This paper presents a non-linear optimization method for trajectory planning of tethered aerial robots. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV and tether.

This paper presents a vision-based localization system for global pose estimation of a sewer inspection robot given prior information of the sewer network from local institutions. The system is based on a Monte-Carlo Localization system that uses RGBD odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Moreover, this step is further refined whenever a discrete element of the network (i.e. manhole) is detected. To this end, another RGBD camera pointing upwards is used for precise manhole detection. A Convolutional Neural Network has been successfully trained for classifying images with and without manholes with 96% accuracy over the tested dataset. The complete system has been validated with real data obtained from the sewers of Barcelona yielding accurate localization results. All the logs and code used in the context of this paper are publicly available.

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).

This paper presents a cooperative system architecture that extends the flight duration of multiple gliding fixed-wing Unmanned Aerial Vehicles (UAVs) for long endurance missions. The missions are defined by a set of Points of Interest (PoI) and UAVs should pass through them. A module to detect and identify thermals is implemented to exploit their energy and extend the flight duration, known as static soaring. A collision-free trajectory planner based on the RRT∗ (Optimal Rapidly-exploring Random Trees) planning algorithm is implemented. The proposed system allows applications in real time because of its low computational needs. Simulations and experiments carried out in the airfield of La Cartuja (Seville, Spain) show the performance and advantages of the proposed system.

This paper presents an efficient conflict resolution method for multiple aerial vehicles based on speed planning. The problem is assigning a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial planned trajectories is minimized. Also, the arrival time of each aerial vehicle at the end waypoint of the trajectory is taken into account to solve the conflicts. The proposed method involves the use of appropriate airspace discretization. The method consists of two steps: a search tree step, which finds if it exists a solution; and an optimization step by solving a QP-problem, which minimizes a cost function. The paper also presents simulations for several scenarios and experiments that have been carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

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.