Jose A. Cobano

Data Retrieving From Heterogeneous Wireless Sensor Network Nodes Using UAVs

This paper describes a method and experimental results of a flight planning method that takes into account uncertainties to determine a safe UAV trajectory. It uses particle filters to predict UAV trajectories taking into account the model of the UAV and of the atmospheric conditions and also considering uncertainties. A waypoint generation module computes intermediate waypoints in order to ensure that the trajectory achieves the required levels of safety (avoids forbidden zones) and mission achievement (passes through way-zones). The method has been applied to collection of data from wireless sensor network and has been validated in the airfield of Bollullos in the Spanish province of Seville.

Multi-UAV collision avoidance with separation assurance under uncertainties

This paper presents a collision avoidance method for multiple UAVs and other non-cooperative aircraft based on velocity planning and taking into account the trajectory prediction under uncertainties. The proposed method finds a safe trajectory from the predicted trajectory modifying the velocity profile of the different co-operative vehicles involved in the collision. A particle filter is used to predict the trajectories under uncertainties dealing with the influence of different sources of uncertainty such as the atmospheric conditions, the UAV model and the limitations of the sensors and control system on board the UAV.

Path planning based on Genetic Algorithms and the Monte-Carlo method to avoid aerial vehicle collisions under uncertainties

This paper presents a collision-free path planning method for an aerial vehicle sharing airspace with other aerial vehicles. It is based on grid models and genetic algorithms to find safe trajectories. Monte-Carlo method is used to evaluate the best predicted trajectories considering different sources of uncertainty such as the wind, the inaccuracies in the vehicle model and limitations of on-board sensors and control system.

Conflict Detection and Resolution Method for Cooperating Unmanned Aerial Vehicles

This paper presents a Conflict Detection and Resolution (CDR) method for cooperating Unmanned Aerial Vehicles (UAVs) sharing airspace. The proposed method detects conflicts using an algorithm based on axis-aligned minimum bounding box and solves the detected conflicts cooperatively using a genetic algorithm that modifies the trajectories of the UAVs with an overall minimum cost. The method changes the initial flight plan of each UAV by adding intermediate waypoints that define the solution flight plan while maintaining their velocities. The method has been validated with many simulations and experimental results with multiple aerial vehicles platforms based on quadrotors in a common airspace. The experiments have been carried out in the multi-UAV aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction

This paper presents a new system for assembly and structure construction with multiple Unmanned Aerial Vehicles (UAVs) which automatically identifies conflicts among them. The system proposes the most effective solution considering the available computation time. After detecting conflicts between UAVs, the system resolves them cooperatively using a collision-free 4D trajectory planning algorithm based on a simple one-at-a-time strategy to quickly compute a feasible but non-optimal initial solution and a stochastic optimization technique named Particle Swarm Optimization (PSO) to improve the initial solution. An anytime approach using PSO is applied. It yields trajectories whose quality improves when available computation time increases. Thus, the method could be applied in real-time depending on the available computation time. The method has been validated with simulations in scenarios with multiple UAVs in a common workspace and experiment in an indoor testbed.

Particle Swarm Optimization for collision-free 4D trajectory planning in Unmanned Aerial Vehicles

This paper presents a new system which automatically identifies conflicts between multiple UAVs (Unmanned Aerial Vehicles) and proposes the most effective solution considering the available computation time. The system detects conflicts using an algorithm based on axis-aligned minimum bounding box and resolves them cooperatively using a collision-free trajectory planning algorithm based on a simple one-at-a-time strategy to quickly compute a feasible but non-optimal initial solution and a stochastic optimization technique named Particle Swarm Optimization (PSO) to improve the initial solution. PSO modifies the 4D trajectories of the UAVs with an overall minimum cost. Determining optimal trajectories with short time intervals during the execution of the mission is not feasible, hence an anytime approach using PSO is applied. This approach yields trajectories whose quality improves when available computation time increases. Thus, the method could be applied in realtime depending on the available computation time. The method has been validated with simulations in scenarios with multiple UAVs in a common workspace.

The Velocity Assignment Problem for Conflict Resolution with Multiple Aerial Vehicles Sharing Airspace

Efficient conflict resolution methods for multiple aerial vehicles sharing airspace are presented. The problem of assigning a velocity profile to each aerial vehicle in real time, such that the separation between them is greater than a given safety distance, is considered and the total deviation from the initial planned trajectory is minimized. The proposed methods involve the use of appropriate airspace discretization. In the paper it is demonstrated that this aerial vehicle velocity assignment problem is NP-hard. Then, the paper presents three different collision detection and resolution methods based on speed planning. The paper also presents simulations and studies for several scenarios.

4D trajectory planning in ATM with an anytime stochastic approach

This paper presents the Anytime Stochastic Conflict Detection and Resolution system (ASCDR), which automatically identifies conflicts between multiple aircraft and proposes the most effective solution 4D trajectory considering the available computation time. The system detects conflicts using an algorithm based on axis-aligned minimum bounding box and resolves them cooperatively using a collision-free 4D trajectory planning algorithm based on a roundabout fast initial solution and a stochastic optimization technique named Particle Swarm Optimization (PSO) to modify the 4D initial trajectories of the aircraft with an overall minimum cost. Moreover, an anytime approach using PSO is applied because determining optimal trajectories with short time intervals in the flight phase is not feasible. Thus, trajectories whose quality improves when available computation time increases are yielded. The method could be applied to Medium-Term and even Short-Term Conflict Detection and Resolution depending on the look-ahead times. The method has been validated with simulations in scenarios with multiple aerial vehicles in a common airspace.

Optimal Reciprocal Collision Avoidance with mobile and static obstacles for multi-UAV systems

This paper presents a collision avoidance algorithm for multiple aerial vehicle systems to be applied in real-time. The proposed algorithm is based on the 3D-Optimal Reciprocal Collision Avoidance (ORCA) algorithm. Several improvements have been implemented such as considering dynamic constraints of the UAV model and static obstacles, so it can be used in realistic environments. The algorithm has been integrated in ROS framework and tested with up to eight Unmanned Aerial Vehicles (UAVs). Several simulations in a realistic environment have been performed, including long endurance cooperative missions. A comparison with the ORCA algorithm is presented to analyze the improvements done.

Multiple gliding UAV coordination for static soaring in real time applications

This paper addresses the problem of extending the flight duration of cooperative missions with multiple gliding fixed-wing UAVs by using the energy that comes from static soaring. We consider exploration missions where UAVs should pass through a set of Point of Interest (PoI) with the presence of thermals in the space. These thermals can be exploited to provide energy in terms of altitude for each gliding UAV. The objective of the mission is to extend the flight duration of each UAV to explore the environment without landing and decreasing the time to perform the mission. An algorithm named Bounded Recursive Heuristic Search (BRHS), based on Depth-First search techniques, is applied to the PoIs and to the UAVs. The main advantage is the real time application because of the low computational load. The paper presents a set of simulations and experiments have carried out in the airfield of La Cartuja (Seville, Spain).