Robotics and Autonomous Mobile Robots

Introduction

According to Nourbakhsh and Siegwart mobile robotics is a recent field that has combined technologies from various fields of engineering and science. The essence of mobile robotics is to provide the previously rigid parts of machines a dexterity rivaling and even exceeding human beings through the complex combination of technologies. These technologies include ‘electrical and electronic engineering, computer engineering and cognitive and social sciences.’¹ Nourbakhsh and Siegwart go on to outline that Robots have recently found use in various sectors of the industry and are actually replacing human beings. They provide the example of a ‘manipulators or Robots arms that have the capacity of performing complex and repetitive tasks much easier due to their speed and precision. The speed and precision are particularly mandatory requirements for most industries that deal with the manufacture of complex and small devices such as laptops and mobile phones.²

However, as technological advancements took stage it became observable that there was still big room for improvement of the robots. These Robots were being controlled from a central position where someone was required to keep constant look to ensure that the Robots did not overdo certain tasks. For instance, a Robot that is programmed to perform spray painting would continue to spray paint even when there were no vehicles to paint unless it was shut down. This limitation was sufficient for technologists to begin thinking of an ‘intelligent Robot’ that would be fed memory and would perform tasks with the same precision and speed, but with minimum human supervision. Furthermore, it was also realized that to manipulate Robot’s movements it would be imperative to first understand how they move. Human beings do not control Robots but rather use the motions by the Robots to control movements. Nourbakhsh and Siegwart outline that ‘…humans perform localization and cognitive activities, but relies on the robot control scheme to control the robot’³

Locomotion

A robot is a machine consisting of parts that are immobile on their own. Therefore the question that arises is how a robot achieves the capacity to move freely. Dudek and Jenkin answer this question by outlining that a robot is, ‘a collection of subsystems’ with the capacity to move, perceive, reason and communicate. The movement helps the robot to explore its environment, the perception helps the robot to respond to changes within its environment and communication provides and interfaces for the exchange of information between the robot and human beings.⁴ Among the locomotion methods that have been studied include the wheeled and the legged locomotion methods. The methods are based on the computation of the motions observed in the surrounding fauna. ⁵According to Paul Chandana, the morphology of the robot plays an important role on how easily it navigates its environment and it responds to instructions. The morphology is crucial in a number of factors such as how the sensory and motor aspects of the robot interrelate, the resulting changes and the complexity of the control system that will be required. ⁶Kim and Shim in their research realized that the use of algorithm would solve the problem of driving the robot at a particular velocity as well as ensuring its stability based on the evolutionary programming. The study realized that the proposed algorithm had the capacity to provide stability for the robot as evidenced by computer simulations and based on the Layapunov Theory. ⁷

Mobile Robot Kinematics

Kinematics is concerned with various aspects of velocity as a robot moves including the angular and the linear velocity of the robot. The computation of the linear and the angular velocity as the robot moves helps in determining the most applicable design for a particular environment. According to Fahimi, most commercial mobile robots are based on the Hilare Model where the linear and the angular velocities are computed and resolved in coming up with a general law that guide the production of subsequent robots.⁸ Mobile Kinematics constitutes an important aspect for all mobile robots as it determines the degree of stability that a particular design will be able to achieve in a specific environment. Stability even becomes much more of a pre-requisite for autonomous mobile robots because they do not require supervision. Various other important aspects are put into consideration and they include the center of gravity for a particular model in resolving the angular and the linear velocity. The essence is always to come up with laws governed by calculations that will be applicable for a particular design.

Perception

Perception is concerned with how the Robots sense changes in the environment and the responses that the robot undertakes. For instance the vision of the autonomous mobile robot is very important and should therefore be accurate and full of clarity so that the robot can respond according to the memory that has been fed. Louis and Boyer explains that it is important for the robot to be able to use only the existing form of light to process images instead of requiring additional illumination. The mostly available form of light is generally the white light. Algorithm is usually employed to resolve the distance between a particular image and the robot and it is important that the algorithm employed is very sensitive to depth variations. This will have the effect of improving the accuracy of the image.

Blur is one particular challenge that designers of mobile autonomous have to deal with. Algorithm is also used in this aspect to estimate the extent of blur. In most instances special optics technology is employed to resolve an image observed from different planes. According to Louis and Boyer, the essence is to, ‘find a point spread function that if convolved with the small focal gradient, and image produces a large focal gradient.’⁹ The major challenge in autonomous mobile robotics in terms of perceptions has always been to resolve the robot’s trajectory from the point of origin to a particular destination. In autonomous mobile robots an additional challenge is presented in how to formulate a sensory strategy that will guide the robot into detecting such aspects as light and analyzing the variations. These challenges require special devices that will guarantee proper detection and response. An example of a design employed to compute the sensory problems is the Amplitude Modulated Continuous Wave (AMCW). This design makes use of a single frequency for reception.¹⁰

Localization and Mapping

According to Chatila Raja, localization and mapping are aspects that should be computed simultaneously so that the performance of the autonomous robot can be maximized. The ability of a robot ot autonomously navigate is the essence of the autonomy. The robot should have the capacity to construct a spatial representation, make decisions concerning motion, plan the motion and then finally initiate the motion. This challenge is also solved via mathematical laws and the computation of all probabilities.

