In the field of robotics, the demand for adaptable skills capable of effectively handling diverse situations has surpassed the reliance on repetitive tasks. To enhance the generalization of motion policies, task-parameterized learning has emerged as a valuable approach, as it encodes pertinent contextual information in task parameters, facilitating the flexible execution of tasks. This process requires the collection of multiple demonstrations in various situations. Generate a set of diverse situations covering all possible cases is a complex task. Due to this, the search for training with fewer demonstrations becomes highly desirable. In this article, we present a novel algorithm focused on generating synthetic information to facilitate the generalization of parameterized tasks. This algorithm makes use of Kinesthetic Fast Marching Learning, a Learning from Demonstration (LfD) algorithm that allows obtaining optimal movement paths based on velocity fields. The algorithm enables autonomous data generation, producing demonstrations that are equal to or more optimal than those generated by users themselves. Evaluation is done through a metric based on Wasserstein distance that takes into account the probabilistic data of the generated paths. The algorithm has been evaluated through tests in simulated environments, comparing its efficiency against two widely used algorithms (where it has shown greater efficiency in generalization and the generation of more optimal paths) and with real-world tests in a task-oriented environment (sweeping) carried out by the ADAM robot.

Swarms composed of multiple unmanned aerial vehicles (UAVs) are emerging as a key tool in various application fields including aerial surveillance, urban search and rescue, and package delivery. In this context, trajectory planning remains one of the principal concerns regarding complex robotic applications. This paper proposes a 4D path planning algorithm based on the Fast Marching Square (FM$^2$) method for multi-UAV teams in high-dimensional 3D environments with multiple obstacles. The 4D-FM$^2$algorithm integrates a time-dependent speed function within the Fast Marching (FM) framework. The proposed algorithm was tested in a simulated urban scenario and results demonstrate that the algorithm effectively plans safe, optimal solutions to the shortest path problem for UAVs while avoiding obstacles and other drones, even in complex situations. Additionally, the algorithm excels in providing smooth speed profiles for the vehicles. Furthermore, the study also successfully evaluated the effect of various implementation parameters on the algorithm’s outcome, such as the number of concurrent missions and the velocity of the vehicles. Moreover, computational time was kept within acceptable limits, and the method demonstrated overall good performance in terms of air traffic flow. The main contribution of this work is the introduction of a novel 4D trajectory planning algorithm that implicitly incorporates UAV movement and speed and hence the spatio-temporal realization of the path during the planning phase, leveraging the light propagation phenomenon. This approach efficiently addresses the presence of other UAVs and environmental obstacles, thereby preventing collisions and avoiding unnecessary airspace blockages.

The exploration of unknown environments is a challenge in robotics. The proposed method approaches this problem by combining the Fast Marching Square path planning technique with the machine learning method called Gaussian processes (GP). The Fast Marching Square method is used to determine the most unexplored areas of the environment and to plan the path of the vehicle from the current position to the selected point. The GP model is used to obtain predictions about the unexplored regions of the environment based on the collected data so far during the exploration. The use of Unmanned Aerial Vehicles (UAVs) for exploration and surveillance has increased exponentially in the recent years, due to their sensor equipment capabilities and their versatility for flying over difficult terrain. By defining the weight each method has on the selection of the next point to explore, we can focus the UAV on the points with more interesting data defined by the user (i.e. bodies of water), the most unexplored regions, or a combination of both. We present an study on the influence of these weights on the mean absolute error (MAE) and predictive variance obtained from the GP model and test the algorithm on a real environment obtained from a satellite image. We show that we are able to generate an accurate depiction of the environment way faster than traditional methods such as the Boustrophedon.

Traditional path planning for robots is a well-studied problem. However, the classical setting of the problem is simple to state: plan a path for a robot, starting from an initial point, and ending at a desired target point, given an environmental map, usually in the form of an occupancy grid. In this setting though, no special consideration is given to humans; they are thought of simply, as being obstacles in the environment, equivalent to chairs or walls. However, with more robots entering human spaces, special consideration needs to be given: humans need special treatment as obstacles, and furthermore humans can also serve the goal of goal points, towards starting a social interaction; either individual humans or groups of humans. Also, special mechanisms are required for engaging and disengaging in such interactions, taking into account psychological considerations of proxemics. In this paper, we first introduce our unifying theoretical framework for all the subproblems of social path planning; then, we propose an extended mode for engaging groups of people; and then, by using a special version of the fast-marching square planning method, we present and demonstrate actual algorithmic solutions for the social path planning subproblems. Our results prove the strengths of our approach and its generalizability. Finally, concrete further steps are discussed.

