Top PDF Obstacle Sensing Autonomous Mobile (OSAMO) Robot

Obstacle Sensing Autonomous Mobile (OSAMO) Robot

Obstacle Sensing Autonomous Mobile (OSAMO) Robot

Troubleshooting is needed to ensure a functioning robot is produced at the end of the project period.. Ultrasonic Sensors Other Circuitry.. PORTD is a bi-directional UO port [r]

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Autonomous Operation and Human-Robot Interaction on an Indoor Mobile Robot

Autonomous Operation and Human-Robot Interaction on an Indoor Mobile Robot

MARVIN needs to know its current pose (position and rotation) to allow it to navigate. The Segway measures the amount each of its wheels ro- tates over time. From these measurements we can estimate the change in MARVIN’s pose relative to its starting pose. This is known as the robot’s odometry. Odometry can be used to estimate MARVIN’s pose, but it prone to errors. These errors can be caused by the wheel radius changing (due to flat tires), by the wheels slipping or by stochastic errors in the wheel rotation measurements. Over time these errors accumulate providing a less certain estimate. This is known as odometry drift. Because of this, MAR- VIN needs additional information to correctly estimate its pose. MARVIN’s operating environment is confined to known areas, as discussed in section 1.1.1, so a pre-built map can be used. We can compare what MARVIN is currently sensing to the map to narrow down the list of possible poses. If we then combine this list of possible poses with the odometry and any previous pose estimates, a much more certain pose estimate can be made. This process is known as probabilistic localisation.
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Autonomous Wheeled Mobile Robot Control

Autonomous Wheeled Mobile Robot Control

The author applied the proposed fuzzy controller to the autonomous wheeled mobile robotic platform moving in an unstructured environment with obstacles. The control strategy was tested through simulations of wheeled mobile robot motion [24-27]. A simulation example of a wheeled autonomous mobile robotic platform is presented in Figure 3. The corresponding fuzzy control is implemented to perform tasks of obstacle and collision avoidance. In particular, the navigation strategy proved to be extremely sensitive to the balance between avoid obstacle and reach the target behaviors. Simulation results are shown in Figure 3.
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Path Planning, Motion Control and Obstacle Detection of Indoor Mobile Robot

Path Planning, Motion Control and Obstacle Detection of Indoor Mobile Robot

Find-path problem or Path planning is popular in robotics since it has an essential role in the navigation of autonomous mobile robots. Accurate path planning is very important for the robot to be able to track an optimal path between start position and goal position without collision with obstacles. According to [3], there are two main types of path planning: global path planning and local path planning. Global path planning needs all terrain to be static. In addition, there should be a complete awareness about the environment. In contrast, the implementation of the local path planning is while the robot is moving. Therefore, the local path planning has the ability to produce a new path when there is a change in the environment. For this project the local path planning will be used since the robot needs to check if there is an obstacle within its path to avoid it by change its path [3].
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Mobile Robot Multiple Sensor Behaviour For Obstacle Avoidance

Mobile Robot Multiple Sensor Behaviour For Obstacle Avoidance

William et al. had studies about implementation of a binaural sensory pod using an ultrasonic emitter and two receivers on a legged robot. A series of obstacle avoidance behavior is programmed onto a microcontroller that allows the robot to move both semi- autonomously and autonomously successfully programmed. From his studies, binaural ultrasonic sensor pod and programmed avoidance behavior has proven itself useful as a mobile robot navigation aid. By using the modular design implemented for these experiments, the sensor pods could be integrated with other mobile robots to provide non- contact sensing and navigation for them as well. [6].
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Odometry Algorithm with Obstacle Avoidance on Mobile Robot Navigation

Odometry Algorithm with Obstacle Avoidance on Mobile Robot Navigation

An autonomous robot is required to have navigation capability. Given a certain destination point in its environment, the robot is expected to reach the destination autonomously. Many algorithms with different degrees of complexities have been devised in the recent years. Some of them use fuzzy logic [1 – 3], genetic algorithms [4 – 6]. Some also stick on older algorithm like odometry. Odometry is a basic method of navigation, used by virtually all robots [7]. Odometry also proven successfully used on project like Mars Exploration Rovers [8].

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Interactive Online Learning for Obstacle Classification on a Mobile Robot

Interactive Online Learning for Obstacle Classification on a Mobile Robot

In our scenario, an autonomous robot is exploring a garden environment (Fig. 45) in a random scheme. The user interacts in real-time with the robot by labeling approached objects via an iPad. Labeled objects are incrementally incorporated into the model and learned immediately enabling a direct reaction of the system. New objects can be introduced at any time. Whenever an object is approached, the robot stops in front of it within a certain distance. If the user does not provide a label, the robot announces the recognized class. Unknown objects as well as uncertain classifications are expressed explicitly. Object specific actions are only executed in case of confident classifications. Actions may include oral comments as well as driving behaviors such as avoiding or driving over. Objects are always avoided whenever they are classified as unknown or uncertain. Since the garden border is treated as any other object, but coupled strictly with avoidance, the robot stays within the grass area.
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Autonomous Navigation and Obstacle Avoidance for a Wheeled Mobile Robots: A Hybrid Approach

