Mobile Robotics Education

With the educational mobile robot Worm-Type Mobile Educational Robot (Robot Móvil Educativo tipo Oruga, or ROMEO, by its Spanish acronym), the authors offer three hierarchical levels of experimental learning, where the operator can develop as far as his/her ability or imagination permits, gaining knowledge about the basics of sensors, communications, and mechanical and robot programming. Due the lack of learning focused on robotics in Mexican educational institutions, the authors present this chapter, where an early stimulation to this topic could trigger curiosity to research that leads to technological advancement. ROMEO is a mobile wireless communication platform with different types of sensors: moisture, brightness, temperature, etc., as well as a compass and accelerometers with similar characteristics to industrial and commercial applications that allow us to experiment with communication algorithms, sampling, and autonomous and semiautonomous navigation.

Local Path-Tracking Strategies for Mobile Robots Using PID Controllers

Accurate path following is an important mobile robot research topic. In many cases, radio controlled robots are not able to work properly due to the lack of a good communication system. This problem can cause many difficulties when robot positioning is regarded. In this context, gaining automatic abilities becomes essential to achieving a major number of mission successes. This chapter presents a suitable control methodology used to achieve accurate path following and positioning of nonholonomic robots by using PID controllers. An important goal is to present the obtained experimental results by using the available mobile robot platform that consists of a differential driven one.

Ad Hoc Communications for Wireless Robots in Indoor Environments

This chapter presents a theoretical and experimental comparison of electromagnetic propagation models for indoor robot communication using mobile ad-hoc IEEE802.11 and IEEE802.15.4. The analysis includes the behavior of the electromagnetic signal using the abovementioned standards in two scenarios, both located inside the building of the College of Telematics of the University of Colima. The results of the propagation of the electromagnetic signals in the two scenarios were then compared with the mathematical model.

Design of a Mobile Robot to Clean the External Walls of Oil Tanks

This chapter describes a Mechatronics Design methodology applied to the design of a mobile robot to climb vertical surfaces. The first part of this chapter reviews different ways to adhere to vertical surfaces and shows some examples developed by different research groups. The second part presents the stages of Mechatronics design methodology used in the design, including mechanical design, electronics design, and control design. These stages describe the most important topics for optimally successful design. The final part provides results that were obtained in the design process and construction of the robot. Finally, the conclusions of this research work are presented.

Path Planning in a Mobile Robot

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.

A Swarm Robotics Approach to Decontamination

Many interesting and difficult practical problems need to be tackled in the areas of firefighting, biological and/or chemical decontamination, tactical and/or rescue searches, and Web spamming, among others. These problems, however, can be mapped onto the graph decontamination problem, also called the graph search problem. Once the target space is mapped onto a graph G(N,E), where N is the set of G nodes and E the set of G edges, one initially considers all nodes in N to be contaminated. When a guard, i.e., a decontaminating agent, is placed in a node i ??N, i becomes (clean and) guarded. In case such a guard leaves node i, it can only be guaranteed that i will remain clean if all its neighboring nodes are either clean or clean and guarded. The graph decontamination/search problem consists of determining a sequence of guard movements, requiring the minimum number of guards needed for the decontamination of G. This chapter presents a novel swarm robotics approach to firefighting, a conflagration in a hypothetical apartment ground floor. The mechanism has been successfully simulated on the Webots platform, depicting a firefighting swarm of e-puck robots.

An Alternative for Trajectory Tracking in Mobile Robots Applying Differential Flatness

One solution for trajectory tracking in a non-holonomic vehicle, like a mobile robot, is proposed in this chapter. Using the boundary values, a desired route is converted into a polynomial using a point-to-point algorithm. With the properties of Differential Flatness, the system is driven along this route, finding the necessary input values so that the system can perform the desired movement.

Mobile/Wireless Robot Navigation

Sensor-based localization has been found to be one of the most preliminary issues in the world of Mobile/Wireless Robotics. One can easily track a mobile robot using a Kalman Filter, which uses a Phase Locked Loop for tracing via averaging the values. Tracking has now become very easy, but one wants to proceed to navigation. The reason behind this is that tracking does not help one determine where one is going. One would like to use a more precise “Navigation” like Monte Carlo Localization. It is a more efficient and precise way than a feedback loop because the feedback loops are more sensitive to noise, making one modify the external loop filter according to the variation in the processing. In this case, the robot updates its belief in the form of a probability density function (pdf). The supposition is considered to be one meter square. This probability density function expands over the entire supposition. A door in a wall can be identified as peak/rise in the probability function or the belief of the robot. The mobile updates a window of 1 meter square (area depends on the sensors) as its belief. One starts with a uniform probability density function, and then the sensors update it. The authors use Monte Carlo Localization for updating the belief, which is an efficient method and requires less space. It is an efficient method because it can be applied to continuous data input, unlike the feedback loop. It requires less space. The robot does not need to store the map and, hence, can delete the previous belief without any hesitation.

Study and Design of an Autonomous Mobile Robot Applied to Underwater Cleaning

The chapter describes the stages of an autonomous mobile robot project, in this case, an underwater cleaning robot. First, the authors analyze the products already available for costumers, mainly focusing on the tasks they can perform (instead of the systems they use), in order to define the requirements of their project. Then, they build some models, based in the literature available. Based on them, the authors dimension the parts and systems by evaluating the results of these models. Finally, the authors use all information gathered to create a prototype, modeled with a CAE system.

RobotBASIC

Unlike most chapters in this book, this chapter does not introduce new methods or algorithms related to robotic navigation systems. Instead, it provides an overview of a simulation tool that, in some situations, can be useful for quickly evaluating the overall appropriateness of a wide variety of alternatives before focusing more advanced development activities on a chosen design. In addition, since the tool described herein is totally free, it can be used to help students and others new to robotics understand the value of utilizing a design-simulate-deploy approach to developing robotic behaviors. Robot Simulators can emulate nearly all aspects of a robot’s functionality. Unfortunately, many programming environments that support simulation have steep learning curves and are difficult to use because of their ability to handle complex attributes such as 3D renderings and bearing friction. Fortunately, there are many situations where advanced attributes are unnecessary. When the primary goal is to quickly test the feasibility of a variety of algorithms for robotic behaviors, RobotBASIC provides an easy-to-use, economical alternative to more complex systems without sacrificing the features necessary to implement a complete design-simulate-deploy cycle. RobotBASIC’s ability to simulate a variety of sensors makes it easy to quickly test the performance of various configurations in an assortment of environments. Once algorithm development is complete, the same programs used during the simulation phase of development can immediately control a real robot.