Traditional path planning for robots is a well-studied problem. However, the classical setting of the problem is simple to state: plan a path for a robot, starting from an initial point, and ending at a desired target point, given an environmental map, usually in the form of an occupancy grid. In this setting though, no special consideration is given to humans; they are thought of simply, as being obstacles in the environment, equivalent to chairs or walls. However, with more robots entering human spaces, special consideration needs to be given: humans need special treatment as obstacles, and furthermore humans can also serve the goal of goal points, towards starting a social interaction; either individual humans or groups of humans. Also, special mechanisms are required for engaging and disengaging in such interactions, taking into account psychological considerations of proxemics. In this paper, we first introduce our unifying theoretical framework for all the subproblems of social path planning; then, we propose an extended mode for engaging groups of people; and then, by using a special version of the fast-marching square planning method, we present and demonstrate actual algorithmic solutions for the social path planning subproblems. Our results prove the strengths of our approach and its generalizability. Finally, concrete further steps are discussed.

Rhombic like vehicles are characterized for high maneuverability in cluttered environments. This type of vehicles will be used on remote handling operations of maintenance in the International Thermonuclear Experimental Reactor (ITER). Previous work was done in motion planning using Constrained Delaunay Triangulation for rhombic like vehicles operating in ITER. This paper shows that the integration of Fast Marching Square improves the motion planning methodology, decreasing also the computational effort, which can be applied not only in ITER but also in other complex and cluttered environments. Simulated results are presented comparing the initial and the improved motion planning.

This paper presents a novel methodology for planning the movements of a robotic hand when a precision grasp is going to be performed. The approach used is based on the standard Fast Marching Square (FM2) path planning method and its application to robot formations motion planning. In this case, the hand is considered to be a kinematic chain in which a mobile robot is located at every joint position. The robot formation is therefore deformable among the positions allowed by the mechanical limits of the joints. To perform a given precision grasp, the task is divided into two phases. First the hand approaches the object. FM2 is used to calculate a fast and smooth path towards the object to be grasped. While the hand is covering it, the formation updates its shape according to the map of velocities calculated in FM2. The second phase consists on performing the precision grasp. Every finger is modelled as a robot formation and a path is calculated for each fingertip so that they reach the grasping points on the object. The position of the joints of the fingers is computed using an inverse kinematics algorithm. Simulations show the usefulness of this approach thanks to a good performance of the approaching and the grasping tasks.

This paper presents a novel robotic learning technique based on Fast Marching Square (FM 2 ). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.

This paper describes the control of an automatic overhead crane for assembly of modular building elements. The automatic system was developed using a commercial full-size crane, which was modified by adding adequate sensors, servomotors, and control strategy. The crane, which maintained its original cables, was converted to a robotic system, and is controlled via a computer-based multiaxes control board. The implemented algorithms solve two main problems of module assembly: 1) precision positioning of large and heavy modules in the order of a few centimeters, and 2) anti-swinging transportation of modules, even in difficult weather conditions (not extreme). The implementation of the anti-swinging control is achieved using the on-line two-dimensional inclinometer measurements and an on-line calculation of input impulse trains. The developed input-shaping control is divided into two phases: straight line motion and external perturbances cancellation. Experimental results of the developed algorithms which demonstrate the effectiveness of the new process are presented.

A new solution to the Simultaneous Localization and Modelling problem is presented. It is based on the stochastic search of solutions in the state space to the global localization problem by means of a differential evolution algorithm. A non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at a local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In a second step the aligned laser measures together with the corrected robot poses are use to detect when the robot is revisiting a previously crossed area. Once a cycle is detected, the Evolutive Localization Filter is used again to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment. The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.

This paper presents a new path planning method which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step fast marching method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines speed and reliability, because the map dimensions is reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot

This paper presents a new sensor based global path planner which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step fast marching method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines an extremely fast global planner operating on a simple sensor based environment modeling, while it operates at the sensor frequency. The main characteristics are speed and reliability, because the map dimensions are reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot

This paper presents a new path planning method based in the inverse of the logarithm of the distance transform and in the fast marching method. The distance transform of an image gives a grey scale that is darker near the obstacles and walls and more clear far from them and it is calculated via Voronoi diagram. The logarithm of the inverse of the distance transform imitates the repulsive electric potential from walls and obstacles. This method is very fast and reliable and the trajectories are similar to the human trajectories: smooth and not very close to obstacles and walls