activity
20192026
most citedIntegrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Multi-Agent Transportation of Free-Flyers in Microgravity Via Pushing Interaction Under Human-in-the-Loop Control

Gregorio Marchesini, Nicola De Carli, Sihyun Cho +5

We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser robots acts through unilateral pushing contacts…

cs.RO2024

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

Yunwoo Lee, Jungwon Park, H. Jin Kim

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-V…

cs.RO2024

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

Yunwoo Lee, Jungwon Park, Boseong Jeon +2

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…

cs.RO2019★ 3 cited

Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

Boseong Jeon, H. Jin Kim

This letter suggests an integrated approach for a drone (or multirotor) to perform an autonomous videography task in a 3-D obstacle environment by following a moving object. The pr…

eess.SY2019

Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor

Jungwon Park, H. Jin Kim

This paper presents a new trajectory planning method for multiple quadrotors in obstacle-dense environments. We suggest a relative safe flight corridor (RSFC) to model safe region…

eess.SY2019★ 1 cited

Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomial

Jungwon Park, Junha Kim, Inkyu Jang +1

This paper presents a new efficient algorithm which guarantees a solution for a class of multi-agent trajectory planning problems in obstacle-dense environments. Our algorithm comb…