Concurrently Evolving Sensor Morphology and Control for a Hexapod Robot

Evolving a robot’s sensor morphology along with its control program has the potential to significantly improve its effectiveness in completing the assigned task, plus accommodates the possibility of allowing it to adapt to significant changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. The evolution was done in simulation and the tests, which were also done in simulation, showed that effective sensor morphologies and control programs could be co-learned by the system. In this paper, we describe the learning system and show that the simulated results are confirmed by tests on the actual hexapod robot

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Online Optimization-based Gait Adaptation of Quadruped Robot Locomotion

Quadruped robots demonstrated extensive capabilities of traversing complex and unstructured environments. Optimization-based techniques gave a relevant impulse to the research on legged locomotion. Indeed, by designing the cost function and the constraints, we can guarantee the feasibility of a motion and impose high-level locomotion tasks, e.g., tracking of a reference velocity. This allows one to have a generic planning approach without the need to tailor a specific motion for each terrain, as in the heuristic case. In this context, Model Predictive Control (MPC) can compensate for model inaccuracies and external disturbances, thanks to the high-frequency replanning. The main objective of this dissertation is to develop a Nonlinear MPC (NMPC)-based locomotion framework for quadruped robots. The aim is to obtain an algorithm which can be extended to different robots and gaits; in addition, I sought to remove some assumptions generally done in the literature, e.g., heuristic reference generator and user-defined gait sequence. The starting point of my work is the definition of the Optimal Control Problem to generate feasible trajectories for the Center of Mass. It is descriptive enough to capture the linear and angular dynamics of the robot as a whole. A simplified model (Single Rigid Body Dynamics model) is used for the system dynamics, while a novel cost term maximizes leg mobility to improve robustness in the presence of nonflat terrain. In addition, to test the approach on the real robot, I dedicated particular effort to implementing both a heuristic reference generator and an interface for the controller, and integrating them into the controller framework developed previously by other team members. As a second contribution of my work, I extended the locomotion framework to deal with a trot gait. In particular, I generalized the reference generator to be based on optimization. Exploiting the Linear Inverted Pendulum model, this new module can deal with the underactuation of the trot when only two legs are in contact with the ground, endowing the NMPC with physically informed reference trajectories to be tracked. In addition, the reference velocities are used to correct the heuristic footholds, obtaining contact locations coherent with the motion of the base, even though they are not directly optimized. The model used by the NMPC receives as input the gait sequence, thus with the last part of my work I developed an online multi-contact planner and integrated it into the MPC framework. Using a machine learning approach, the planner computes the best feasible option, even in complex environments, in a few milliseconds, by ranking online a set of discrete options for footholds, i.e., which leg to move and where to step. To train the network, I designed a novel function, evaluated offline, which considers the value of the cost of the NMPC and robustness/stability metrics for each option. These methods have been validated with simulations and experiments over the three years. I tested the NMPC on the Hydraulically actuated Quadruped robot (HyQ) of the IIT’s Dynamic Legged Systems lab, performing omni-directional motions on flat terrain and stepping on a pallet (both static and relocated during the motion) with a crawl gait. The trajectory replanning is performed at high-frequency, and visual information of the terrain is included to traverse uneven terrain. A Unitree Aliengo quadruped robot is used to execute experiments with the trot gait. The optimization-based reference generator allows the robot to reach a fixed goal and recover from external pushes without modifying the structure of the NMPC. Finally, simulations with the Solo robot are performed to validate the neural network-based contact planning. The robot successfully traverses complex scenarios, e.g., stepping stones, with both walk and trot gaits, choosing the footholds online. The achieved results improved the robustness and the performance of the quadruped locomotion. High-frequency replanning, dealing with a fixed goal, recovering after a push, and the automatic selection of footholds could help the robots to accomplish important tasks for the humans, for example, providing support in a disaster response scenario or inspecting an unknown environment. In the future, the contact planning will be transferred to the real hardware. Possible developments foresee the optimization of the gait timings, i.e., stance and swing duration, and a framework which allows the automatic transition between gaits

The development of fire detection robot

Bu tez çalışmasının amacı; özellikle endüstriyel alanlarda, erken yangın algılamada kullanılacak bir yangın algılama robotu tasarlamak ve imal etmektir. Bu robot; önceden belirlenen sanal güzergâh üzerinde engel algılama fonksiyonuyla ve yeniden programlanabilir hareket ünitesiyle devriye gezebilecek ve yangın kaynağını tespit edebilmek için ortam taraması yapabilecek şekilde tasarlanmış ve imal edilmiştir. Sistem; hareket planlama ünitesine tanımlanan programlar ile değişken devriye güzergâhlarını takip edebilme yeteneğine sahiptir. Robotun tasarım ve uygulama süreçleri şu şekildedir; mekanik sistemin tasarımı ve geliştirilmesi, elektronik sistemin tasarımı ve geliştirilmesi ve gerekli yazılımların hazırlanmasıdır. Mekanik sistemin tasarım ve geliştirilme sürecinde; taslak çizimleri, ölçülendirmeler ve üç boyutlu modelleme için bilgisayar destekli tasarım ve katı modelleme programları kullanılmıştır. Robotun taşıyıcı gövdesi; ucuz, sağlam ve kolay işlenebilir malzemeler olan ahşap ve sert plastik köpük kullanılarak imal edilmiştir. Robot sürüş sisteminde diferansiyel metot kullanılmıştır. Yarı otomatik robot dört adet fırçalı doğru akım motoru ile çalışmaktadır. Elektronik sistemin tasarımı ve geliştirilmesi sürecinde; hazır kart almak yerine ihtiyaca uygun elektronik veri kazanım ve kontrol devreleri tasarlanıp üretilmiştir. Bu devrelerin şematik diyagramı ve baskı devresi Proteus elektronik tasarım programı kullanılarak hazırlanmıştır. Bu devreler; motor hareketlerini kontrol etmekte ve dizüstü bilgisayar ile algılama üniteleri arasında bir köprü kurmakta kullanılmıştır. Yazılımların hazırlanma sürecinde; engel algılamada ve güzergâh takibinde kullanılacak akıllı yazılımlar geliştirilmiştir. Ayrıca daha güvenilir yangın algılama sağlamak için; çoklu sensör algılama ve değerlendirme algoritması geliştirilmiştir. Bu tezin sonucunda; özellikle endüstriyel alanlarda kullanılabilecek, çeşitli fonksiyonlara sahip bir yangın algılama robotu tasarlanıp imal edilmiştir. Yapılan testlerle; sistemin en fazla 100 cm mesafedeki yangını, robot 0,5 m/s hızla ilerlerken tespit edebildiği sonucuna varılmıştır.The aim of this thesis is to design and manufacture a fire detection robot that especially operates in industrial areas for fire inspection and early detection. Robot is designed and implemented to track prescribed paths with obstacle avoidance function through obstacle avoidance and motion planning units and to scan the environment in order to detect fire source using fire detection unit. Robot is able to track patrolling routes using virtual lines that defined to the motion planning unit. The design and implementation processes of the robot are as follow; the design and the development of mechanical, electronic systems and software. The design and the development of mechanical system; for the sketch drawings, dimensioning and solid state modeling of the robot, computer aided design and solid modelling computer programs were used. The carrier board of the robot is produced using wooden material and rigid plastic foam which are cheap, strong enough and easy to manufacture. Differential steering method is selected for semi-autonomous robot driving system and it is powered by four brushed DC (direct current) motors. The design and the development of electronic system; electronic circuits were designed and produced, instead of buying a commercial card. Both schematic diagrams and circuits of the data acquisition and control circuits are designed using Proteus electronic design program. These circuits are used to control the motion of the motors and establish a data flow between the laptop and the other peripheral sensing components. Software development; intelligent algorithms for obstacle avoidance and path tracking have been developed. A sensor data fusion algorithm for the sensors was also developed to get more reliable fire detection information. In conclusion; a fire inspection and detection robot with various functions to especially can be used in industrial areas was designed and manufactured. The functions of the robot were tested. It can be concluded that system is able to detect the fire source maximum 100 cm distance away while robot is moving with 0.5 m/s forward speed

Locomotion and pose estimation in compliant, torque-controlled hexapedal robots

Several scenarios, such as disaster response or terrestrial and extra-terrestrial exploration, comprise environments that are dangerous or even inaccessible for humans. In those cases, autonomous robots pose a promising alternative to render such endeavours possible. While most of today’s robotic explorers are wheeled or tracked vehicles, legged systems gained increased attention in recent years. With their unique combination of omnidirectional mobility and intrinsic manipulation capabilities, they are envisioned to serve as the rough terrain specialists in scouting or sample and return missions. Especially, small to mid-size hexapods are of great interest for those scenarios. Providing static stability across a wide range of walking speeds, they offer an attractive trade-off between versatility and complexity. Another important advantage is their redundancy, allowing them to tolerate the loss of single legs. However, due to their small size, the computational on-board resources are limited. Thus, the use of smart and efficient algorithms is of utmost importance in order to enable autonomous operation within a priori unknown rough environments. Working towards such autonomous robotic scouts, this thesis contributes with the development, implementation, and test of a self-contained walking layer as well as a 6 degrees of freedom (DOF) leg odometry for compliant, torque-controlled, hexapedal robots. Herein, the important property of all presented algorithms is the sole use of proprioceptive measurements provided by the legs, i. e. joint angles and joint torques. Especially the joint torque sensors improve the walking process by enabling the use of sensitive compliance controllers and distributed collision detection. Comprising a set of algorithms, the walking layer organises and structures the walking process in order to generate robust, adaptive, and leg loss tolerant locomotion in uneven terrain. Furthermore, it encapsulates the walking process, and thus hides its complexity from higher-level algorithms such as navigation. Its three main functional components are a flexible, biologically-inspired gait coordination algorithm, single leg reflexes, and active joint compliance control. Thereof, the gait coordination algorithm realises temporal adaptation of the step sequence while reflexes adjust the leg trajectories to the local terrain. The joint compliance control reduces internal forces and allows for situation dependent stiffness adjustments. An algorithmic extensions to the basic gait coordination enables the immediate adaptation to leg loss. In combination with stiffness and pose adjustments, this allows the hexapod to retain stable locomotion on five legs. In order to account for the emerging gait, the leg odometry algorithm employs an optimisation approach to obtain a kinematics-based pose estimate from joint angle measurements. Fusing the resulting pitch and roll angle estimates with joint-torque-measurement-based attitude data, reduces the associated drift, and thus stabilises the overall pose estimate. Various simulations and experiments with the six-legged, torque-controlled DLR Crawler demonstrate the effectiveness of the proposed walking layer as well as the 6-DOF leg odometry.Für die planetare Exploration sowie den Einsatz in Katastrophengebieten sind autonome Laufroboter zunehmend von Interesse. In diesen Szenarien sollen sie den Menschen an gefährlichen oder schwer zugänglichen Orten ersetzen und dort Erkundungseinsätze sowie Probenahmen in schwierigem Gelände durchführen. Unter der Vielzahl an möglichen Systemen bieten im Besonderen kleinere Sechsbeiner einen sehr guten Kompromiss zwischen Stabilität, hoher Beweglichkeit, Vielseitigkeit und einer vertretbaren Komplexität der Regelung. Ein weiterer Vorteil ist ihre Redundanz, die es ihnen erlaubt, den Ausfall einzelner Beine mit geringem Aufwand zu kompensieren. Dementgegen ist die beschränkte Rechenkapazität ein Nachteil der reduzierten Größe. Um diesen auszugleichen und das autonome Agieren in einer unbekannten Umgebung zu ermöglichen, werden daher einfache und effiziente Algorithmen benötigt, die im Zusammenspiel jedoch ein komplexes Verhalten erzeugen. Auf dem Weg zum autonom explorierenden Laufroboter entwickelt diese Arbeit einen robusten, adaptiven und fehlertoleranten Laufalgorithmus sowie eine 6D Eigenbewegungsschätzung für nachgiebige, drehmomentgeregelte Sechsbeiner. Besonders herauszustellen ist, dass alle in der Arbeit vorgestellten Algorithmen ausschließlich die propriozeptive Sensorik der Beine verwenden. Durch diesen Ansatz kann der Laufprozess von anderen Prozessen, wie der Navigation, getrennt und somit der Datenaustausch effizient gestaltet werden. Für die Fortbewegung in unebenem Gelände kombiniert der vorgestellte Laufalgorithmus eine flexible, biologisch inspirierte Gangkoordination mit verschiedenen Einzelbeinreflexen und einer nachgiebigen Gelenkregelung. Hierbei übernimmt die Gangkoordination die zeitliche Steuerung der Schrittfolge, während die Einzelbeinreflexe für eine räumliche Variation der Fußtrajektorien zuständig sind. Die nachgiebige Gelenkregelung reduziert interne Kräfte und erlaubt eine Anpassung der Gelenksteifigkeiten an die lokalen Umgebungsbedingungen sowie den aktuellen Zustand des Roboters. Eine wichtige Eigenschaft des Laufalgorithmus ist seine Fähigkeit, den Ausfall einzelner Beine zu kompensieren. In diesem Fall erfolgt eine Adaption der Gangkoordination über die Erneuerung der Nachbarschaftsbeziehungen der Beine. Zusätzlich verbessern eine Veränderung der Pose und eine Erhöhung der Gelenksteifigkeiten die Stabilität des durch den Beinverlust beeinträchtigten Roboters. Gleich dem Laufalgorithmus verwendet die 6D Eigenbewegungsschätzung nur die Messungen der propriozeptiven Sensoren der Beine. Hierbei arbeitet der Algorithmus in einem dreistufigen Verfahren. Zuerst berechnet er mit Hilfe der Beinkinematik und einer Optimierung die Pose des Roboters. Nachfolgend bestimmt er aus den Gelenkmomentmessungen den Gravitationsvektor und berechnet daraus die Neigungswinkel des Systems. Eine Fusion dieser Werte mit den Nick- und Rollwinkeln der ersten Stufe stabilisiert daraufhin die gesamte Odometrie und reduziert deren Drift. Alle in dieser Arbeit entwickelten Algorithmen wurden mit Hilfe von Simulationen sowie Experimenten mit dem drehmomentgeregelten DLR Krabbler erfolgreich validiert

Bridging Vision and Dynamic Legged Locomotion

Legged robots have demonstrated remarkable advances regarding robustness and versatility in the past decades. The questions that need to be addressed in this field are increasingly focusing on reasoning about the environment and autonomy rather than locomotion only. To answer some of these questions visual information is essential. If a robot has information about the terrain it can plan and take preventive actions against potential risks. However, building a model of the terrain is often computationally costly, mainly because of the dense nature of visual data. On top of the mapping problem, robots need feasible body trajectories and contact sequences to traverse the terrain safely, which may also require heavy computations. This computational cost has limited the use of visual feedback to contexts that guarantee (quasi-) static stability, or resort to planning schemes where contact sequences and body trajectories are computed before starting to execute motions. In this thesis we propose a set of algorithms that reduces the gap between visual processing and dynamic locomotion. We use machine learning to speed up visual data processing and model predictive control to achieve locomotion robustness. In particular, we devise a novel foothold adaptation strategy that uses a map of the terrain built from on-board vision sensors. This map is sent to a foothold classifier based on a convolutional neural network that allows the robot to adjust the landing position of the feet in a fast and continuous fashion. We then use the convolutional neural network-based classifier to provide safe future contact sequences to a model predictive controller that optimizes target ground reaction forces in order to track a desired center of mass trajectory. We perform simulations and experiments on the hydraulic quadruped robots HyQ and HyQReal. For all experiments the contact sequences, the foothold adaptations, the control inputs and the map are computed and processed entirely on-board. The various tests show that the robot is able to leverage the visual terrain information to handle complex scenarios in a safe, robust and reliable manner