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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.

Calibration and accuracy validation of a FANUC LR Mate 200iC industrial robot

Author  Ilian Bonev

Video ID : 430

This video shows excerpts from the process of calibrating a FANUC LR Mate 200iC industrial robot using two different methods. In the first method, the position of one of three points on the robot end-effector is measured using a FARO laser tracker in 50 specially selected robot configurations (not shown in the video). Then, the robot parameters are identified. Next, the position of one of the three points on the robot's end-effector is measured using the laser tracker in 10,000 completely arbitrary robot configurations. The mean positioning error after calibration was found to be 0.156 mm, the standard deviation (std) 0.067 mm, the mean+3*std 0.356 mm, and the maximum 0.490 mm. In the second method, the complete pose (position and orientation) of the robot end-effector is measured in about 60 robot configurations using an innovative method based on Renishaw's telescoping ballbar. Then, the robot parameters are identified. Next, the position of one of the three points on the robot's end-effector is measured using the laser tracker in 10,000 completely arbitrary robot configurations. The mean position error after calibration was found to be 0.479 mm, the standard deviation (std) 0.214 mm, and the maximum 1.039 mm. However, if we limit the zone for validations, the accuracy of the robot is much better. The second calibration method is less efficient but relies on a piece of equipment that costs only $12,000 (only one tenth the cost of a laser tracker).

Chapter 53 — Multiple Mobile Robot Systems

Lynne E. Parker, Daniela Rus and Gaurav S. Sukhatme

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Miniature air-vehicle, cooperative timing missions

Author  Tim McLain, Randy Beard

Video ID : 207

Small UAVs are used to demonstrate cooperation, including in aerial maneuvers such as flying in formation, intersecting at a point, and landing simultaneously.

Chapter 18 — Parallel Mechanisms

Jean-Pierre Merlet, Clément Gosselin and Tian Huang

This chapter presents an introduction to the kinematics and dynamics of parallel mechanisms, also referred to as parallel robots. As opposed to classical serial manipulators, the kinematic architecture of parallel robots includes closed-loop kinematic chains. As a consequence, their analysis differs considerably from that of their serial counterparts. This chapter aims at presenting the fundamental formulations and techniques used in their analysis.

IPAnema

Author  Andreas Pott

Video ID : 50

This video demonstrates a fully constrained cable-driven parallel robot with eight cables. Reference: A. Pott, H. Mütherich, W. Kraus, V. Schmidt, P. Miermeister, A. Verl: IPAnema: A family of cable-driven parallel robots for industrial applications, Mech. Mach. Sci. 12, 119-134 (2013)

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.

Controversial comments

Author  James P. Trevelyan

Video ID : 585

In this video from RT, the Russia-sponsored, English- language, news channel, Kevin Kamps from Beyond Nuclear claims that radiation levels at Fukushima are "too high" even for robots, which is only partly true. He goes on to claim that "countless thousands of emergency workers died from radiation exposure", claims which are contradicted by a large WHO study published in 2005. Unfortunately, there are many videos with doubtful claims, and one needs to be careful in searching for evidence. This video has been included in the collection to remind researchers to be cautious when evaluating evidence available from public-domain, video sources.

Chapter 18 — Parallel Mechanisms

Jean-Pierre Merlet, Clément Gosselin and Tian Huang

This chapter presents an introduction to the kinematics and dynamics of parallel mechanisms, also referred to as parallel robots. As opposed to classical serial manipulators, the kinematic architecture of parallel robots includes closed-loop kinematic chains. As a consequence, their analysis differs considerably from that of their serial counterparts. This chapter aims at presenting the fundamental formulations and techniques used in their analysis.

CoGiRo

Author  Marc Gouttefarde

Video ID : 45

This video demonstrates a 6-DOF fully constrained 8-cable-driven robot acting in a large workspace on palletizing applications (CoGiRo robot). Reference: J. Lamaury, M. Gouttefarde: Control of a large redundantly actuated cable-suspended parallel robot, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Karlsruhe (2013), pp. 4659-4664

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.

Nuclear manipulator, remote-handling equipment (1960)

Author  James P. Trevelyan

Video ID : 588

Demonstration video showing the pouring of a cup of tea – illustrates the dexterity of these popular manipulators which are ubiquitous in nuclear laboratories.

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.

An assistive, decision-and-control architecture for force-sensitive, hand–arm systems driven by human–machine interfaces (MM3)

Author  Jörn Vogel, Sami Haddadin, John D. Simeral, Daniel Bacher , Beata Jarosiewicz, Leigh R. Hochberg, John P. Donoghue, Patrick van der Smagt

Video ID : 621

This video shows a 3-D reach and grasp experiment using the Braingate2 neural interface system. The robot is controlled through a multipriority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this a decision-and-control architecture, which uses sensory information available from the robotic system to evaluate the current state of task execution, is employed. Available assistive skills of the robotic system are not actively helping in this task but they are used to evaluate task success.

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.

The Nerd Herd

Author  Maja J. Mataric

Video ID : 34

This is a video showing the work done in the early 1990s with the Nerd Herd used as a multirobot behavior-based system. Reference: M.J. Matarić: Designing and understanding adaptive group behavior, Adapt. Behav. 4(1), 50–81 (1995)