Robots are rapidly evolving from factory workhorses, which are physically bound to their work-cells, to increasingly complex machines capable of performing challenging tasks in our daily environment. The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is put on mobile robot locomotion and kinematics, environment perception, probabilistic map based localization and mapping, and motion planning. The lectures and exercises of this course introduce several types of robots such as wheeled robots, legged robots and drones.
Abstract:Providing mobile robots with autonomous capabilities is advantageous. It allows one to dispense with the intervention of human operators, which may prove beneficial in economic and safety terms. Autonomy requires, in most cases, the use of path planners that enable the robot to deliberate about how to move from its location at one moment to another. Looking for the most appropriate path planning algorithm according to the requirements imposed by users can be challenging, given the overwhelming number of approaches that exist in the literature. Moreover, the past review works analyzed here cover only some of these approaches, missing important ones. For this reason, our paper aims to serve as a starting point for a clear and comprehensive overview of the research to date. It introduces a global classification of path planning algorithms, with a focus on those approaches used along with autonomous ground vehicles, but is also extendable to other robots moving on surfaces, such as autonomous boats. Moreover, the models used to represent the environment, together with the robot mobility and dynamics, are also addressed from the perspective of path planning. Each of the path planning categories presented in the classification is disclosed and analyzed, and a discussion about their applicability is added at the end.Keywords: guidance; autonomy; vehicle; survey; trajectory; route; graph search; sampling; wheeled
This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides
Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.
This work presents a new control strategy for mobile robots in wireless environments. The main idea is to develop architectures that manage the limited radio resources efficiently with a similar accuracy as the classical solutions. The event-based control strategies have been investigated to implement navigation algorithms and to compare them to the discrete-time implementations.
In this scheme, the controller sends the control signals uc[n] over a communication channel Ch1(t); this information is received in the robot ur[n] and acts over the actuators. The difference between uc[n] and ur[n] is the noise and the perturbations in the communication channel Ch1(t). The sensor signals yr[n] are sent towards the controller over another communication channel Ch2(t). Finally, the controller receives the sensor signals yc[n] and calculates the control signals uc[n], computing the reference signal w[n] and the information from the sensors. As in channel Ch1(t), the noise and the perturbations in channel Ch2(t) produce two different signals yr[n] and yc[n]. This system interchanges information between the controller and the mobile robot every 1/fs seconds.
The architecture used to check these algorithms is depicted in Figure 5. The system consists of a centralized controller and some mobile robots, which use an RF communication between them. The navigation algorithm is implemented in the controller. It sends the control signals (angular speeds) to the robots, and the mobile robots send to the controller the sensor signals (obstacle information). The communication between elements (robots and controller) occurs only when the robot sends sensor information to the controller. When this happens, the controller calculates the control signals and sends this information to the robot. In the discrete-time architecture, the communication between elements is periodical. In the event-based solution proposed in this work, the communication occurs only when the event condition is satisfied.
In order to solve the real-time motion planning problem, there are effective methods (Khatib, 1986) (Arkin, 1989) (Borenstein & Koren, 1991) (Fox et al., 1997). Fuzzy potential method (FPM) (Tsuzaki & Yoshida, 2003) (Otsuka et al. 2005) is also one of the effective methods. In this research, the method was applied to an autonomous mobile robot which plays soccer. By adequate designing of potential membership function (PMF), it was realized that wheeled robots got to the goal with conveying a soccer ball and avoiding obstacles. This method is easy to understand at a glance. However, in dynamic environment, to avoid moving obstacles efficiently, more specific guideline of designing is desired. In this paper, we introduce design method of PMF considering the predicted positions and discuss the availability by comparing the design of PMF considering the relative velocity and that not considering.
To verify the effectiveness of the proposed method that employs PMF considering the velocity of the obstacle of the robot, numerical simulations which assumed an obstacle avoidance of autonomous omni-directional mobile robot were carried out.
In this paper, for the purpose of avoiding the moving obstacle safely and smoothly, design methods of the potential membership function (PMF), taking into consideration the velocity of the obstacle relative to the robot have been presented. The proposed PMF for an obstacle and PMF for a goal are unified by fuzzy inference. By defuzzification, the command velocity vector of the robot is calculated and the obstacle. Numerical simulations and simplified experiments, which assumed an obstacle avoidance of an autonomous omni-directional mobile robot, were done. As the result of the comparison between the design method of PMF using relative velocity and not using, it was confirmed that the PMF using relative velocity enhanced the ability of avoiding the moving obstacle.
This paper focuses on comparative results of two different controllers applied to kinematic bicycle model with rear wheel contact point to the ground as the reference point. The wide range of representation of different types of robots and vehicles of kinematic bicycle model is the main reason for this model selection. This paper has three main sections. The first section of the paper is mathematical modeling of the model. The second section is describing the utilized control techniques. The last section shares results of the simulations. The simulations have been carried out with pure feedback signals in absence of noise. The compared two controllers are an (Linear Quadratic Regulator)LQR controller and a Lyapunov based controller. The objective in the simulations is to track and complete a given constant radius trajectory. Last section includes comparison of results by analyzing statistical values of a defined error signal.
Even while the introduction of warehouse robots sometimes raises fears about the future of human employment, Amazon is certain that Sparrow will "take up boring activities," freeing up workers' time to focus on more meaningful tasks.
Autonomous mobile robots (AMRs) are widely used in many industries like logistics and manufacturing. However, there are various challenges in developing and operating autonomous robots. To develop autonomous robots, a wide range of technologies are required, and the process is complex and time-consuming. Integration with the cloud is also required to develop and operate the robots effectively. However, many robot builders are not familiar with the benefits of cloud robotics or lack cloud development expertise that can help them bring smarter robots to market faster.
By reading this article, you will learn how to solve the common challenges in developing and operating autonomous robots with AWS services. You can also understand which services are required to realize your use case and where to start your prototype.
The development of robots requires expertise in a wide range of domains. For example, artificial intelligence (AI) and machine learning (ML) technologies are used for autonomous navigation; cloud connectivity is required for application integration; and video streaming is used for remote monitoring and operation.
Because of these challenges, development of autonomous robots is laborious and time-consuming. AWS provides various services that can be used to develop, test, and operate such robot applications faster. With these services, you can quickly build your prototypes and easily operate a large number of robots in production. In the following section, I will introduce how you can utilize these services in robot development and solve the challenges.
An autonomous robot is supposed to operate by itself in various environment, and unforeseen circumstances requires help by operators. In that case, the following capabilities are required. For example, operators can remotely control the robot via the cloud and developers can troubleshoot using the logs collected from robots. You can utilize AWS IoT Core to develop these features. 2b1af7f3a8