• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.2
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• pp.63-70
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• 2012

The collision-free flight planning method based on the extended collision map is proposed for cooperation of multiple unmanned aerial vehicles (UAVs) in a common 3-dimensional workspace. First, a UAV is modeled as a sphere, taking its 3-D motions such as rolling into consideration. We assume that after entering the common workspace, the UAVs move along their straight paths until they depart from the workspace, and that the priorities of the UAVs are determined in advance. According to the assumptions, the collision detection problem between two spheres in $R^3$ can be reduced into the collision detection problem between a circle and a line in $R^2$. For convenience' sake and safety, the collision regions are approximated by collision boxes. Using the collision boxes, the entrance times of the UAVs are scheduled for collision avoidance among the UAVs. By this way, all UAVs can move in the common workspace without collisions with one another. For verifying the effectiveness of the proposed flight planning method, the simulation with 12 UAVs is carried out.

• ETRI Journal
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• v.43 no.4
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• pp.580-593
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• 2021

When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.767-779
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• 2017

Automated mission planning for unmanned aerial vehicles (UAVs) is difficult because of the propagation of several sources of error into the solution, as for any large scale autonomous system. To ensure reliable system performance, we quantify all sources of error and their propagation through a mission planner for operation of UAVs in an obstacle rich environment we developed in prior work. In this sequel to that work, we show that the mission planner developed before can be made robust to errors arising from the mapping, sensing, actuation, and environmental disturbances through creating systematic buffers around obstacles using the calculations of uncertainty propagation. This robustness makes the mission planner truly autonomous and scalable to many UAVs without human intervention. We illustrate with simulation results for trajectory generation of multiple UAVs in a surveillance problem in an urban environment while optimizing for either maximal flight time or minimal fuel consumption. Our solution methods are suitable for any well-mapped region, and the final collision free paths are obtained through offline sub-optimal solution of an mTSP (multiple traveling salesman problem).