HRA*: hybrid randomized path planning for complex 3D environments

We propose HRA*, a new randomized path planner for complex 3D environments. The method is a modified A* algorithm that uses a hybrid node expansion technique that combines a random exploration of the action space meeting vehicle kinematic constraints with a cost to goal metric that considers only kinematically feasible paths to the goal. The method includes also a series of heuristics to accelerate the search time. These include a cost penalty near obstacles, and a filter to prevent revisiting configurations. The performance of the method is compared against A*, RRT and RRT* in a series of challenging 3D outdoor datasets. HRA* is shown to outperform all of them in computation time, and delivering shorter paths than A* and RRPostprint (author's final draft

Nonholonomic motion planning using the fast marching square method

This research presents two novel approaches to nonholonomic motion planning. The methodologies presented are based on the standard fast marching square path planning method and its application to car-like robots. Under the first method, the environment is considered as a three-dimensional C-space, with the first two dimensions given by the position of the robot and the third dimension by its orientation. This means that we operate over the configuration space instead of the bi-dimensional environment map. Moreover, the trajectory is computed along the C-space taking into account the dimensions of the vehicle, and thus guaranteeing the absence of collisions. The second method uses the standard fast marching square, and takes advantage of the vector field of the velocities computed during the first step of the method in order to adapt the motion plan to the control inputs that a car-like robot is able to execute. Both methods ensure the smoothness and safety of the calculated paths in addition to providing the control actions to perform the trajectory.This work is funded by project number DPI2010-17772, by the Spanish Ministry of Science and Innovation, and also by the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid, and co-funded by the Structural Funds of the EU

Sliding mode control applied in trajectory-tracking of WMRs and autonomous vehicles

Tese de doutoramento apresentada à Fac. de Ciências e Tecnologia da Universidade de CoimbraThe thesis is structured as follows: • Chapter 2: Trajectory tracking problems are summarized. • Chapter 3: Kinematic and dynamic modeling of theWMRs and car-like robots are presented. • Chapter 4: The concept of sliding mode are first introduced. Then the fundamentals of SMC are summarized, including basic definitions, methods of sliding surface and control law design, robustness properties and the methods on handling chattering problems. New sliding-mode trajectory-tracking and slidingmode path-following controllers for WMRs and car-like vehicles, are also proposed in this chapter. • Chapter 5: The trajectory/path planning are developed, including the velocity profile. • Chapter 6: A model with two freedom degrees is considered for the HNC model. The user comfort is examined not only in the time domain, but also in the frequency domain. • Chapter 7: Experimental results obtained with the implementation of the proposed controllers in RobChair are summarized and discussed. • Chapter 8: Finally, conclusions are drawn and some suggestions for future work are provided

Planning Framework for Robotic Pizza Dough Stretching with a Rolling Pin

Stretching a pizza dough with a rolling pin is a nonprehensile manipulation. Since the object is deformable, force closure cannot be established, and the manipulation is carried out in a nonprehensile way. The framework of this pizza dough stretching application that is explained in this chapter consists of four sub-procedures: (i) recognition of the pizza dough on a plate, (ii) planning the necessary steps to shape the pizza dough to the desired form, (iii) path generation for a rolling pin to execute the output of the pizza dough planner, and (iv) inverse kinematics for the bi-manual robot to grasp and control the rolling pin properly. Using the deformable object model described in Chap. 3, each sub-procedure of the proposed framework is explained sequentially

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Planning for steerable needles in neurosurgery

The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way. Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery. However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use. However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment. The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery. Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context. The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces

HRA*: Hybrid randomized path planning for complex 3D environments

Trabajo presentado al IROS celebrado en Tokyo del 3 al 7 de noviembre de 2013.We propose HRA*, a new randomized path planner for complex 3D environments. The method is a modified A* algorithm that uses a hybrid node expansion technique that combines a random exploration of the action space meeting vehicle kinematic constraints with a cost to goal metric that considers only kinematically feasible paths to the goal. The method includes also a series of heuristics to accelerate the search time. These include a cost penalty near obstacles, and a filter to prevent revisiting configurations. The performance of the method is compared against A*, RRT and RRT* in a series of challenging 3D outdoor datasets. HRA* is shown to outperform all of them in computation time, and delivering shorter paths than A* and RRT.This work has been partially supported by the Mexican Council of Science and Technology with a PhD Scholarship to Ernesto Teniente, by the Spanish Ministry of Science and Innovation under project DPI-2011-27510 and by the EU project ARCAS FP7-287617.Peer Reviewe

