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Chapter 47 — Motion Planning and Obstacle Avoidance

Javier Minguez, Florant Lamiraux and Jean-Paul Laumond

This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

The challenge of this chapter is to present both nonholonomic motion planning (Sects. 47.1–47.6) and obstacle avoidance (Sects. 47.7–47.10) issues. Section 47.11 reviews recent successful approaches that tend to embrace the whole problemofmotion planning and motion control. These approaches benefit from both nonholonomic motion planning and obstacle avoidance methods.

Autonomous navigation of a mobile vehicle

Author  Visp team

Video ID : 713

This video shows the vision-based autonomous navigation of a Cycab mobile vehicle able to avoid obstacles detected by its laser range finder. The reference trajectory is provided as a sequence of previously-acquired key images. Obstacle avoidance is based on a predefined set of circular avoidance trajectories. The best trajectory is selected when an obstacle is detected by the laser scanner.

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.

HD footage of 1950s atomic power plants - Nuclear reactors

Author  James P. Trevelyan

Video ID : 586

Robot manipulators, mainly remotely controlled and operated by people, have been widely used in the nuclear industry since the 1950s. This video contains archival film footage showing operations using remote manipulators.

Chapter 8 — Motion Control

Wan Kyun Chung, Li-Chen Fu and Torsten Kröger

This chapter will focus on the motion control of robotic rigid manipulators. In other words, this chapter does not treat themotion control ofmobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other chapters. In this chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional– integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robotmanipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

Gain change of the PID controller

Author  Wan Kyun Chung

Video ID : 25

The control architecture of the PID tracking controller is introduced. Moreover, according to the gain change, the performance variations of the PID controller implemented in the digital control system are shown.

Chapter 47 — Motion Planning and Obstacle Avoidance

Javier Minguez, Florant Lamiraux and Jean-Paul Laumond

This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

The challenge of this chapter is to present both nonholonomic motion planning (Sects. 47.1–47.6) and obstacle avoidance (Sects. 47.7–47.10) issues. Section 47.11 reviews recent successful approaches that tend to embrace the whole problemofmotion planning and motion control. These approaches benefit from both nonholonomic motion planning and obstacle avoidance methods.

A ride in the Google self-driving car

Author  Google Self-Driving Car Project

Video ID : 710

The maturity of the tools developed for mobile-robot navigation and explained in this chapter have enabled Google to integrate them into an experimental vehicle. This video demonstrates Google's self-driving technology on the road.

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.

IED hunters

Author  James P. Trevelyan

Video ID : 572

The video shows the work of route-clearance teams in Afghanistan.   This video has been included because researchers can see plenty of examples of realistic field conditions under which explosive-ordnance clearance is being done in Afghanistan. It is essential for researchers to have an accurate appreciation of the real field conditions before considering expensive research projects. It is also essential that researchers understand how easily insurgent forces can adapt and defeat technological solutions that have cost tens of millions of dollars to develop. Read the caption below carefully and then watch the video with this in mind. Better-quality blast-protected vehicles provide the teams with more confidence to handle challenging tasks. You will also see that improvised explosive devices (IEDs) used by insurgents are typically made from the unexploded ordnance (UXO) which the demining teams are trying to remove. Between 15% (typical failure rate for high quality US-made ammunition) and 70% (old Russian-designed ammunition) fail to explode when used.   These UXOs lie in the ground in a, at best, semi-stable state, so some easily exploded accidentally at times. Insurgents collect and attempt to disarm them, then set them up with remotely operated or vehicle-triggered detonation fuses. That is why the demining teams came to be seen as legitimate targets by insurgents, because they were removing the explosive devices the insurgency needed to fight people who they regarded as legitimate enemies. Although not explicitly acknowledged in the commentary, this video also demonstrates one of the many methods used by insurgents to adapt their techniques to defeat the highly advanced technologies available to the ISAF teams. By laying multiple devices in different locations, using different triggering devices and different deployment methods, the insurgents soon learned what the ISAF teams could and could not detect.   Every blast indicated a device that was not detected in advance by the ISAF team. Every device removed by the team indicated a device that was detected. In this way, the insurgents rapidly learned how to deploy undetectable devices that maximized their destructive power.

Chapter 26 — Flying Robots

Stefan Leutenegger, Christoph Hürzeler, Amanda K. Stowers, Kostas Alexis, Markus W. Achtelik, David Lentink, Paul Y. Oh and Roland Siegwart

Unmanned aircraft systems (UASs) have drawn increasing attention recently, owing to advancements in related research, technology, and applications. While having been deployed successfully in military scenarios for decades, civil use cases have lately been tackled by the robotics research community.

This chapter overviews the core elements of this highly interdisciplinary field; the reader is guided through the design process of aerial robots for various applications starting with a qualitative characterization of different types of UAS. Design and modeling are closely related, forming a typically iterative process of drafting and analyzing the related properties. Therefore, we overview aerodynamics and dynamics, as well as their application to fixed-wing, rotary-wing, and flapping-wing UAS, including related analytical tools and practical guidelines. Respecting use-case-specific requirements and core autonomous robot demands, we finally provide guidelines to related system integration challenges.

UAV stabilization, mapping and obstacle avoidance using VI-Sensor

Author  Skybotix AG

Video ID : 689

The video depicts UAV stabilization, mapping and obstacle avoidance using the Skybotix--Autonomous Systems Lab VI-Sensor - on-board and realtime. The robot is enabled with assisted teleoperation without line of sight and without the use of GPS during the ICARUS trials in Marche-En-Famenne.

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 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.