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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.

Swarm construction robots

Author  Radhika Nagpal

Video ID : 216

This video describes produced at Harvard's Wyss Institute for Biologically Inspired Engineering, showing the development of swarm robots for construction. These robots follow the biological principles underlying insect swarms to achieve their constructions. The robots follow local control roles that, together with traffic control laws, guarantee the building of desired structures.

Chapter 10 — Redundant Robots

Stefano Chiaverini, Giuseppe Oriolo and Anthony A. Maciejewski

This chapter focuses on redundancy resolution schemes, i. e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes.

In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different firstorder methods to solve kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue when a redundant manipulator is used to execute a repetitive task, e.g., in industrial applications. The use of kinematic redundancy for fault tolerance is analyzed in detail. Suggestions for further reading are given in a final section.

Human robot arm with redundancy resolution

Author  PRISMA Lab

Video ID : 816

In this video, the mapping of human-arm motion to an anthropomorphic robot arm (7-DOF Kuka LWR ) using Xsens MVN is demonstrated. The desired end-effector trajectories of the robot are reconstructed from the human hand, forearm and upper arm trajectories in the Cartesian space obtained from the motion tracking system by means of human-arm biomechanical models and sensor-fusion algorithms embedded in the Xsens technology. The desired pose of the robot is reconstructed taking into account the differences between the robot and human-arm kinematics and is obtained by suitably scaling to the human-arm link dimensions.

Chapter 70 — Human-Robot Augmentation

Massimo Bergamasco and Hugh Herr

The development of robotic systems capable of sharing with humans the load of heavy tasks has been one of the primary objectives in robotics research. At present, in order to fulfil such an objective, a strong interest in the robotics community is collected by the so-called wearable robots, a class of robotics systems that are worn and directly controlled by the human operator. Wearable robots, together with powered orthoses that exploit robotic components and control strategies, can represent an immediate resource also for allowing humans to restore manipulation and/or walking functionalities.

The present chapter deals with wearable robotics systems capable of providing different levels of functional and/or operational augmentation to the human beings for specific functions or tasks. Prostheses, powered orthoses, and exoskeletons are described for upper limb, lower limb, and whole body structures. State-of-theart devices together with their functionalities and main components are presented for each class of wearable system. Critical design issues and open research aspects are reported.

Hand exoskeletons

Author  Massimo Bergamasco

Video ID : 150

The video shows the hand exoskeletons, highlighting the mechanism adopted for implementing the finger kinematic and the specifically-designed force sensors integrated into the mechanical structure.

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.

Agents at play: Off-the-shelf software for practical multi-robot applications

Author  Enric Cervera, Jorge Sales, Leo Nomdedeu, Raul Marin, Veysel Gazi

Video ID : 192

This video focuses on how to use off-the-shelf components to design multirobot systems for real-world applications. The system makes use of Player and JADE as middleware, integrated using Java. The application that illustrates this system requires robots to visit destinations in an indoor environment, making use of market-based task allocation.

Chapter 72 — Social Robotics

Cynthia Breazeal, Kerstin Dautenhahn and Takayuki Kanda

This chapter surveys some of the principal research trends in Social Robotics and its application to human–robot interaction (HRI). Social (or Sociable) robots are designed to interact with people in a natural, interpersonal manner – often to achieve positive outcomes in diverse applications such as education, health, quality of life, entertainment, communication, and tasks requiring collaborative teamwork. The long-term goal of creating social robots that are competent and capable partners for people is quite a challenging task. They will need to be able to communicate naturally with people using both verbal and nonverbal signals. They will need to engage us not only on a cognitive level, but on an emotional level as well in order to provide effective social and task-related support to people. They will need a wide range of socialcognitive skills and a theory of other minds to understand human behavior, and to be intuitively understood by people. A deep understanding of human intelligence and behavior across multiple dimensions (i. e., cognitive, affective, physical, social, etc.) is necessary in order to design robots that can successfully play a beneficial role in the daily lives of people. This requires a multidisciplinary approach where the design of social robot technologies and methodologies are informed by robotics, artificial intelligence, psychology, neuroscience, human factors, design, anthropology, and more.

Influence of response time

Author  Takayuki Kanda

Video ID : 806

This video illustrates the importance of response time in interactions with a social robot. In the first part of the study, it was revealed that it is hard to wait for more than two seconds. In the second part of the study, a technique to use a "conversational filler" is developed, which moderates the frustrations of waiting too long.

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.

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.

Exploration and homing for battery recharge

Author  Dario Floreano

Video ID : 118

Evolved Khepera robot performing exploration and homing for battery recharge. The robot enters the recharging area approximately 2 s before full-battery discharge.

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 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.

PT-400 D:Mine

Author  James P. Trevelyan

Video ID : 576

This video shows another remotely operated demining machine similar in principle to the BOZENA model (Video 574). The video shows the machine operating only on flat terrain.

Chapter 36 — Motion for Manipulation Tasks

James Kuffner and Jing Xiao

This chapter serves as an introduction to Part D by giving an overview of motion generation and control strategies in the context of robotic manipulation tasks. Automatic control ranging from the abstract, high-level task specification down to fine-grained feedback at the task interface are considered. Some of the important issues include modeling of the interfaces between the robot and the environment at the different time scales of motion and incorporating sensing and feedback. Manipulation planning is introduced as an extension to the basic motion planning problem, which can be modeled as a hybrid system of continuous configuration spaces arising from the act of grasping and moving parts in the environment. The important example of assembly motion is discussed through the analysis of contact states and compliant motion control. Finally, methods aimed at integrating global planning with state feedback control are summarized.

Learning to place new objects

Author  Yun Jiang et al.

Video ID : 370

The video shows how to a robot learns to place objects stably in preferred locations. Four different tasks are performed: 1) loading a refrigerator, 2) loading a bookshelf, 3) cleaning a table, and 4) loading dish-racks.