Under Review

SCOPE: Field-of-View-Aware Path Planning in Unknown 3D Environments via Safety-Volume Certification.
```
@misc{junbinyuan_tro,
  title = {SCOPE: Field-of-View-Aware Path Planning in Unknown 3D Environments via Safety-Volume Certification}
}
```
```
, "SCOPE: Field-of-View-Aware Path Planning in Unknown 3D Environments via Safety-Volume Certification," , .
```
Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter.
```
@misc{muqingcao_ral,
  title = {Multi-robot Learning-based Informative Path Planning Using
  Spatio-Temporal Gaussian Process Kalman Filter}
}
```
```
, "Multi-robot Learning-based Informative Path Planning Using
Spatio-Temporal Gaussian Process Kalman Filter," , .
```
MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm.
```
@misc{leeundermcswarm,
  title = {MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm}
}
```
```
, "MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm," , .
```
GAR-Tracker: Target-Visible Trajectory Planning for Ground Assistant Robots.
```
@misc{leeundergartracker,
  title = {GAR-Tracker: Target-Visible Trajectory Planning for Ground Assistant Robots}
}
```
```
, "GAR-Tracker: Target-Visible Trajectory Planning for Ground Assistant Robots," , .
```

2025

QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking.

By Lee, Y., Park, J., Jung, S., Jeon, B., Oh, D. and Kim, H.J.

In IEEE Transactions on Automation Science and Engineering, 2025.

🏆 2025 T-ASE Best New Application Paper Award

@article{lee2025qpchaser,
  title = {QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking},
  author = {Lee, Yunwoo and Park, Jungwon and Jung, Seungwoo and Jeon, Boseong and Oh, D. and Kim, H. Jin},
  year = {2025},
  journal = {IEEE Transactions on Automation Science and Engineering},
  note = {2025 T-ASE Best New Application Paper Award}
}

Maintaining the visibility of the target is one of the major objectives of aerial tracking missions. This paper proposes a target-visible trajectory planning pipeline using quadratic programming. Our approach can handle various tracking settings, including single and dual target following and both static and dynamic environments, unlike other works that focus on a single specific setup. In contrast to other studies that fully trust the predicted trajectory of the target and consider only the visibility of the center of the target, our pipeline considers error in target path prediction and the entire body of the target to maintain the target visibility robustly. First, a prediction module uses a sample-check strategy to quickly calculate the reachable areas of moving objects, which represent the areas their bodies can reach, considering obstacles. Subsequently, the planning module formulates a single QP problem, considering path homotopy, to generate a tracking trajectory that maximizes the visibility of the target’s reachable area among obstacles. The performance of the planner is validated in multiple scenarios, through high-fidelity simulations and real-world experiments.

Lee, Yunwoo and Park, Jungwon and Jung, Seungwoo and Jeon, Boseong and Oh, D. and Kim, H. Jin, "QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking," IEEE Transactions on Automation Science and Engineering2025 T-ASE Best New Application Paper Award, 2025.

DMVC-Tracker: Distributed Multi-Agent Trajectory Planning for Target Tracking Using Dynamic Buffered Voronoi and Inter-Visibility Cells.

By Lee, Y., Park, J. and Kim, H.J.

In IEEE Robotics and Automation Letters, 2025.

@article{lee2025dmvctracker,
  title = {DMVC-Tracker: Distributed Multi-Agent Trajectory Planning for Target Tracking Using Dynamic Buffered Voronoi and Inter-Visibility Cells},
  author = {Lee, Yunwoo and Park, Jungwon and Kim, H. Jin},
  year = {2025},
  journal = {IEEE Robotics and Automation Letters}
}

This letter presents a distributed trajectory planning method for multi-agent aerial tracking. The proposed method uses a Dynamic Buffered Voronoi Cell (DBVC) and a Dynamic Inter-Visibility Cell (DIVC) to formulate the distributed trajectory generation. Specifically, the DBVC and the DIVC are time-variant spaces that prevent mutual collisions and occlusions among agents, while enabling them to maintain suitable distances from the moving target. We combine the DBVC and the DIVC with an efficient Bernstein polynomial motion primitive-based tracking generation method, which has been refined into a less conservative approach than in our previous work. The proposed algorithm can compute each agent’s trajectory within several milliseconds on an Intel i7 desktop. We validate the tracking performance in challenging scenarios, including environments with dozens of obstacles.

Lee, Yunwoo and Park, Jungwon and Kim, H. Jin, "DMVC-Tracker: Distributed Multi-Agent Trajectory Planning for Target Tracking Using Dynamic Buffered Voronoi and Inter-Visibility Cells," IEEE Robotics and Automation Letters, 2025.

