View Chapter

Chapter 41 — Active Manipulation for Perception

Anna Petrovskaya and Kaijen Hsiao

This chapter covers perceptual methods in which manipulation is an integral part of perception. These methods face special challenges due to data sparsity and high costs of sensing actions. However, they can also succeed where other perceptual methods fail, for example, in poor-visibility conditions or for learning the physical properties of a scene.

The chapter focuses on specialized methods that have been developed for object localization, inference, planning, recognition, and modeling in activemanipulation approaches.We concludewith a discussion of real-life applications and directions for future research.

Modeling articulated objects using active manipulation

Author  Juergen Strum

Video ID : 78

The video illustrates a mobile, manipulation robot that interacts with various articulated objects, such as a fridge and a dishwasher, in a kitchen environment. During interaction, the robot learns their kinematic properties such as the rotation axis and the configuration space. Knowing the kinematic model of these objects improves the performance of the robot and enables motion planning. Service robots operating in domestic environments are typically faced with a variety of objects they have to deal with to fulfill their tasks. Some of these objects are articulated such as cabinet doors and drawers, or room and garage doors. The ability to deal with such articulated objects is relevant for service robots, as, for example, they need to open doors when navigating between rooms and to open cabinets to pick up objects in fetch-and-carry applications. We developed a complete probabilistic framework that enables robots to learn the kinematic models of articulated objects from observations of their motion. We combine parametric and nonparametric models consistently and utilize the advantages of both methods. As a result of our approach, a robot can robustly operate articulated objects in unstructured environments. All software is available open-source (including documentation and tutorials) on http://www.ros.org/wiki/articulation.

Chapter 0 — Preface

Bruno Siciliano, Oussama Khatib and Torsten Kröger

The preface of the Second Edition of the Springer Handbook of Robotics contains three videos about the creation of the book and using its multimedia app on mobile devices.

Bruno Siciliano — Keynote, February 2017

Author  Bruno Siciliano

Video ID : 847

Bruno Siciliano, Editor of the Springer Handbook of Robotics, gives a keynote during the One SpringerNature event in Barcelona on 7 February 2017.

Chapter 59 — Robotics in Mining

Joshua A. Marshall, Adrian Bonchis, Eduardo Nebot and Steven Scheding

This chapter presents an overview of the state of the art in mining robotics, from surface to underground applications, and beyond. Mining is the practice of extracting resources for utilitarian purposes. Today, the international business of mining is a heavily mechanized industry that exploits the use of large diesel and electric equipment. These machines must operate in harsh, dynamic, and uncertain environments such as, for example, in the high arctic, in extreme desert climates, and in deep underground tunnel networks where it can be very hot and humid. Applications of robotics in mining are broad and include robotic dozing, excavation, and haulage, robotic mapping and surveying, as well as robotic drilling and explosives handling. This chapter describes how many of these applications involve unique technical challenges for field roboticists. However, there are compelling reasons to advance the discipline of mining robotics, which include not only a desire on the part of miners to improve productivity, safety, and lower costs, but also out of a need to meet product demands by accessing orebodies situated in increasingly challenging conditions.

Autonomous loading of fragmented rock

Author  Joshua Marshall

Video ID : 718

This video shows autonomous loading of fragmented rock, first on a 1-t capacity Kubota loader at Kingston, Canada, followed by an implementation on a 14-t capacity Atlas Copco ST14 LHD in an underground mine at Kvarntorp, Sweden. The algorithm used in these demonstrations is based on force-feedback sensed in the loader cylinder pressures and utilizes an admittance control structure.

Chapter 41 — Active Manipulation for Perception

Anna Petrovskaya and Kaijen Hsiao

This chapter covers perceptual methods in which manipulation is an integral part of perception. These methods face special challenges due to data sparsity and high costs of sensing actions. However, they can also succeed where other perceptual methods fail, for example, in poor-visibility conditions or for learning the physical properties of a scene.

