Mobile Robots: Navigation, Control and Remote Sensing

Mobile Robots: Navigation, Control and Remote Sensing

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A preceding chapter is devoted to coordinate rotations and transformations since they play an important role in the understanding of this body of theory.

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To get the free app, enter your mobile phone number. The use of mobile robots to sense objects of interest plays a vital role in our society, from its value in military maneuvers to the exploration of natural resources to search and rescue operations. Written by an expert in the field, this book is the only resource to explain all the major areas of mobile robot applications—control, navigation, and remote sensing—which are essential to not only detecting desired objects but also providing accurate information on their precise locations.

The material can be readily applied to any type of ground vehicle.

In the controls area, both linear and nonlinear models of robot steering are presented. Through these applications, the reader is introduced to linearization and use of linear control design methods for control about a reference trajectory; use of Lyapunov stability theory for nonlinear control design; derivation of optimal control strategies via Pontryagin's maximum principle; and derivation of a local coordinate system. In navigation, the global positioning system GPS is described in detail, as are inertial navigation systems INS , which are treated in terms of their ability to provide vehicle position as well as altitude.

In remote sensing methods, the author addresses the problem of ground registration of detected objects of interest, which provides essential information for any future actions such as inspection or retrieval. The book covers control of a robotic manipulator as well as airborne sensing and detection of objects on the ground. It also explains the use of optimal processing via the Kalman Filter when there are multiple detections of the same object, and the decision process of associating detections with the proper objects when tracking multiple objects.

The book's clear presentation, numerous examples in each chapter, and references combine to make Mobile Robots a textbook for a one-semester electrical engineering graduate course on the same subject area. Since the topics covered in this volume cut across traditional curricular boundaries and bring together material from several engineering disciplines, this book also serves as a text for courses taught in mechanical or aerospace engineering, as well as a valuable resource for practicing engineers working in related areas. Air Force Global Hawk, an unmanned reconnaissance aircraft, photograph reproduced with permission of Airforce Link; bottom circle autonomous underwater vehicle, photo taken by an employee of Bluefin Robotics Corporation during U.

Williams Professor of Electrical Engineering and past chairman of electrical and computer engineering at George Mason University. He was previously chairman of electrical and biomedical engineering at Vanderbilt University and professor of electrical engineering at the University of Virginia. Product details File Size: May 11, Sold by: Not Enabled Word Wise: Enabled Amazon Best Sellers Rank: An important feature of this book is the particular combination of topics included.

These are 1 control, 2 navigation and 3 remote sensing, all with application to mobile robots. Much of the material is readily extended to any type ground vehicle. Read more Read less. Kindle Cloud Reader Read instantly in your browser. Editorial Reviews From the Back Cover A unique, accessible guide on mobile robot applications The use of mobile robots to sense objects of interest plays a vital role in our society, from its value in military maneuvers to the exploration of natural resources to search and rescue operations.

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Mobile Robots - Navigation, Control and Remote Sensing (Electronic book text)

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Urban search and rescue USR are time-critical missions, failing to find and rescue the victims in time within the hazardous areas may lead to a tragedy. Search and rescue robots have been proven to be useful in disasters such as hurricanes, volcanos, collapses and earth quakes [1] [2] [3]. Due to the need of a low-risk solution to detect the victims and assess the hazards, robots were equipped with sensing elements and have been utilized to navigate within the disastrous areas for searching and surveying [1] [2].

Whether manually tele operated or autonomously driven, various types of robots such as unmanned ground vehicles UGV , unmanned aerial vehicles UAV and unmanned surface vehicles USV , have been used in urban search and rescue missions [4] [5] [6]. Surveying and remote sensing in an urban hazardous indoor area is a challenging problem due to the restricted accessibility for personnel and robots. Indoor navigation is difficult for robots due to the loss of GPS signals and would require a localization system to position the robot within the indoor environment [9] [10].

Synchronous Localization and Mapping SLAM methods would require more processing time and would be beneficial in less critical applications than search and rescue missions where time factor is of utmost importance to facilitate the aid and save victims lives [8] [10] [11]. This paper presents a novel work on combining path planning using SDA algorithm in an unknown indoor environment with minimum priori information. This paper presents an approach for optimal path planning for a remote sensing autonomous robot in a cluttered and hazardous indoor environment.

