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Chapter 6 — Model Identification

John Hollerbach, Wisama Khalil and Maxime Gautier

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

Dynamic identification of Kuka KR270 : Trajectory with load

Author  Maxime Gautier

Video ID : 487

This video shows a trajectory with a known payload mass used to identify the dynamic parameters of the links, load, joint drive gains and gravity compensator of a heavy industrial Kuka KR 270 manipulator Details and results are given in the paper: A. Jubien, M. Gautier, Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013), pp. 1355-1360

Chapter 62 — Intelligent Vehicles

Alberto Broggi, Alex Zelinsky, Ümit Özgüner and Christian Laugier

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology. Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure. Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles. Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance. Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment. Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed. The chapter is concluded in Sect. 62.7 with a discussion of future prospects, while Sect. 62.8 provides references to further reading and additional resources.

Cybercars and the city of tomorrow

Author  Christian Laugier, Michel Parent, Inria Multimedia

Video ID : 429

The video presents an overview of the CityMobil European Project and of the related concept of cybercars.

Chapter 13 — Behavior-Based Systems

François Michaud and Monica Nicolescu

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creature’s ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to explain behavior-based systems and their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 13.3. Section 13.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multirobot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

SpartacUS

Author  François Michaud

Video ID : 417

AAAI 2005 Robot Challenge entry from the Université de Sherbrooke, named Spartacus, using MBA (motivated behavioral architecture) to enable a robot to participate at the conference as a regular attendee. Reference: F. Michaud, C. Côté, D. Létourneau, Y. Brosseau, J.-M. Valin, É. Beaudry, C. Raïevsky, A. Ponchon, P. Moisan, P. Lepage, Y. Morin, F. Gagnon, P. Giguère, M.-A. Roux, S. Caron, P. Frenette, F. Kabanza: Spartacus attending the 2005 AAAI Conference, Auton. Robot. 12(2), 211–222 (2007)

Chapter 58 — Robotics in Hazardous Applications

James Trevelyan, William R. Hamel and Sung-Chul Kang

Robotics researchers have worked hard to realize a long-awaited vision: machines that can eliminate the need for people to work in hazardous environments. Chapter 60 is framed by the vision of disaster response: search and rescue robots carrying people from burning buildings or tunneling through collapsed rock falls to reach trapped miners. In this chapter we review tangible progress towards robots that perform routine work in places too dangerous for humans. Researchers still have many challenges ahead of them but there has been remarkable progress in some areas. Hazardous environments present special challenges for the accomplishment of desired tasks depending on the nature and magnitude of the hazards. Hazards may be present in the form of radiation, toxic contamination, falling objects or potential explosions. Technology that specialized engineering companies can develop and sell without active help from researchers marks the frontier of commercial feasibility. Just inside this border lie teleoperated robots for explosive ordnance disposal (EOD) and for underwater engineering work. Even with the typical tenfold disadvantage in manipulation performance imposed by the limits of today’s telepresence and teleoperation technology, in terms of human dexterity and speed, robots often can offer a more cost-effective solution. However, most routine applications in hazardous environments still lie far beyond the feasibility frontier. Fire fighting, remediating nuclear contamination, reactor decommissioning, tunneling, underwater engineering, underground mining and clearance of landmines and unexploded ordnance still present many unsolved problems.

Robots answer nuclear-waste challenges at SRS

Author  James P. Trevelyan

Video ID : 593

Promotional video to reassure citizens that earnest efforts are being made to remove historical nuclear contamination from their neighbourhood. It is not clear how effective the robotic devices were in practice, however.

Chapter 23 — Biomimetic Robots

Kyu-Jin Cho and Robert Wood

Biomimetic robot designs attempt to translate biological principles into engineered systems, replacing more classical engineering solutions in order to achieve a function observed in the natural system. This chapter will focus on mechanism design for bio-inspired robots that replicate key principles from nature with novel engineering solutions. The challenges of biomimetic design include developing a deep understanding of the relevant natural system and translating this understanding into engineering design rules. This often entails the development of novel fabrication and actuation to realize the biomimetic design.

This chapter consists of four sections. In Sect. 23.1, we will define what biomimetic design entails, and contrast biomimetic robots with bio-inspired robots. In Sect. 23.2, we will discuss the fundamental components for developing a biomimetic robot. In Sect. 23.3, we will review detailed biomimetic designs that have been developed for canonical robot locomotion behaviors including flapping-wing flight, jumping, crawling, wall climbing, and swimming. In Sect. 23.4, we will discuss the enabling technologies for these biomimetic designs including material and fabrication.

Jumping-and-landing robot MOWGLI

Author  Ryuma Niiyama, Akihiko Nagakubo, Yasuo Kuniyoshi

Video ID : 285

In this research, we developed a bipedal robot with an artificial musculoskeletal system. Here, we present an approach to realize motor control of jumping and landing that exploits the synergy between control and mechanical structure. Our experimental system is a bipedal robot called MOWGLI. This video shows a jumping-onto-a-chair experiment to a height of 0.4 m. MOWGLI can reach heights of more than 50 % of its body height and can land softly. As a multiple-DOF legged robot, this performance is extremely high. Our results show a proximo-distal sequence of joint extensions during jumping despite simultaneous motor activity. In addition to the experiments with the real robot, the simulation results demonstrate the contribution of the artificial musculoskeletal system as a physical feedback loop in explosive movements.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

Admittance control of a human-centered 3-DOF robotic arm using dfferential elastic actuators

Author  Marc-Antoine Legault, Marc-Antoine Lavoie, Francois Cabana, Philippe Jacob-Goudreau, Dominic Létourneau, François Michaud

Video ID : 610

This video shows the functionalities of a three-serial-DOF robotic arm where each DOF is actuated with a patent-pending differential elastic actuator (DEA). A DEA uses differential coupling between a high-impedance mechanical speed source and a low-impedance mechanical spring. A passive torsion spring (thus the name elastic), with a known impedance characteristic corresponding to the spring stiffness, is used, with an electrical DC brushless motor. A non-turning sensor connected in series with the spring measures the torque output of the actuator. Reference: M.-A. Legault, M.-A. Lavoie, F. Cabana, P. Jacob-Goudreau, D. Létourneau, F. Michaud: Admittance control of a human centered 3-DOF robotic arm using differential elastic actuators , Proc. IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Nice (2008), pp. 4143–4144; doi: 10.1109/IROS.2008.4651039.