The chapter focuses on specialized methods that have been developed for object localization, inference, planning, recognition, and modeling in activemanipulation approaches.We concludewith a discussion of real-life applications and directions for future research.

Tactile localization of a power drill

Author  Kaijen Hsiao

Video ID : 77

This video shows a Barrett WAM arm tactilely localizing and reorienting a power drill under high positional uncertainty. The goal is for the robot to robustly grasp the power drill such that the trigger can be activated. The robot tracks the distribution of possible object poses on the table over a 3-D grid (the belief space). It then selects between information-gathering, reorienting, and goal-seeking actions by modeling the problem as a POMDP (partially observable Markov decision process) and using receding-horizon, forward search through the belief space. In the video, the inset window with the simulated robot is a visualization of the current belief state. The red spheres sit at the vertices of the object mesh placed at the most likely state, and the dark-blue box also shows the location of the most likely state. The purple box shows the location of the mean of the belief state, and the light-blue boxes show the variance of the belief state in the form of the locations of various states that are one standard deviation away from the mean in each of the three dimensions of uncertainty (x, y, and theta). The magenta spheres and arrows that appear when the robot touches the object show the contact locations and normals as reported by the sensors, and the cyan spheres that largely overlap the hand show where the robot controllers are trying to move the hand.

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.

OctArms I-V

Author  Ian Walker

Video ID : 158

Video showing five different iterations of the OctArm continuum manipulator.

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.

Motion prediction using the Bayesian-occupancy-filter approach (Inria)

Author  Christian Laugier, E-Motion Team

Video ID : 420

This video illustrates the prediction capabilities of the Bayesian-occupancy-filter approach which is able to maintain an updated record and estimate of the relatives positions and velocities of an autonomous vehicle and of a detected-and-tracked moving obstacle (e.g., a pedestrian in the video). The approach still works despite temporary obstructions. The method has been patented in, and commercialized since, 2005. More details in [62.60].

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.

PETMAN tests Camo

Author  Boston Dynamics

Video ID : 457

The PETMAN robot was developed by Boston Dynamics with funding from the DoD CBD program. It is used to test the performance of protective clothing designed for hazardous environments. The video shows initial testing in a chemical protection suit and gas mask. PETMAN has sensors embedded in its skin that detect any chemicals leaking through the suit. The skin also maintains a microclimate inside the clothing by sweating and regulating temperature. Partners in developing PETMAN were MRIGlobal, Measurement Technology Northwest, Smith Carter, SRD, CUH2A, and HHI.

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.

Natural interaction design of a humanoid robot

Author  François Michaud

Video ID : 418

Demonstration of the use of HBBA, hybrid behavior-based architecture, to implement three interactional capabilities on IRL-1. Reference: F. Ferland, D. Létourneau, M.-A. Legault, M. Lauria, F. Michaud: Natural interaction design of a humanoid robot, J. Human-Robot Interact. 1(2), 118-134 (2012)

Chapter 46 — Simultaneous Localization and Mapping

Cyrill Stachniss, John J. Leonard and Sebastian Thrun

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main perception problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot’s location. SLAM serves both of these purposes.

We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.

Extended Kalman-filter SLAM

Author  John Leonard

Video ID : 455

This video shows an illustration of Kalman filter SLAM, as described in Chap. 46.3.1, Springer Handbook of Robotics, 2nd edn (2016). References: J.J. Leonard, H. Feder: A computationally efficient method for large-scale concurrent mapping and localization, Proc. Int. Symp. Robot. Res. (ISRR), Salt Lake City (2000), pp. 169–176.

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.

Concentric tube robot at TEDMED 2010

Author  Pierre Dupont

Video ID : 252

This video was recorded at TEDMED 2010 in San Diego and features a teleoperated, concentric tube robot with 1 mm- wide forceps solving a miniature version of the puzzle Kanoodle.