This paper presents a 3D safe heuristic planner for aerial robot indoor navigation incorporating semantic information of the environment. Our interest lies in how the inclusion of semantic information in industrial environments or buildings can positively affect planning to ensure 3D safe navigation within the environment. The planner is based on an any-angle path planning algorithm, particularly Lazy Theta* algorithm. There are many works in the literature that include non-uniform costs related to distance to the closest obstacles in order to achieve a safe planner, but they generally do not consider the semantic information which is relevant in factories. Semantic-aware planning opens the door to safer plans, adapting the distance to obstacles depending on the category of the objects. Our Semantic Heuristic planner considers the semantic information of all the nodes along the line of sight between an initial and final node. Tests in a 3D environment with corridors, stairs, doors, columns and walls are performed to evaluate the proposed planner with respect to Lazy Theta* algorithms and a cost-aware Lazy-Theta* over Euclidean distance functions. The results show advantages of the Semantic Heuristic planner in terms of safety depending on the type of obstacle, the length of the path and efficiency with respect to the cost-aware Lazy-Theta* which only considers the distance to obstacles in its cost function.

This paper presents a fast cost-aware any-angle path planning algorithm for aerial robots in 3D building-like environments. The approach integrates Euclidean Distance Fields (EDF) and Lazy Theta* algorithm to compute safe and smooth paths. We show how to consider the analytical proper-ties of EDFs for polygonal obstacles to get an approximation of the cost along the line of sight segments of the planner, reducing the computational requirements. Numerous tests in a realistic building-like environment are performed to evaluate the proposed algorithm with respect to other heuristic search algorithms considering the distance cost by using an EDF. The results show that the proposed algorithm considerably reduces the computation time in indoor and outdoor environments enabling fast, safe and smooth paths.

This paper addresses the material handling problem (MHS) in warehouse automation by proposing a system that uses an automated guided vehicle (AGV) in industrial environments. The aim is to optimize the picking task with respect to manual operation in a paint factory. The work describes the whole system to perform all the automatic tasks. The process is controlled by the Manufacturing Process Management System (MPMS) and an autonomous co-worker robot execute the mission in partially known environments. The navigation system implemented is safe and robust. It considers the people detection and unknown static obstacles. Also, an ultra-wide-band localization system is implemented by offering new capabilities for situation awareness in factories. Experiments with a real holonomic platform called ARCO are performed to validate the approach.

This paper presents a method for trajectory generation with Bézier curves, by considering the wind field and dynamic constraints for small Unmanned Aerial Systems (UAS). Atmospheric phenomena affect UAS trajectories, so the wind presence should be considered in order efficiently perform a mission. These missions require precise area decomposition, efficient waypoint sequencing, and a smooth trajectory generation to fulfill the required level of safety and reliability. In this context, a path planning algorithm is presented which incorporates the use of an area decomposition scheme which considers complex shapes and restrictions, in order to provide high levels of coverage. A novel trajectory generation algorithm is proposed, which aims to harvest energy from the atmosphere by taking advantage of the previously estimated wind field and the identified wind features, such as shear wind and discrete gusts, by taking into account flight envelope restrictions. Different test cases and scenarios, including Software-In-The-Loop (SITL) simulations and real telemetry data analysis, are presented to assess the energy gain for the implemented algorithms. The results indicate promising energy gains of almost 15% in voltage saving if the mentioned factors are considered to re-plan the path with the energy-efficient trajectories.

This paper presents a new method for estimation and identification of shear wind and discrete gusts of a previously unknown wind field by using an Unmanned Aircraft System (UAS). Wind estimation and identification is key in energy-efficient trajectory planning and dynamic soaring applications. The research proposes an approach for mapping a complete wind field from the collected data. Therefore, the generated map also describes areas where UAS has not passed through. The proposed method consists of the next steps: 1) the wind vector is estimated in each UAS position; 2) wind data are fitted into a Weibull probability density function and meeting the Prandtl's power law relationship; 3) scale factor of the Weibull distribution and the power law coefficient are computed; 4) wind feature detection such as shear layer and gusts is performed from the relation wind magnitude vs. altitude obtained; and finally 5), data could be extrapolated to generate the complete wind field. Novel aspects and advantages include the optimization of the scale factor from the estimated wind data by using a genetic algorithm, the identification of wind features separately, and the possibility to apply the method online. Real data of flight have been used to validate the method and many simulations and studies have been performed to test and analyze the proposed method in different scenarios.

This paper proposes a method called Segmentation-adaptive Pseudospectral collocation to address the problem of safe trajectory generation in missions with cooperating multiple aerial vehicles. Pseudospectral collocation can generate optimized collision-free trajectories, but for multiple aerial vehicles it cannot guarantee that the safety separation distance is maintained in the whole trajectories, since the constraints are only enforced in discrete points in the trajectory (collocation points). Hp-adaptive pseudospectral collocation increases iteratively the number of collocation points and the degree of the approximating polynomial, but this may lead to an exponential increase of the computational load. The proposed method solves the problem by selectively adding new collocation points where they are needed, only in the segments with conflicts in each iteration, thus effectively reducing the number of collocation points and the computation time with respect to other pseudospectral collocation formulations. The proposed method allows both changes of speed and changes of heading for each aerial vehicle to guarantee the safety distance between them. Its computational load and scalability are studied in randomly generated scenarios. Moreover, a comparison with other method is presented. Several experiments to test the validity of the approach have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies.

This paper proposes the application of an hp-adaptive pseudospectral for trajectory generation in scenarios with multiple aerial vehicles in order to avoid collisions. This method computes an optimal solution numerically. The method assigns 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 trajectories is minimized. The Estimated Time of Arrival (ETA) of each aerial vehicle is also taken into account to solve the conflicts. Its computational load and scalability depending on the main parameters of the method are studied. Many simulations have been performed to analyze the best parameters of the method. Experiments have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

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.