Dexterous robotic motion planning using intelligent algorithms

The fundamental purpose of robots is to help humans in a variety of difficult tasks, enabling people to increase their capabilities of strength, energy, speed, memory, and to operate in hazardous environments and many other applications. Service robots, more precisely mobile manipulators, incorporate one or two robotic arms and a mobile base, and must accomplish complex manipulations tasks, interacting with tools or objects and navigating through cluttered environments. To this end, the motion planning problem plays a key role in the ahead calculation of robot movements to interact with its world and achieve the established goals. The objective of this work is to design various motion planning methods in order to improve the autonomy of MANFRED-2, which is a mobile robot fully developed by the Robotics Lab research group of the Systems Engineering and Automation Department of the Carlos III University of Madrid. Mobile robots need to calculate accurate paths in order to navigate and interact with objects throughout their surrounding area. In this work, we have developed motion planning algorithms for both navigation and manipulation. The presented algorithms for path planning are based on the Fast Marching Square method and include a replanner with subgoals, an anytime triangular planner, and a nonholonomic approach. The replanner with subgoals starts by generating a smooth and safe global path with the Fast Marching Square method. Then, this path is divided into multiple subpaths separated by equidistant nodes (defined by topological or metric constraints). After that, the obstacles information is progressively added and modifications are made only when the original path is unreachable. The most important advantage with respect to similar approaches is that the generated sub-paths are always efficient in terms of smoothness and safeness. Besides, the computational cost is low enough to use the algorithm in real-time. The anytime triangular planner, such as “Anytime” algorithms, quickly finds a feasible but not necessarily optimal motion plan which is incrementally improved. One important characteristic that this type of algorithms must satisfy is that the path must be generated in real-time. The planner relies on the Fast Marching Square method over a triangular mesh structure. Different variants are introduced and compared under equal circumstances that produce different paths in response time and quality, which leads us to an additional consideration. As in the field of benchmarking it is becoming increasingly difficult to compare new planners approaches because of the lack of a general benchmarking platform, improvements to existing approaches are suggested. Finally, the nonholonomic approach is presented. It is based on the Fast Marching Square method and its application to car-like robots. In order to apply the proposed method, a three dimensional configuration space of the environment is considered. The first two dimensions are given by the position of the robot, and the third one by its orientation. This means that we operate over the configuration space instead of the bi-dimensional environment map. Besides, the trajectory is computed along the configuration space taking into account the dimensions of the vehicle. In this way, it is possible to guarantee the absence of collisions. The proposed method is consistent at local and global scale because it guarantees a motion path solution, if it exists, and does not require global replanning supervision when a local trap is detected. Once a mobile robot has reached a goal location, it usually triggers the servomotors enclosed inside its robotic arm to manipulate a target. The manipulation algorithms presented in this work include the adaptation of trajectories, a planner with adaptive dimensionality, and an implementation of a dimensionality reduction approach inside a nuclear device. The adaptation of manipulation trajectories enables the robot to accomplish a task in different locations by using Evolution Strategies and forward kinematics. This approach avoids all the inconveniences that inverse kinematics imply, as well as the convergence problems in singular kinematic configurations. The planner with adaptive dimensionality reduces the complexity of high-dimensional path planning. First, a Rapidly-exploring Random Tree trajectory is generated using the full degrees of freedom of the robotic arm. Then, a geometry as a closed tube is built around the path line and the Fast Marching Square method is applied from start to goal using three dimensions inside the surface. The resulted three dimensional path is converted to full degrees of freedom with the inverse kinematics of the robot. The result is a smoother and safer path, visually more human friendly. Additionally, the search space is reduced, and therefore, also the planning time and the memory requirements. The application inside the nuclear device, similarly to the previous approach, reduces the degrees of freedom of the problem (but this time to two dimensions due to the mostly planar nature of the robot). The manipulation path is smooth and safe in an environment where safety must be the primarily objective. The motion planning algorithms have been tested in numerous experiments. The fast response of the methods allows its application in real-time tasks.El propósito fundamental de los robots es ayudar a los humanos en tareas difíciles, lo que permite a las personas incrementar sus capacidades de fuerza, energía, velocidad y memoria para trabajar en entornos peligrosos y en una inmensa variedad de aplicaciones. Los robots de servicio, puntualmente los manipuladores móviles, incorporan uno o dos brazos robóticos y una base móvil, y deben ser capaces de realizar tareas complejas de