Decentralized Trajectory Planning for Quadrotor Swarm in Cluttered Environments with Goal Convergence Guarantee.

By Park, J., Lee, Y., Jang, I. and Kim, H.J.

In The International Journal of Robotics Research, 2025.

@article{park2025decentralized,
  title = {Decentralized Trajectory Planning for Quadrotor Swarm in Cluttered Environments with Goal Convergence Guarantee},
  author = {Park, Jungwon and Lee, Yunwoo and Jang, Inkyu and Kim, H. Jin},
  year = {2025},
  journal = {The International Journal of Robotics Research}
}

Decentralized multi-agent trajectory planning (MATP) can enhance the efficiency of multi-robot systems thanks to high scalability and short computation time. However, it may lead to deadlock or livelock in obstacle-rich environments. To tackle this challenge, this paper presents a decentralized MATP algorithm for a quadrotor swarm that ensures convergence to a goal in maze-like environments. The proposed method guides the agents to their goal using the waypoints generated by a grid-based multi-agent path planning (MAPF) algorithm. Additionally, we introduce subgoal optimization to prevent deadlock while the agents follow the waypoints. The proposed algorithm guarantees that the agents converge to their goal if there is no dynamic obstacle and the agents are connected through a fully connected network. Moreover, it ensures deadlock-free even when the agent has a limited communication range. For dynamic obstacle avoidance, we revise the grid-based MAPF to prioritize collision avoidance when the agents encounter dynamic obstacles in a narrow corridor. In simulation, the proposed algorithm achieves a 100% success rate in static environments and shows a higher success rate and shorter flight time compared to most state-of-the-art baseline algorithms in dynamic environments. We validate the safety and robustness of the proposed work through the experiment with 10 quadrotors and one pedestrian in a maze-like environment.

Park, Jungwon and Lee, Yunwoo and Jang, Inkyu and Kim, H. Jin, "Decentralized Trajectory Planning for Quadrotor Swarm in Cluttered Environments with Goal Convergence Guarantee," The International Journal of Robotics Research, 2025.

2024

BPMP-Tracker: A Versatile Aerial Target Tracker Using Bernstein Polynomial Motion Primitives.

By Lee, Y., Park, J., Jeon, B., Jung, S. and Kim, H.J.

In IEEE Robotics and Automation Letters, 2024.

@article{lee2024bpmptracker,
  title = {BPMP-Tracker: A Versatile Aerial Target Tracker Using Bernstein Polynomial Motion Primitives},
  author = {Lee, Yunwoo and Park, Jungwon and Jeon, Boseong and Jung, Seungwoo and Kim, H. Jin},
  year = {2024},
  journal = {IEEE Robotics and Automation Letters}
}

This letter presents a versatile trajectory planning pipeline for aerial tracking. The proposed tracker is capable of handling various chasing settings such as complex unstructured environments, crowded dynamic obstacles and multiple-target following. Among the entire pipeline, we focus on developing a predictor for future target motion and a chasing trajectory planner. For rapid computation, we employ the sample-check-select strategy: modules sample a set of candidate movements, check multiple constraints, and then select the best trajectory. Also, we leverage the properties of Bernstein polynomials for quick calculations. The prediction module predicts the trajectories of the targets, which do not overlap with static and dynamic obstacles. Then the trajectory planner outputs a trajectory, ensuring various conditions such as occlusion and collision avoidance, the visibility of all targets within a camera image and dynamical limits. We fully test the proposed tracker in simulations and hardware experiments under challenging scenarios, including dual-target following, environments with dozens of dynamic obstacles and complex indoor and outdoor spaces.

Lee, Yunwoo and Park, Jungwon and Jeon, Boseong and Jung, Seungwoo and Kim, H. Jin, "BPMP-Tracker: A Versatile Aerial Target Tracker Using Bernstein Polynomial Motion Primitives," IEEE Robotics and Automation Letters, 2024.

Mono-Camera-Only Target Chasing for a Drone in a Dense Environment by Cross-Modal Learning.

By Yoo, S., Jung, S., Lee, Y., Shim, D. and Kim, H.J.

In IEEE Robotics and Automation Letters, 2024.

