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Chapter 56 — Robotics in Agriculture and Forestry

Marcel Bergerman, John Billingsley, John Reid and Eldert van Henten

Robotics for agriculture and forestry (A&F) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.

Autonomous orchard vehicle for specialty-crop production

Author  Sanjiv Singh, Marcel Bergerman

Video ID : 91

In the United States, production of specialty crops (fruits and vegetables, tree nuts, dried fruits and horticulture and nursery crops, including floriculture) is very labor-intensive. The autonomous orchard vehicle presented in this video can be used year-round to automate tasks such as mowing, spraying, scouting for disease or insects, and estimating crop yield; or to augment humans for pruning, thinning, training trees, placing pheromone dispensers, and harvesting. Studies by the extension teams at The Pennsylvania and Washington State Universities report an increase in efficiency of up to 116% when workers perform operations on the upper part of trees onboard the vehicle, as compared to workers using ladders.

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.

BONES and SUE exoskeletons for robotic therapy

Author  Julius Klein, Steve Spencer, James Allington, Marie-Helene Milot, Jim Bobrow, David Reinkensmeyer

Video ID : 498

BONES is a 5-DOF, pneumatic robot developed at the University of California at Irvine for naturalistic arm training after stroke. It incorporates an assistance-as-needed algorithm that adapts in real time to patient errors during game play by developing a computer model of the patient's weakness as a function of workspace location. The controller incorporates an anti-slacking term. SUE is a 2-DOF pneumatic robot for providing wrist assistance. The video shows a person with a stroke using the device to drive a simulated motor cycle through a simulated Death Valley.

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 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.

Autonomous continuum grasping

Author  Jing Xiao et al.

Video ID : 357

The video shows three example tasks: (1) autonomous grasping and lifting operation of an object, (2) autonomous obstacle avoidance operation, and (3) autonomous operation of grasping and lifting an object while avoiding another object. Note that the grasped object was lifted about 2 inches off the table.

Chapter 20 — Snake-Like and Continuum Robots

Ian D. Walker, Howie Choset and Gregory S. Chirikjian

This chapter provides an overview of the state of the art of snake-like (backbones comprised of many small links) and continuum (continuous backbone) robots. The history of each of these classes of robot is reviewed, focusing on key hardware developments. A review of the existing theory and algorithms for kinematics for both types of robot is presented, followed by a summary ofmodeling of locomotion for snake-like and continuum mechanisms.

Two-dimensional binary manipulator

Author  Greg Chirikjian

Video ID : 160

Greg Chirikjian's binary manipulator operating in two dimensions.

Chapter 21 — Actuators for Soft Robotics

Alin Albu-Schäffer and Antonio Bicchi

Although we do not know as yet how robots of the future will look like exactly, most of us are sure that they will not resemble the heavy, bulky, rigid machines dangerously moving around in old fashioned industrial automation. There is a growing consensus, in the research community as well as in expectations from the public, that robots of the next generation will be physically compliant and adaptable machines, closely interacting with humans and moving safely, smoothly and efficiently - in other terms, robots will be soft.

This chapter discusses the design, modeling and control of actuators for the new generation of soft robots, which can replace conventional actuators in applications where rigidity is not the first and foremost concern in performance. The chapter focuses on the technology, modeling, and control of lumped parameters of soft robotics, that is, systems of discrete, interconnected, and compliant elements. Distributed parameters, snakelike and continuum soft robotics, are presented in Chap. 20, while Chap. 23 discusses in detail the biomimetic motivations that are often behind soft robotics.

Introducing WildCat

Author  Boston Dynamics

Video ID : 458

WildCat is a four-legged robot being developed to run fast on all types of terrain. So far WildCat has run at about 16 mph on flat terrain using bounding and galloping gaits. The video shows WildCat's best performance so far. WildCat is being developed by Boston Dynamics with funding from DARPA's M3 program. For more information about WildCat visit our website at www.BostonDynamics.com.

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.

MARS (multiple autonomous robots)

Author  Camillo J. Taylor, Vijay Kumar

Video ID : 204

The goal of the research is to develop a framework and the support tools for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications to reconnaissance, surveillance, target acquisition, and the removal of explosive ordnance. The current state of the art in control software enables supervised autonomy, a paradigm in which a human user can command and control one robot using teleoperation and close supervisory control. The objective here is to develop the software framework and tools for a new generation of autonomous robots.

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.

Reducing uncertainty in robotics surface-assembly tasks

Author  Jing Xiao et al.

Video ID : 356

This video demonstrates how surface assembly strategies with pose estimation can be used to overcome pose uncertainties. The assembly path is updated based on the newly estimated values of parameters after the compliant exploratory move. In this way, the robot is able to successfully overcome disparities between the nominal and the actual poses of the objects to accomplish the assembly. No force sensor is used.

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.

Visual navigation of mobile robot with pan-tilt camera

Author  Dario Floreano

Video ID : 36

A mobile robot with a pan-tilt camera is asked to to navigate in a square arena with low walls and located in an office.

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.

The ArmeoSpring Therapy Exoskeleton

Author  Hocoma, A.G.

Video ID : 502

The ArmeoSpring Therapy Exoskeleton is a widely-used arm- and-hand training exoskeleton manufactured by Hocoma which provides anti-gravity support and can sense trace-grasp force. It is based on the T-WREX device developed at the University of California at Irvine, which in turn was based in part of the WREX arm exoskeleton developed at the A.I. Dupont Hospital for Children.