A memetic approach to the inverse kinematics problem for robotic applications

The inverse kinematics problem of an articulated robot system refers to computing the joint configuration that places the end-effector at a given position and orientation. To overcome the numerical instability of the Jacobian-based algorithms around singular joint configurations, the inverse kinematics is formulated as a constrained minimization problem in the configuration space of the robot. In previous works this problem has been solved for redundant and non-redundant robots using evolutionary-based algorithms. However, despite the flexibility and accuracy of the direct search approach of evolutionary algorithms, these algorithms are not suitable for most robot applications given their low convergence speed rate and the high computational cost of their population-based approach. In this thesis, we propose a memetic variant of the Differential Evolution (DE) algorithm to increase its convergence speed on the kinematics inversion problem of articulated robot systems. With the aim to yield an efficient trade-off between exploration and exploitation of the search space, the memetic approach combines the global search scheme of the standard DE with an independent local search mechanisms, called discarding. The proposed scheme is tested on a simulation environment for different benchmark serial robot manipulators and anthropomorphic robot hands. Results show that the memetic differential evolution is able to find solutions with high accuracy in less generations than the original DE. -----------------------------------------------------------La cinemática inversa de los robots manipuladores se refiere al problema de calcular las coordenadas articulares del robot a partir de coordenadas conocidas de posición y orientación de su extremo libre. Para evitar la inestabilidad numérica de los métodos basados en la inversa de la matriz Jacobiana en la vecindad de configuraciones singulares, el problema de cinemática inversa es definido en el espacio de configuraciones del robot manipulador como un problema de optimización con restricciones. Este problema de optimización ha sido previamente resuelto con métodos evolutivos para robots manipuladores, redundantes y no redundantes, obteniéndose buenos resultados; sin embargo, estos métodos exhiben una baja velocidad de convergencia no adecuada para aplicaciones robóticas. Para incrementar la velocidad de convergencia de estos algoritmos, se propone un método memético de evolución differencial. El enfoque de búsqueda directa propuesto combina el esquema estándar de evolución diferencial con un mecanismo independiente de refinamiento local, llamado discarding o descarte. El desempeño del método propuesto es evaluado en un entorno de simulación para diferentes robot manipuladores y manos robóticas antropomórficas. Los resultados obtenidos muestran una importante mejora en precisión y velocidad de convergencia en comparación del método DE original.Programa en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Pedro M. Urbano de Almeida Lima; Vocal: Cecilia Elisabet García Cena; Secretario: Mohamed Abderrahim Fichouch

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved

Theory of Self-maintaining Robots

This thesis proposes a theory for robotic systems that can be fully self-maintaining. The presented design principles focus on functional survival of the robots over long periods of time without human maintenance. Self-maintaining semi-autonomous mobile robots are in great demand in nuclear disposal sites from where their removal for maintenance is undesirable due to their radioactive contamination. Similar are requirements for robots in various defence tasks or space missions. For optimal design, modular solutions are balanced against capabilities to replace smaller components in a robot by itself or by help from another robot. Modules are proposed for the basic platform, which enable self-maintenance within a team of robots helping each other. The primary method of self-maintenance is replacement of malfunctioning modules or components by the robots themselves. Replacement necessitates a robot team’s ability to diagnose and replace malfunctioning modules as needed. Due to their design, these robots still remain manually re-configurable if opportunity arises for human intervention. A system reliability model is developed to describe the new theory. Depending on the system reliability model, the redundancy allocation problem is presented and solved by a multi objective algorithm. Finally, the thesis introduces the self-maintaining process and transfers it to a multi robot task allocation problem with a solution by genetic algorithm

Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments

This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight. The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two differentComment: Ph.D Dissertatio

Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments

This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight. The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two different approaches to negotiate tether with complex obstacle-occupied environments. The proposed research provides a formal reasoning of motion risk in unstructured or confined spaces, contributes to the field of risk-aware planning with a versatile planner, and opens up a new regime of indoor UAV navigation: tethered indoor flight to ensure battery duration and failsafe in case of vehicle malfunction. It is expected to increase teleoperation productivity and reduce costly errors in scenarios such as safe decommissioning and nuclear operations in the Fukushima Daiichi facility