Autonomous Navigation and Obstacle Avoidance for a Wheeled Mobile Robots: A Hybrid Approach

In this paper, an autonomous navigation and obstacle avoidance strategy is proposed for an omnidirectional mobile robot. The robot plans a path, starting from an initial point going to a target point. A hybrid approach has been developed where a global approach has been applied to the motion along the desired path (DP) using 2 nd order polynomial planning, while a local reactive approach is used to avoid collisions with static and/or dynamic obstacles based on the “sensing vector” and the “gap vector” concepts. The “sensing vector” is a binary vector which provides information about obstacles detection, while the “gap vector” provides information about a possible nearest gap the robot can pass through it.
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The Integration Of Fuzzy Logic System For Obstacle Avoidance Behaviour Of Mobile Robot

The Integration Of Fuzzy Logic System For Obstacle Avoidance Behaviour Of Mobile Robot

Despite the number of causalities, this deadly weapon is still widely used as a tactic to win a war. To solve this severe problem various method are using to remove the landmines from the contaminated area. The procedure of clearance of landmine should be well prepared and followed accordingly so that the confidence level that the area is free from landmine will be higher. The mines can be neutralized by either removal or detonation using various technique such as manual demining, the use of animals, insects and bacteria, mechanical demining and robot demining [4]. Eliminating the landmines using the robots is the ideal solution because this technique can improve the safety of personnel as well as the efficiency, productivity and flexibility of the work. Due to the uncertainty of the environment, using autonomous robots that able to coordinate their movement by avoiding obstacles and reaching the position of the landmine will speed up the process of detection and elimination of landmines [5], [6], and [7]. These robots also need a robust controller to analyze the input and output that help the mobile robot to move in an uncertain environment without colliding with any obstacles.
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Sonar sensor for an autonomous mobile robot

Sonar sensor for an autonomous mobile robot

The next topic will discuss on the selection of sensor suitable for obstacle detection and collision avoidance application for an autonomous mobile robot.... Newcastle University' 2.4.[r]

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Autonomous navigation with ROS for a mobile robot in agricultural fields

Autonomous navigation with ROS for a mobile robot in agricultural fields

Our ongoing aim to create a low-cost, fully au- tonomous mobile robotic platform, which can provide useful services to farmers and make full use of mod- ern robotic technology. In this paper, we describe the current state in development and technology of this practical rover that has the ability to localize itself through odometry and the ability to navigate reliably to locations in a farm field while using off the shelf parts in its construction without the need for high- priced inertial measurement, processing, and sensing hardware.

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Autonomous navigation with ROS for a mobile robot in agricultural fields

Autonomous navigation with ROS for a mobile robot in agricultural fields

Our ongoing aim to create a low-cost, fully au- tonomous mobile robotic platform, which can provide useful services to farmers and make full use of mod- ern robotic technology. In this paper, we describe the current state in development and technology of this practical rover that has the ability to localize itself through odometry and the ability to navigate reliably to locations in a farm field while using off the shelf parts in its construction without the need for high- priced inertial measurement, processing, and sensing hardware.

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Autonomous Mobile Robot Navigating Towards A Preset Target

Autonomous Mobile Robot Navigating Towards A Preset Target

to planning; know the current situation and mission goals, decide what to do next. Its locomotion will control the direction either steers to left or right. It will move at fixed speed. The robot will be equipped with sonar system, so if there was an obstacle in the middle of its way, its capable to avoid it either move to right or left. The robot’s also possible to reverse. It will be embodied in chassis. These projects are expected to be a basic autonomous project which it can be used for another research and for education. It can be expand or added such the robot can be control by using notebook.
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Wheeled Mobile Robot Obstacle Avoidance Using Compass and Ultrasonic

Wheeled Mobile Robot Obstacle Avoidance Using Compass and Ultrasonic

Abstract This paper presents the use of ARDUINO board to control an autonomous mobile robot (AMR) for navigation purpose. The wheeled robot is capable to perform two tasks. The first task is to move autonomously to the north direction. And the second task is to avoid collision with unexpected static and moveable obstacles. The robot uses two sensors to navigate and avoid the obstacle, a digital compass HMC5883L uses to detect the north direction and update the situation of the robot during its movement. And the ultrasonic sensor uses to avoid nearest obstacles on the robot way. C language used to program the system to do its mission and installed to the ARDUINO board. The results obtained show that the robot was able to navigate and move to the north and avoid obstacles in the outdoor environment.
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Obstacle Detection and Object Size Measurement for Autonomous Mobile Robot using Sensor

Obstacle Detection and Object Size Measurement for Autonomous Mobile Robot using Sensor

Robotics is a leading branch of engineering which demands knowledge of hardware, sensors, actuators and programming. The result is a system which can be made to do a lot of different things. However, to develop such a system is expensive and difficult. So, we have come up with a plan to build an autonomous mobile robot which is less expensive. A robot has three main different parts – preceptors, processors and actuators. The perceptors are the sensors which provide information about the surrounding environment to the robot. There are many works had done for object detection. Those robots are efficient in the purpose of accuracy. But they are very costly. So we move in a direction where we can have a robot that is cost effective and good enough in its accuracy. 1.1 Robot Navigation and Sensor Fusion Robot navigation algorithms are classified as global or local, depending on surrounding environment. In global navigation, the environment surrounding the robot is known and the path which avoids the obstacle is selected. In local navigation, the environment surrounding the robot is unknown, and sensors are used to detect the obstacles and avoid collision. [3] For global navigation, INS (Inertial Navigation System) or odometric system can be used [4]. INS uses the velocity, orientation, and direction of the robot to calculate the location of the robot relative to a starting position. In global environment, where the starting position, the goal and the obstacles are known, INS can lead a robot to its goal. But a major problem of INS is that it suffers from integration drift: small errors in the measurements accumulate to larger error in position. It is like letting a blindfolded man to navigate from point X to point Y in a known environment. He knows the way but he cannot see. He has to guess his location and decide the direction to move. With every guess, every error he makes is cumulated. By the time he thinks he has reached Y, his actual position may be quite a drift from Y.
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Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control

Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control

The main part of the project is to design a fuzzy logic controller, which is the main component of a fuzzy control system. More attention has been given to this part since the author has implemented a complete design process. The four elements, which compose the fuzzy controller, are discussed in detail. The designed controller will be used to control the navigation process of an autonomous mobile robot (AMR) m a completely unstructured environment.

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Path Planning and Obstacle Avoidance for Boe Bot Mobile Robot-

Path Planning and Obstacle Avoidance for Boe Bot Mobile Robot-

Many researches have investigated, during the last decades, the guidance of land autonomous vehicles, underseas robots, manipulators and walking machines. Many experiments have been carried out on real robots (wheeled mobile robots, and AUV) and on simulated ones [6], [3]. A real-time obstacle avoidance algorithm coupled with path following is studied and implemented in this paper.

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Mobile Robot Following Obstacle Avoidance And Collision

Mobile Robot Following Obstacle Avoidance And Collision

It is an increased in research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behaviour. Groups of mobile robots are constructed, with the aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems.

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Compressed Sensing Environmental Mapping by an Autonomous Robot

Compressed Sensing Environmental Mapping by an Autonomous Robot

Environmental mapping is essential to making truly autonomous robots and is critical for robotic exploration and discovery underwater, outdoors, within buildings, and in space [1]. However, the necessarily limited capabilities of mobile robots and inherent communication challenges of certain environments conspire to complicate the problem of mapping, even when the location of the robot(s) is known. For instance, autonomous underwater vehicles would be im- mensely useful for mapping natural resources and scientific data collection in the ocean [2], but returning the information to a land-based server is difficult because radio communica- tion is impractical underwater [3]. Acoustic communication is possible, but has greater bandwidth constraints and power requirements [4], hence there is a need to minimize data transmission. One approach would be to collect as much data as necessary and compress it prior to transmission, but this has significant drawbacks, as cost increases with the number of sensor nodes in a network and mobile robots are limited in their physical speed and data storage capability. A more promising solution is compressed sensing, a technique in which a relatively small amount of data is collected in order to reconstruct a higher-resolution signal or map. [5] considers the use of Random Access Compressed Sensing to create an energy-efficient network of static sensor nodes for oceanographic data collection, but in certain scenarios, such as exploration of unchartered territory, autonomous robots are clearly preferably to a network of fixed sensors. In this paper, we pioneer the use of mobile robots for compressed sensing environmental mapping. Section II describes the testbed hardware: a small wheeled robot tracked by an overhead camera measures the reflectivity of the testbed surface and sends the data to a central server. Section III documents the algorithm used by the server to reconstruct a complete map of the surface from the incomplete data collected by the robot. In Section IV we describe both simulated and physical simulation results, and we conclude
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Micro-Controller Based Obstacle Avoiding Autonomous Robot

Micro-Controller Based Obstacle Avoiding Autonomous Robot

The global focus on terrorism and security may have geared up following the 9/11 attacks in the USA. The risk of terrorist attack can perhaps never be eliminated, but sensible steps can be taken to reduce the risk. The word “Robot” was first used in a 1921 play titled R.U.R. Rossum’s Universal Robots, by Czechoslovakian writer Karel Capek. Robot is a Czech word meaning “worker.” Merriam-Webster defines robot [2] as “a machine that looks like a human being and perform various complex acts; a device that automatically performs complicated, often repetitive tasks; a mechanism guided by automatic controls.” ISO describes a robot as “an automatically controlled reprogrammable, multipurpose manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications”. Yet, all these definitions do give us a rough idea about what comprises a robot, which needs to sense the outside world and act accordingly. There are motors, pulleys, gears, gearbox, levers, chains, and many more mechanical systems, enabling locomotion. There are sound, light, magnetic field and other sensors that help the robot to collect information about its environment. There are Processors powered by powerful software that help the robot make sense environmental data captured and tell it what to do next and also microphones,
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