Planning and Navigation

Planning, motion and navigation constitute an important aspect in designing and building of autonomous mobile vehicles. Some designs like to combine the aspects of trajectory planning while other designs approach them independently. Path planning deals with the ability of the robot to avoid obstacles while trajectory planning deals with the actions generated by the robot that initiate motion in a particular path. Motion planning makes use of evolutionary computation in determining the navigation algorithm that can generate real time response. Research has established that evolutionary approach has many advantages over other approaches; hence it is the most preferred. ¹¹

The basis of planning and navigation of autonomous mobile robots is the avoidance of hurdles and being able to manipulate various landscape and environments effectively. As outlined by Olunloyo and Ayomoh various strategies are available. One is to modulate the integration between virtual obstacle concept and virtual goal concept in a method termed as hybrid virtual force field. In their findings Olunloyo and Ayomoh established that the hybrid virtual force field methodology was ‘versatile and robust.’¹² A combination of GPS (Global Positioning System) and INS (Inertial Navigation System) technologies are used to resolve the 3 dimensional position of the robots, its velocity and its position. This is achieved by analyzing data from the GPS receiver and Inertial Measurement Unit (IMU). INS uses the information from the IMU to estimate the position and velocity. GPS receiver records information that can be analyzed to show the velocity of the antennae of the robot.¹³

Reference List

Adams, D. Sensor Modeling, Design and Data Processing for Autonomous Navigation. World Scientific Publishing Company, New York, 1998, p. 43.

Dudek, G. & Jenkin, M. computational Principles of Mobile Robotics. Cambridge University Press, New York, 2000, p.15.

Fahimi, F. Autonomous Robots: Modeling, Path Planning and Control, Volume 740. University of Alberta, Edmonton, 2009, p.163.

Kim, H. and Shim, S. Robust Optimal Locomotion Control Using Evolutionary Programming for Autonomous Mobile Robots.2009, Web.

Louis, S. and Boyer, L. Applications of AI Machine, Vision and Robotics. World Scientific Publishing Company, New York, 2005, p. 214.

Nourbakhsh, L. and Siegwart, L. Autonomous Mobile Robots. Massachusetts Institute of Technology, Massachusetts, 2005.

Nourbakhsh, R. and Siegwart, R. Introduction to Autonomous Mobile Robots.Massachusetts Institute of Technology, Massachusetts, 2004.

Olunloyo, s. Ayomoh, O. Autonomous Mobile Robot Navigation Using Hybrid Virtual Force Field Concept. 2009, Web.

Schaal, S. From Animals to Animats: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior. Massachusetts Institute of Technology, Massachusetts, 2004, p.33.

Tan, C. K., Wang, L. & Liu, D. Design and Control of Intelligent Robotic Systems. University of Technology, Sidney, 2007, p. 210.

Lewis, F. L. & Shuzi, G. Autonomous Mobile Robots: Sensing, Control, Decision-Making and Application. New York, Taylor & Francis, 2006, p.319.

Footnotes

1. Nourbakhsh, R. and Siegwart, R. Introduction to Autonomous Mobile Robots.Massachusetts Institute of Technology, Massachusetts, 2004.
2. Nourbakhsh and Siegwart, P. 3.
3. Nourbakhsh and Siegwart, P. 2.
4. Dudek, G. and Jenkin, M. computational Principles of Mobile Robotics. Cambridge University Press, New York, 2000, p.15.
5. Nourbakhsh, L. and Siegwart, L. Autonomous Mobile Robots. Massachusetts Institute of Technology, Massachusetts, 2005.
6. Schaal, S. From Animals to Animats: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior. Massachusetts Institute of Technology, Massachusetts, 2004, p.33.
7. Kim, H. and Shim, S. Robust Optimal Locomotion Control Using Evolutionary Programming for Autonomous Mobile Robots.2009, Web.
8. Fahimi, F. Autonomous Robots: Modeling, Path Planning and Control, Volume 740. University of Alberta, Edmonton, 2009, p.163.
9. Louis, S. and Boyer, L. Applications of AI Machine, Vision and Robotics. World Scientific Publishing Company, New York, 2005, p. 214.
10. Adams, D. Sensor Modeling, Design and Data Processing for Autonomous Navigation. World Scientific Publishing Company, New York, 1998, p. 43.
11. Tan, C. K., Wang, L. & Liu, D. Design and Control of Intelligent Robotic Systems. University of Technology, Sidney, 2007, p. 210
12. Olunloyo, s. Ayomoh, O. Autonomous Mobile Robot Navigation Using Hybrid Virtual Force Field Concept. 2009, Web.
13. Lewis, F. L. & Shuzi, G. Autonomous Mobile Robots: Sensing, Control, Decision-Making and Application. New York, Taylor & Francis, 2006, p.319.