heat maps, etc.
• Experiment 2: Hybrid Control Strategy Verification (Centralized + Distributed)
In complex environments, verify the task switching and cooperative performance of UAV and UGV under centralized global optimal planning and distributed local obstacle avoidance.
Expected outputs include: obstacle avoidance trajectory plots, task completion time comparison, cooperation error analysis, performance radar charts, mode switching timelines, etc.
• Additional Experiment
UAV first collects a global drivable-area map with semantic segmentation and sends it to UGV.
UGV moves within the drivable area, and if new unmarked obstacles are found, it feeds back to UAV to update the map.
This part only requires experimental design and result comparison figures, no text description needed. Reference code repository: KumarRobotics/lang-air-ground-teaming.
(Example reference figures already provided by the client, such as UAV Semantic Segmentation image.)
3. Deliverables
• Packaged source code files (runnable)
• Submit an experimental report (≥10,000 words),
including theoretical derivation + experimental verification, with both figures and text, and experimental result analysis.
• Figures and text must correspond, and experimental results must be visualized (trajectory plots, error curves, performance comparison charts, etc.).
• Deadline: deliver before September 18, 2025.