Real-time architecture for mobile assistant robots

MOBILE ROBOTICS, 2023

Autonomous robots are becoming increasingly important in today's world. They have the potential to revolutionize many industries, such as manufacturing, logistics, healthcare, and transportation. they are efficiency, cost effective and safe and as technology continues to advance, we can expect to see even more exciting developments in this field in the coming years. This report was carried out to program a robot to perform three robot behaviors of Odometry movement, Target tracking and Obstacle avoidance. Results was analyzed data between these behaviors to check different statistical measures of deviations, mean calculations, covariance and correlations between each behavior. The aim was to identify weaknesses and propose corrective solutions. Experiment was carried out using software applications and a pioneer robot. Using the intelligent system architecture methodology, robot was programmed and tested, data was captured and analyzed.

2008

In this paper, we introduce a hardware-software architecture for controlling the autonomous mobile robot Kapeck. The Kapeck robot is composed of a set of sensors and actuators organized in a CAN bus. Two embedded computers and eigth microcontroller-based boards are used in the system. One of the computers hosts the vision system, due to the significant processing needs of this kind of system. The other computer is used to coordinate and access the CAN bus and to accomplish the other activities of the robot. The microcontrollerbased boards are used with the sensors and actuators. The robot has this distributed configuration in order to exhibit a good real-time behavior, where the response time and the temporal predictability of the system is important. We adopted the hybrid deliberative-reactive paradigm in the proposed architecture to conciliate the reactive behavior of the sensors-actuators net and the deliberative activities required to accomplish more complex tasks.

International Journal of Electrical and Computer Engineering (IJECE), 2021

Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-today activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.

2007

Interest on using mobile autonomous agents has been growing (Weiss, G., 2000), (K. Kitano; Asada, M.; Kuniyoshi, Y.; Noda, I. & Osawa E., 1997) due to their capacity to gather information on their operating environment in diverse situations, from rescue to demining and security. In many of these applications, the environments are inherently unstructured and dynamic, and the agents depend mostly on visual information to perceive and interact with the environment. In this scope, computer vision in a broad sense can be considered as the key technology for deploying systems with an higher degree of autonomy, since it is the basis for activities like object recognition, navigation and object tracking. Gathering information from such type of environments through visual perception is an extremely processor-demanding activity with hard to predict execution times (Davison, J., 2005). To further complicate the situation many of the activities carried out by the mobile agents are subject to real-time requirements with different levels of criticality, importance and dynamics. For instance, the capability to timely detect obstacles near the agent is a hard activity, since failures can result in injured people or damaged equipment, while activities like self-localization, although important for the agent performance, are inherently soft since extra delays in these activities simply cause performance degradation. Therefore, the capability to timely process the image at rates high enough to allow visual-guided control or decision-making, called real-time computer vision (RTCV) (Blake, A; Curwen, R. & Zisserman, A., 1993), plays a crucial role in the performance of mobile autonomous agents operating in open and dynamic environments. This chapter describes a new architectural solution for the vision subsystem of mobile autonomous agents that substantially improves its reactivity by dynamically assigning computational resources to the most important tasks. The vision-processing activities are broken into separated elementary real-time tasks, which are then associated with adequate real-time properties (e.g. priority, activation rate, precedence constraints). This separation allows avoiding the blocking of higher priority tasks by lower priority ones as well as to set independent activation rates, related with the dynamics of the features or objects being processed, together with offsets that de-phase the activation instants of the tasks to further

2013 IEEE 4th International Conference on Software Engineering and Service Science, 2013

Nowadays, robots are used to perform tasks that require great levels of precision or are simply repetitive. Mobile robots are able to move their position from one place to another in unstructured or structured environments to do their task with using sensor for their navigation. In this paper, we present some of current technology in mobile robots which can be implemented to intelligent vehicles based on their architecture that uses a sequence of three steps: Sense, Plan, and Act (SPA). For sensing, several sensors such as camera and proximity sensors are common modules for a mobile robot which can be also employed in an intelligent vehicle. Advanced technologies for planning in mobile robots can be employed to intelligent vehicles in order to organize and plan more intelligent behaviors. Furthermore trends in actuation of the mobile robot can be adapted to vehicles to achieve novel architecture in future cars.

2006

Operating environments for autonomous mobile robots are characterized by short-term and long-term changes or moving obstacles that make it very difficult for the robot to acquire and maintain an exact model of the world. For robots to operate successfully under such conditions, they have to use any knowledge that is available. In order to continuously perceive information about the environment and update its model of the world, an autonomous robot is typically equipped with several sensor systems. In this paper, we present a control architecture where perceptions are also capable of directly activating robot behaviour. The control architecture scheme follows the hybrid guidelines and it maintains an environment representation where the highest level is built from perception outcomes. These perceptions are organized into a set of modules that perform different levels of sensory representation: primitives, such as localization or landmark detection, and compound perceptions, such as feature maps (local SLAM). The low reactive layer of the architecture is based on a set of behaviours which only provide the robot with navigation capabilities. Finally, the deliberative layer builds a symbolic representation of the environment (topological map) and integrates suitable algorithms to accomplish the execution of the task.

2006

After proving to be an efficient tool for improving quality, productivity, and competitiveness of manufacturing organizations, robots now expand to service organizations, offices, and even homes. Global competition and the tendency to reduce production cost and increase efficiency creates new applications for robots that stationary robots can't perform. These new applications require the robots to move and perform certain activities at the same time. The availability and low cost of faster processors, better programming, and the use of new hardware allow robot designers to build more accurate, faster, and even safer robots. For example, Egemin Automation has been selling mobile robots for a number of years, available as a specialized unit that delivers mail within a large building to warehouse automation systems. Currently, mobile robots are expanding outside the confines of buildings and into rugged terrain, as well as familiar environments like schools and city streets (Wong, 2005). The problem addressed in this paper is to describe the theory and architecture of robust learning for mobile robots and to illustrate the theory for designing intelligent mobile robots for a wide variety of applications anywhere in the world including complex and uncertain environments. The proposed architecture for machine learning is also based on the perceptual creative controller for an intelligent robot that uses a multi-modal adaptive critic for performing learning in an unsupervised situation but can also be trained for tasks in another mode and then is permitted to operate autonomously. The robust nature is derived from the automatic changing of modes based on internal measurements of error at appropriate locations in the controller. 2. Types of Mobile Robots M a n y d i f f e r e n t t y p e s o f m o b i l e r o b o t s h a d been developed depending on the kind of application, velocity, and the type of environment whether its water, space, terrain with fixed or moving obstacles. Four major categories had been identified (Dudek & Jenkin, 2000): Terrestrial or ground-contact robots: The most common ones are the wheeled robots; others are the tracked vehicles and Limbed vehicles. Aquatic robots: Those operate in water surface or underwater. Most use water jets or propellers. Airborne robots: Flying robots like Robotic helicopters, fixed-wing aircraft, robotically controlled parachutes, and dirigibles.

This project introduces the intelligent mobile robot systems in indoor environment applications. There are three major algorithms involved. They are known as object classification, object tracking and obstacle avoidance. The inputs are received from cameras which are mounted at a ceiling. The main idea of the object classification is to classify object into three categories depending upon their colors; the categories are mobile robot, destinations and obstacle position. These categories are represented by X symbol with different colors. This system is to teach and train the mobile robot proceeding to destination without hitting the obstacle. The mobile robot is autonomous; that means, it could be pursuing to the target position automatically without user guided. In this project, fuzzy logic is use to guide the mobile robot direction until it reaches the target position. This system is generates in real-time and suitable for indoor environment applications. One of the unique advantages of this project is that it only uses, there only used a camera and image processing generated by the algorithms itself without additional sensor such as sonar or IR sensor. Intelligent Space (iSpace) has been active since 1996 and expending with more and more functions. The concept of the intelligent space is to effectively use distributed sensors, actuators, robots, computing processors, and information technology over communication networks. iSpace is a large scale Mechatronics System by integrating sensors, actuators, and control algorithms in a communication system using knowledge from various engineering disciplines such as automation and control, hardware and software design, image processing, communication and networking.

Abstract. This paper deals with the general problem of designing hardware and software architectures for mobile robots. In particular, four different software architectures of mobile robots are studied. An architecture for an industrial forklift, used in the automation of management and transport processes is firstly described. An architecture for a convoying application with an electric car is secondly showed.