Hybrid PSO-PWL-Dijkstra approach for path planning of non holonomic platforms in dense contexts

Planning is an essential capability for autonomous robots. Many applications impose a diversity of constraints and traversing costs in addition to the usually considered requirement of obstacle avoidance. In applications such as route planning, the use of dense properties is convenient as these describe the terrain and other aspects of the context of operation more rigorously and are usually the result of a concurrent mapping and learning process. Unfortunately, planning for a platform with more than three degrees of freedom can be computationally expensive, particularly if the application requires the platform to optimally deal with a thorough description of the terrain. The objective of this thesis is to develop and demonstrate an efficient path planning algorithm based on dynamic programming. The goal is to compute paths for ground vehicles with and without trailers, that minimise a specified cost-to-go while taking into account dynamic constraints of the vehicle and dense properties of the environment. The proposed approach utilises a Quadtree Piece-Wise Linear (QT-PWL) approximation to describe the environment in a low dimensional subspace and later uses a particle approach to introduce the dynamic constraints of the vehicle and to smooth the path in the full dimensional configuration space. This implies that the optimisation process can exploit the QT-PWL partition. Many usual contexts of operation of autonomous platforms have cluttered spaces and large regions where the dense properties are smooth; therefore, the QT-PWL partition is able to represent the context in a fraction of cells that would be needed by a homogeneous grid. The proposed methodology includes adaptations to both algorithms to achieve higher efficiency of the computational cost and optimality of the planned path. In order to demonstrate the capabilities of the algorithm, an idealized test case is presented and discussed. The case for a car and a tractor with multiple trailers is presented. A real path planning example is presented in addition to the synthetic experiments. Finally, the experiments and results are analysed and conclusions and directions for possible future work are presented

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

Navigational Path Analysis of Mobile Robot in Various Environments

This dissertation describes work in the area of an autonomous mobile robot. The objective is navigation of mobile robot in a real world dynamic environment avoiding structured and unstructured obstacles either they are static or dynamic. The shapes and position of obstacles are not known to robot prior to navigation. The mobile robot has sensory recognition of specific objects in the environments. This sensory-information provides local information of robots immediate surroundings to its controllers. The information is dealt intelligently by the robot to reach the global objective (the target). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimisation problem and thus can be analyzed and solved using AI techniques. The optimisation of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A successful way of structuring the navigation task deals with the issues of individual behaviour design and action coordination of the behaviours. The navigation objective is addressed using fuzzy logic, neural network, adaptive neuro-fuzzy inference system and different other AI technique.The research also addresses distributed autonomous systems using multiple robot

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

Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles

This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems. The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima. To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios