Gyeongtaek Oh

Fully Autonomous Vision-Based Net-Recovery Landing System for a Fixed-Wing UAV

This paper presents an autonomous vision-based netrecovery system for small fixed-wing unmanned aerial vehicles (UAVs). A fixed-wing UAV platform is constructed using various avionic sensors, and integrated with a flight control system and a vision system. The ground operation system consists of a vision station and ground control station that provide operation commands and monitor the UAV status. The vision algorithm to detect the recovery net and provide the bearing angle to the guidance algorithm is explained, along with the discussions on the techniques employed to improve the reliability of visual detection. The system identification process and controller are described, which enables to track given waypoints and to approach the detected net under the pursuit guidance law. Experimental results show the autonomous capabilities including take-off, waypoint following, and vision-based net recovery. The proposed technique can be an effective solution to recover fixed-wing UAVs without resorting to a complicated structure such as an instrumented landing system or expensive sensors such as a differential GPS.

Market-Based Decentralized Task Assignment for Cooperative UAV Mission Including Rendezvous

Rendezvous is a very important ability in military operation since the mission can be preformed effectively by the cooperative action. For autonomous rendezvous of Unmanned Aerial Vehicles (UAVs), task assignment and path planning algorithm are required. This paper proposes a market-based decentralized task assignment algorithm for cooperative UAV mission including rendezvous of multiple UAVs. The proposed algorithm is motivated by collaborator recruitment process composed of six intuitive phases. Then, convergence and the scalability is discussed. Also, the path planning is performed for the rendezvous which is well-matched with the proposed task assignment algorithm, which guarantees the simultaneous arrival at all times. Numerical simulation for two scenarios is performed to verify the effectiveness of the presented algorithm.

Market-Based Task Assignment for Cooperative Timing Missions in Dynamic Environments

New market-based decentralized algorithms are proposed for the task assignment of multiple unmanned aerial vehicles in dynamic environments with a limited communication range. In particular, a cooperative timing mission that cannot be performed by a single vehicle is considered. The baseline algorithms for a connected network are extended to deal with time-varying network topology including isolated subnetworks due to a limited communication range. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity, and numerical simulation results support the scalability of the proposed algorithm in terms of the runtime and communication burden. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for the scenario of the suppression of enemy air defenses.

Nonlinear Conflict Resolution and Flow Management Using Particle Swarm Optimization

A new optimization problem to resolve conflicts by allowing aircraft to change both their heading angle and speed is considered. The performance index in the optimization problem is formulated to reduce the variation of the aircraft arrival time that is caused by conflict resolution maneuvers. To achieve both conflict resolution and flow management, metering constraints are introduced together with separation constraints. Even though the performance index and constraints are nonlinear, the optimal solution can be easily obtained by utilizing particle swarm optimization. Numerical simulations are performed to evaluate the performance of the proposed algorithm, and the numerical experimental results showed a significant reduction in the variation of the aircraft arrival time as well as the magnitude of heading angle and speed changes.

Market-Based Task Assignment for Cooperative Timing Missions over Networks with Limited Connectivity

A new market-based decentralized algorithm is proposed for task assignment of multiple unmanned aerial vehicles in network environment with limited connectivity. In particular, the cooperative timing mission which cannot be performed by a single vehicle is considered. The proposed market mechanism consists of four intuitive phases to allocate coalitions with a greedy approach. Convergence and scalability analysis shows that the proposed algorithm has a polynomial-time complexity. The upper bound of the amount of communication is also derived. Numerical results support the scalability with respect to runtime and communication. The suppression of enemy air defense scenario is introduced as a numerical example to demonstrate the performance of the proposed algorithm.

Market-Based Distributed Task Assignment for Rendezvous Mission Over Networks with Limited Connectivity

Abstract This paper extends the market-based task assignment algorithm to handle the networks with limited connectivity. The original algorithm was proposed to allocate tasks which cannot be executed by a single agent, but fully connected network is assumed. After analyzing the existing approaches for task allocation in limited networks, a new communications protocol is proposed which preserve key features of the original algorithm. The data required for task allocation algorithm is relayed via bridge agents. By adopting relaying operation, the proposed strategy allocates tasks regardless of the network topology. Convergence property is also preserved. Numerical simulation is performed to verify the feasibility of the proposed strategy considering the communication load.

Task Allocation of Multiple UAVs for Cooperative Parcel Delivery

Modified sequential greedy algorithm is proposed to enhance the efficiency of task allocation for cooperative parcel delivery problem of multiple unmanned aerial vehicles (UAVs). The cooperative parcel delivery problem is formulated as an integer programming problem. In view of practical and commercial operation, multiple UAVs should build a team and carry together when the weight of the parcel is greater than the payload limit of a single UAV, and this makes the problem hard to solve. Numerical simulation is conducted to demonstrate the performance of the proposed algorithm comparing with the traditional sequential greedy algorithm.

Market-Based Distributed Task Assignment of Multiple Unmanned Aerial Vehicles for Cooperative Timing Mission

This paper introduces a new market-based distributed algorithm to solve a task assignment problem arising in missions using multiple unmanned aerial vehicles with timing coordination. The proposed ...

Trajectory Generation, Guidance, and Navigation for Terrain Following of Unmanned Combat Aerial Vehicles

This paper implements and integrates algorithms for terrain following of UCAVs (Unmanned Combat Aerial Vehicles): trajectory generation, guidance, and navigation. Terrain following is very important for UCAVs because they perform very dangerous missions such as Suppression of Enemy Air Defences while the terrain following can improve the survivability of UCAVs against from the air defence systems of the enemy. To deal with the GPS jamming, terrain referenced navigation based on nonlinear filter is chosen. For the trajectory generation, Voronoi diagram is adopted to generate horizontal plane path to avoid the air defense system. Cubic spline method is used to generate vertical plane path to prevent collisions with ground while flying sufficiently close to surface. Follow-the-Carrot and pure pursuit tracking methods, which are look-ahead point based guidance algorithms, are applied for the guidance. Numerical simulation is performed to verify the performance of the integrated terrain following algorithm.