Hierarchical D ∗ algorithm with materialization of costs for robot path planning

In this paper a new hierarchical extension of the D ∗ algorithm for robot path planning is introduced. The hierarchical D ∗ algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance. This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs) are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental results are showed and compared to other path planning algorithms

The Ariadne's Clew Algorithm

We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing

High-Level Control Of Modular Robots

Reconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot. Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform

Navigation of mobile robots using artificial intelligence technique.

The ability to acquire a representation of the spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. This document presents a computer vision method and related algorithms for the navigation of a robot in a static environment. Our environment is a simple white colored area with black obstacles and robot (with some identification mark-a circle and a rectangle of orange color which helps in giving it a direction) present over it. This environment is grabbed in a camera which sends image to the desktop using data cable. The image is then converted to the binary format from jpeg format using software which is then processed in the computer using MATLAB. The data acquired from the program is then used as an input for another program which controls the robot drive motors using wireless controls. Robot then tries to reach its destination avoiding obstacles in its path. The algorithm presented in this paper uses the distance transform methodology to generate paths for the robot to execute. This paper describes an algorithm for approximately finding the fastest route for a vehicle to travel one point to a destination point in a digital plain map, avoiding obstacles along the way. In our experimental setup the camera used is a SONY HANDYCAM. This camera grabs the image and specifies the location of the robot (starting point) in the plain and its destination point. The destination point used in our experimental setup is a table tennis ball, but it can be any other entity like a single person, a combat unit or a vehicle

Controling of Mobile Agents using Intelligent Strategy

Robots are developed to carry out certain task to help the human beings. A robot carrying out a particular needed task has promising applications for the betterment of human society. So the control of their motion remains a vital part for a robot. In this project, I have to develop the simulation of mobile agents (robots) in an arena of obstacles from a start point to a destination point without collision. So in a way this project deals with successful navigation of robots in prior known environment. This document presents a computer vision method and related algorithms for the navigation of a robot in a static environment. Our environment is a simple white coloured area with coloured obstacles (circle with white colour, rectangles with orange colour, triangle with green colour and hexagon with pink colour which helps in identifying the obstacle) and robot is in a rectangular form. The agents starting point is in blue colour and the destination point is in red colour. This environment is input by the user with the starting point and the destination point. The data acquired from here is then used as an input for the program which controls the robot drive motion in graphic control window. Robot then tries to reach its destination avoiding obstacles in its path. The algorithm presented in this paper uses the distance transform methodology to generate paths for the robot to execute which are written in C++ compiler. These paper developments can also be applied to vehicles for collision free driving

Ant colony optimization for agile motion planning

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 67-69).With the need for greater autonomy in unmanned vehicles growing, design of algorithms for mission-level planning becomes essential. The general field of motion planning for unmanned vehicles falls into this category. Of particular interest is the case of operating in hostile environments with unknown threat locations. When a threat appears, a replan must be quickly formulated and executed. The use of terrain masking to hide from the threat is a vital tactic, which a good algorithm should exploit. In addition, the algorithm should be able to accommodate large search spaces and non-linear objective functions. This thesis investigates the suitability of the Ant Colony Optimization (ACO) heuristic for the agile vehicle motion planning problem. An ACO implementation tailored to the motion planning problem was designed and tested against an existing genetic algorithm solution method for validation. Results show that ACO is indeed a viable option for real-time trajectory generation. ACO' ability to incorporate heuristic information, and its method of solution construction, make it better suited to motion planning problems than existing methods.by Tom Krenzke.S.M

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

Real-time motion planning and simulation of cranes in construction

Real-time planning the motion of heavy equipment (e.g. cranes) is an important issue in construction projects, where rapid and accurate planning directly affects the safety and productivity of operation. The work presented in this thesis is directed towards automatically generating an accurate motion plan in space and time for cranes by: (1) Investigating and utilizing motion planning algorithms to generate feasible paths with respect to all considered constraints; (2) Extending the efficiency of motion planning under complex global constraints (Le. geometrical constraints) that represent static and dynamic obstacles found in the construction site; and (3) Considering local constraints that are related to the stability of the crane itself. Local constraints include engineering constraints (e.g. workloads for cranes) in addition to kinematic and dynamic constraints for the crane joints. The methodology presented in this thesis was applied to develop a specialized motion planning system for construction equipment called Intelligent Construction Equipment motion Planner (ICE-Planner). This system was integrated into the 3D software to define, solve and visualize motion planning in real time. The proposed methodology provides: (1) A motion planning framework for supporting cranes with the ability of generalizing over different types of equipment; (2) practical equipment planning which is aware of local constraints derived from engineering and kinematics properties of the equipment itself; (3) more accurate and realistic motion planning with efficiency in re-planning dynamic cases found in actual sites; and (4) the ability of visualizing and simulating motion planning results in real-time