The operating scenario of this robot is applicable during the search and rescue missions, where unmanned ground vehicles UGVs are favored, to survey and sense the environment for various detectable phenomena such as gases, fire or smoke detection and etcetera.

The proposed algorithm can be generalized to any given map and as an example simulation scenario; we present an application of path planning of a mobile robot in an urban search and rescue mission to navigate through an indoor hazardous building for remote-sensing and assessment of the hazardous situation.

With the proposed system, the robot will navigate autonomously by utilizing probabilistic roadmaps PRM to find out all the possible navigation paths for autonomous navigation of the robot, given the building map. With the various solutions of the probabilistic roadmaps, an optimal path that would be selected, based on particle swarm optimization algorithm, that covers most of the indoor area to provide the best possible assessment of the hazard situation. Probabilistic roadmap PRM methods have been known for their efficient approach in path planning complex motions for a wide discipline of applications including various types of robots such as manipulators, unmanned robotic vehicles as well as predicting the motion and transitions of biological systems such as proteins and molecules [12] [13].

In robotics, PRM solves complex motion planning problems for a single or multiple robots with multiple degrees of freedom in free space. PRM is based on representing an approximation of the free space F in a sample-based approach that is referred to as a configuration space and is composed of nodes and local paths or segments. PRM algorithm is based on two steps; roadmap construction and the roadmap query. The algorithm requires start and goal points to calculate collision-free paths from the constructed representation of the free space.

The basic PRM pseudocode used in this research is presented in Table 1. With its simple pseudo code, the PRM algorithm could calculate many feasible paths in any given map. However, with the limited and constrained time of the rescue missions, an optimal path among all the calculated paths would be needed within a fast and reliable time to facilitate the given tasks of surveying or remote-sensing of the environment.

Thus, a fast optimization algorithm would be needed to obtain an optimal comprehensive path with the given constraints.

Spiral dynamics optimization algorithm SDA has been inspired by the common feature of the logarithmic spirals found in nature such as whirling currents and was introduced by Tamura and Yasuda [14] who believed that it could be of a beneficial search strategy. SDA is a relatively new metaheuristic optimization algorithm that was tested and compared against other common optimization methods such as particle swarm optimization PSO and bacterial foraging algorithm BFA and has shown an equal or better performance in terms of the speed of convergence and evolution of cost accuracy [15] [16].

Mobile Robots : Navigation, Control and Remote Sensing by Gerald Cook (, Hardcover) | eBay

The strength of the algorithm relies in the diversification and intensification of the search stages that mimics the whirling current spiral where the diversification covers a wider area of search and the intensification improves the cost accuracy around good solutions. With its fast convergence towards optimal cost. Basic PRM pseudocode [13]. The nomenclature of the SDA optimization is presented in Table 2 followed by the pseudocode of the algorithm in Table 3 as reported by Tamura and Yasuda in [14].

The simple structure both the PRM and SDA algorithms and would enable having an onboard processing unit to calculate the optimal path online.

In the upcoming section, we demonstrate the feasibility of the proposed algorithm over a given operating scenario that involves an indoor cluttered map with obstacles. The simulation scenario assumed in this paper is to have the robot navigate through some hazardous chemical laboratories where a gas leak has been detected. The robot will be simulated to have a starting point at the reception. SDA optimization pseudocode [14]. Chemical Laboratories building map.

Mobile Robots: Navigation, Control and Remote Sensing

The building is assumed to have a cluttered environment with many obstacles and barriers within the navigation path such as furnishing, equipment and building infrastructures as illustrated in Figure 2. These obstacles would need to be defined for the path planning algorithm to be avoided and an optimal path is calculated. To start simulating the PRM algorithm, the building map need to be converted into binary occupancy grid matrices in order to define the area constraints for the algorithm. Figure 3 presents the converted map that is drawn using a binary occupancy grid matrix.

Simulations start with the SDA optimization algorithm to find out the best navigating path where the robot can record most of the sensory data throughout. The obstacles and barriers of the building. A grayscale map constructed from the binary occupancy grid. The objective function is defined to have the best connection distance between the PRM nodes. Table 4 presents the simulation constraints in terms of the number of nodes and connection distances. Figure 4 and Figure 5 show the lower and upper limits of the PRM results respectively with various numbers of nodes and connection distances.

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