@article{yoo2024monocamera,
  title = {Mono-Camera-Only Target Chasing for a Drone in a Dense Environment by Cross-Modal Learning},
  author = {Yoo, S. and Jung, Seungwoo and Lee, Yunwoo and Shim, D. and Kim, H. Jin},
  year = {2024},
  journal = {IEEE Robotics and Automation Letters}
}

Chasing a dynamic target in a dense environment is one of the challenging applications of autonomous drones. The task requires multi-modal data, such as RGB and depth, to accomplish safe and robust maneuver. However, using different types of modalities can be difficult due to the limited capacity of drones in aspects of hardware complexity and sensor cost. Our framework resolves such restrictions in the target chasing task by using only a monocular camera instead of multiple sensor inputs. From an RGB input, the perception module can extract a cross-modal representation containing information from multiple data modalities. To learn cross-modal representations at training time, we employ variational autoencoder (VAE) structures and the joint objective function across heterogeneous data. Subsequently, using latent vectors acquired from the pre-trained perception module, the planning module generates a proper next-time-step waypoint by imitation learning of the expert, which performs a numerical optimization using the privileged RGB-D data. Furthermore, the planning module considers temporal information of the target to improve tracking performance through consecutive cross-modal representations. Ultimately, we demonstrate the effectiveness of our framework through the reconstruction results of the perception module, the target chasing performance of the planning module, and the zero-shot sim-to-real deployment of a drone.

Yoo, S. and Jung, Seungwoo and Lee, Yunwoo and Shim, D. and Kim, H. Jin, "Mono-Camera-Only Target Chasing for a Drone in a Dense Environment by Cross-Modal Learning," IEEE Robotics and Automation Letters, 2024.

2023

DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor.

By Park, J., Lee, Y., Jang, I. and Kim, H.J.

In IEEE Transactions on Robotics, 2023.

@article{park2023dlsc,
  title = {DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor},
  author = {Park, Jungwon and Lee, Yunwoo and Jang, Inkyu and Kim, H. Jin},
  year = {2023},
  journal = {IEEE Transactions on Robotics}
}

This article presents an online distributed trajectory planning algorithm for a quadrotor swarm in a maze-like dynamic environment. We utilize a dynamic linear safe corridor to construct the feasible collision constraints that can ensure interagent collision avoidance and consider the uncertainty of moving obstacles. We introduce mode-based subgoal planning to resolve deadlock faster in a complex environment using only previously shared information. For dynamic obstacle avoidance, we adopt heuristic methods such as collision alert propagation and escape point planning to deal with the situation where dynamic obstacles approach the agents clustered in a narrow corridor. We prove that the proposed algorithm guarantees the feasibility of the optimization problem for every replanning step. In an obstacle-free space, the proposed method can compute the trajectories for 60 agents on average 7.66 ms per agent with an Intel i7 laptop and shows the perfect success rate. Also, our method shows 64.5% shorter flight time than buffered Voronoi cell and 34.6% shorter than with our previous work. We conduct the simulation in a random forest and maze with four dynamic obstacles, and the proposed algorithm shows the highest success rate and shortest flight time compared to state-of-the-art baseline algorithms. In particular, the proposed algorithm shows over 97% success rate when the velocity of moving obstacles is below the agent’s maximum speed. We validate the safety and robustness of the proposed algorithm through a hardware demonstration with ten quadrotors and two pedestrians in a maze-like environment.

Park, Jungwon and Lee, Yunwoo and Jang, Inkyu and Kim, H. Jin, "DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor," IEEE Transactions on Robotics, 2023.

2021

Autonomous Aerial Dual-Target Following Among Obstacles.
By Jeon, B.F., Lee, Y., Choi, J., Park, J. and Kim, H.J.
In IEEE Access, 2021.
```
@article{jeon2021dualtarget,
  title = {Autonomous Aerial Dual-Target Following Among Obstacles},
  author = {Jeon, Boseong Felipe and Lee, Yunwoo and Choi, Jeongjun and Park, Jungwon and Kim, H. Jin},
  year = {2021},
  journal = {IEEE Access}
}
```
```
Jeon, Boseong Felipe and Lee, Yunwoo and Choi, Jeongjun and Park, Jungwon and Kim, H. Jin, "Autonomous Aerial Dual-Target Following Among Obstacles," IEEE Access, 2021.
```
Multirobot Collaborative Monocular SLAM Utilizing Rendezvous.
By Jang, Y., Oh, C., Lee, Y. and Kim, H.J.
In IEEE Transactions on Robotics, 2021.
🏆 27th Samsung Human-Tech Paper Award, Silver Prize
```
@article{jang2021slam,
  title = {Multirobot Collaborative Monocular SLAM Utilizing Rendezvous},
  author = {Jang, Y. and Oh, C. and Lee, Yunwoo and Kim, H. Jin},
  year = {2021},
  journal = {IEEE Transactions on Robotics},
  note = {27th Samsung Human-Tech Paper Award, Silver Prize}
}
```
```
Jang, Y. and Oh, C. and Lee, Yunwoo and Kim, H. Jin, "Multirobot Collaborative Monocular SLAM Utilizing Rendezvous," IEEE Transactions on Robotics27th Samsung Human-Tech Paper Award, Silver Prize, 2021.
```