Human-robot handover

Author  Wesley P. Chan, Chris A. Parker, H.F.Machiel Van der Loos, Elizabeth A. Croft

Video ID : 716

In this video, we present a novel controller for safe, efficient, and intuitive robot-to-human object handovers. The controller enables a robot to mimic human behavior by actively regulating the applied grip force according to the measured load force during a handover. We provide an implementation of the controller on a Willow Garage PR2 robot, demonstrating the feasibility of realizing our design on robots with basic sensor/actuator capabilities.

Chapter 76 — Evolutionary Robotics

Stefano Nolfi, Josh Bongard, Phil Husbands and Dario Floreano

Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.

Introduction to evolutionary robotics at EPFL

Author  Dario Floreano

Video ID : 119

Method for evolving the neural network of a robot. Valid gene sequences are extracted (magnifying lens) from a binary string representing the genome of the robot. Those genes are translated into neurons of different types (colors) according to the genetic specifications, such as sensory, motor, excitatory, or inhibitory neurons. The corresponding neural network is connected to the sensors and motors of the robot and the resulting behavior of the robot is measured according to the fitness function. The genomes of the individuals that had the worst performance are discarded from the population (symbolically thrown into a dustbin) whereas the genomes of the best individuals are paired and crossed over with small random mutations to generate new offspring (the process of selective reproduction is symbolically shown to occur in a mother robot). After several generations of selective reproductions with mutations, robots display better or novel behaviors.

Chapter 64 — Rehabilitation and Health Care Robotics

H.F. Machiel Van der Loos, David J. Reinkensmeyer and Eugenio Guglielmelli

The field of rehabilitation robotics considers robotic systems that 1) provide therapy for persons seeking to recover their physical, social, communication, or cognitive function, and/or that 2) assist persons who have a chronic disability to accomplish activities of daily living. This chapter will discuss these two main domains and provide descriptions of the major achievements of the field over its short history and chart out the challenges to come. Specifically, after providing background information on demographics (Sect. 64.1.2) and history (Sect. 64.1.3) of the field, Sect. 64.2 describes physical therapy and exercise training robots, and Sect. 64.3 describes robotic aids for people with disabilities. Section 64.4 then presents recent advances in smart prostheses and orthoses that are related to rehabilitation robotics. Finally, Sect. 64.5 provides an overview of recent work in diagnosis and monitoring for rehabilitation as well as other health-care issues. The reader is referred to Chap. 73 for cognitive rehabilitation robotics and to Chap. 65 for robotic smart home technologies, which are often considered assistive technologies for persons with disabilities. At the conclusion of the present chapter, the reader will be familiar with the history of rehabilitation robotics and its primary accomplishments, and will understand the challenges the field may face in the future as it seeks to improve health care and the well being of persons with disabilities.

Targeted reinnervation and the DEKA Arm

Author  Rehabilitation Institute of Chicago

Video ID : 513

Claudia Mitchell, 28, of Arkansas, demonstrates advanced, multidegree control of the DEKA Research arm at The Rehabilitation Institute of Chicago. Mitchell, who lost her arm in a motorcycle accident in 2004, underwent targeted muscle reinnervation in 2005. Video courtesy of the Rehabilitation Institute of Chicago and DEKA Research. Learn more at www.ric.org/bionic.

Chapter 27 — Micro-/Nanorobots

Bradley J. Nelson, Lixin Dong and Fumihito Arai

The field of microrobotics covers the robotic manipulation of objects with dimensions in the millimeter to micron range as well as the design and fabrication of autonomous robotic agents that fall within this size range. Nanorobotics is defined in the same way only for dimensions smaller than a micron. With the ability to position and orient objects with micron- and nanometer-scale dimensions, manipulation at each of these scales is a promising way to enable the assembly of micro- and nanosystems, including micro- and nanorobots.

This chapter overviews the state of the art of both micro- and nanorobotics, outlines scaling effects, actuation, and sensing and fabrication at these scales, and focuses on micro- and nanorobotic manipulation systems and their application in microassembly, biotechnology, and the construction and characterization of micro and nanoelectromechanical systems (MEMS/NEMS). Material science, biotechnology, and micro- and nanoelectronics will also benefit from advances in these areas of robotics.

High-speed magnetic microrobot actuation in a microfluidic chip by a fine V-groove surface

Author  Fumihito Arai

Video ID : 491

This video shows high-speed microrobotic actuation driven by permanent magnets in a microfluidic chip. The microrobot has a milliNewton-level output force from a permanent magnet, micrometer-level positioning accuracy, and drive speed of over 280 mm/s. The riblet surface, which is a regularly arrayed V-groove, reduces fluid friction and enables high-speed actuation. Ni- and Si-composite fabrication was employed to form the optimum riblet shape on the microrobot’s surface by wet and dry etching. The evaluation experiments show that the microrobot can be actuated at a rate of up to 90 Hz, which is more than ten times higher than that of the microrobot without a riblet.