manipulación, interactuando con herramientas u objetos y navegando a través de entornos con obstáculos. Para este fin, el problema de la planificación de movimientos juega un rol clave en el cálculo anticipado de los movimientos del robot, para interactuar con su mundo y realizar las tareas establecidas. El objetivo de este trabajo es diseñar diversos métodos de planificación de movimiento con el fin de mejorar la autonomía de MANFRED-2, un robot móvil que fue desarrollado completamente en el grupo de investigación del Laboratorio de Robótica del Departamento de Ingeniería de Sistemas y Automatización de la Universidad Carlos III de Madrid. Los robots móviles necesitan calcular de antemano trayectorias precisas para poder navegar e interactuar con objetos en su entorno. En este trabajo, hemos desarrollado algoritmos de planificación de movimiento para navegación y manipulación robótica. Los algoritmos presentados para la planificación de trayectorias de navegación se basan en el método de Fast Marching Square (FM2) e incluyen un replanificador con sub-objetivos, un planificador triangular de tipo interrumpible (en inglés este enfoque es mejor conocido como Anytime), y un enfoque no holonómico. El replanificador con submetas comienza generando una trayectoria global de curvas suaves y segura con FM2, entonces este camino es dividido en múltiples subtrayectorias separadas por nodos equidistantes (definidos por restricciones topológicas o métricas). Después de esto, se actualiza progresivamente el entorno con obstáculos detectados por los sensores; sólo se realizan cambios cuando la trayectoria original resulta inalcanzable. La ventaja más importante con respecto a enfoques similares es que las sub-trayectorias generadas son siempre eficientes en términos de suavidad y seguridad. Además, el coste computacional es lo suficientemente bajo como para utilizar el algoritmo en tiempo real. El planificador triangular interrumpible, como algoritmo “Anytime”, encuentra rápidamente una trayectoria de navegación válida, pero no necesariamente ópti ma, a continuación, de forma incremental se va mejorando según haya tiempo hasta llegar al óptimo. La capacidad más resaltante de este tipo de algoritmos es la de generar trayectorias en tiempo real. El planificador se basa en el uso de FM2 sobre una estructura de malla triangular. Se presentan diferentes formas de construir el mallado y se comparan en igualdad de circunstancias los diferentes caminos producidos en tiempo de respuesta y calidad, lo que generó una contribución adicional. Debido a la falta de una plataforma general de evaluación robusta, en el campo de la evaluación de trayectorias es cada vez más difícil comparar nuevos planificadores, por consiguiente se sugieren mejoras a los enfoques existentes. Finalmente, se presenta el enfoque no holónomo, que se basa en FM2 y su aplicación en robots móviles con sistemas de dirección similares a la de los coches. Para aplicar el método propuesto, se considera un espacio de configuración tridimensional del entorno, donde las dos primeras dimensiones vienen dadas por la posición del robot y la tercera dimensión, por su orientación. Esto quiere decir, que operamos en el espacio de configuraciones en vez de en el mapa bidimensional del entorno. Además, la trayectoria se calcula en el espacio de configuraciones teniendo en cuenta las dimensiones del vehículo, de esta manera es posible garantizar la ausencia de colisiones. El método propuesto es consistente a nivel local y global, ya que si existe una solución se garantiza encontrarla, y no requiere de supervisión global para reiniciar una planificación cuando se detecta un bloqueo a nivel local. Una vez que el robot móvil ha alcanzado la ubicación requerida, se suelen accionar los servomotores que están dentro del brazo robótico para manipular un objeto. Los algoritmos de manipulación presentados en este trabajo incluyen la adaptación de trayectorias, un planificador con dimensionalidad adaptable, y una implementación de un método de reducción de la dimensionalidad dentro de un dispositivo nuclear. La adaptación de las trayectorias de manipulación permite al robot realizar una misma tarea con diferentes ubicaciones y orientaciones haciendo uso de una Evolution Strategy y la cinemática directa del robot, este enfoque evita todos los inconvenientes que implican utilizar la cinemática inversa, así como los problemas de convergencia en configuraciones cinemáticas singulares. El planificador con dimensionalidad adaptativa reduce la complejidad de la planificación de trayectorias de manipulación con muchas dimensiones; en primer lugar, una trayectoria RRT se genera utilizando todos los grados de libertad (DOF) del brazo robótico, a continuación, una figura geométrica en forma de tubo cerrado se construye alrededor de la línea de la trayectoria y se aplica FM2 dentro de la superficie desde el inicio hasta el objetivo utilizando tres dimensiones, la ruta 3D resultante se convierte con la cinemática inversa del robot. El resultado es un camino más suave y seguro, más amigable a la vista humana. Además, debido a que el espacio de búsqueda se reduce, también se reduce el tiempo de planificación y los requerimientos de memoria. La aplicación en el interior del dispositivo nuclear, de manera similar al enfoque anterior, reduce los DOF del problema pero esta vez a dos dimensiones aprovechando la naturaleza mayormente plana del robot utilizado. La trayectoria de manipulación es suave y segura, lo que es conveniente en un entorno donde la seguridad debe ser el objetivo principal. Los algoritmos de planificación de movimiento resultantes han sido probados en numerosos experimentos. La respuesta rápida de los métodos permite su aplicación en tiempo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Carlos Sagüés Blázquiz.- Vocal: Pedro Lim