Wall-following Algorithm Research Articles

Path planning of a mobile robot using an improved mixed-method of potential field and wall following

The existing Bug algorithms, which are the same as wall-following algorithms, offer good performance in solving local minimum problems caused by potential fields. However, because of the odometer drift that occurs in actual environments, the performance of the paths planned by these algorithms is significantly worse in actual environments than in simulated environments. To address this issue, this article proposes a new Bug algorithm. The proposed algorithm contains a potential field function that is based on the relative velocity, which enables the potential field method to be extended to dynamic scenarios. Using the cumulative changes in the internal and external angles and the reset point of the robot during the wall-following process, the condition for state switching has been redesigned. This improvement not only solves the problem of position estimation deviation caused by odometer noise but also enhances the decision-making ability of the robot. The simulation results demonstrate that the proposed algorithm is simpler and more efficient than existing wall-following algorithms and can realise path planning in an unknown dynamic environment. The experimental results for the Kobuki robot further validate the effectiveness of the proposed algorithm.

Autonomous piloting in a beating heart

Medical Robotics Catheter navigation in minimally invasive procedures is challenging. Fagogenis et al. trained a robotic catheter to identify blood, heart tissue, and an artificial aortic valve using a combination of visual and force data, an approach called haptic vision. They then programmed wall-following algorithms that directed the robot through a beating pig heart in vivo. The robot autonomously moved along the ventricular wall, to and around the valve, and to specific paravalvular leak sites, with a performance comparable to that of handheld catheter navigation and joystick control of the robot. Sci. Robot. 4 , eaaw1977 (2019).

Autonomous Robotic Intracardiac Catheter Navigation Using Haptic Vision.

While all minimally invasive procedures involve navigating from a small incision in the skin to the site of the intervention, it has not been previously demonstrated how this can be done autonomously. To show that autonomous navigation is possible, we investigated it in the hardest place to do it - inside the beating heart. We created a robotic catheter that can navigate through the blood-filled heart using wall-following algorithms inspired by positively thigmotactic animals. The catheter employs haptic vision, a hybrid sense using imaging for both touch-based surface identification and force sensing, to accomplish wall following inside the blood-filled heart. Through in vivo animal experiments, we demonstrate that the performance of an autonomously-controlled robotic catheter rivals that of an experienced clinician. Autonomous navigation is a fundamental capability on which more sophisticated levels of autonomy can be built, e.g., to perform a procedure. Similar to the role of automation in fighter aircraft, such capabilities can free the clinician to focus on the most critical aspects of the procedure while providing precise and repeatable tool motions independent of operator experience and fatigue.

Teaching Introductory Robotics Programming: Learning to Program with National Instruments' LabVIEW

This article considers strategies for teaching beginning students how to program mobile robots for autonomous operation. Many high school and beginning undergraduate students desire to learn about robotics, but they may lack the required knowledge. Experiences from an undergraduate course are described to illustrate the robot, its programming environment, software design, and algorithms, which faculty can use to guide beginning students from a place of no prior experience to writing impressive, autonomous mobile-robot programs. Autonomous algorithms that perform well and are appropriate for beginning students, including a new wall-following algorithm, are reviewed.

SIMULATION OF LANDMARK APPROACH FOR WALL FOLLOWING ALGORITHM ON FIRE-FIGHTING ROBOT USING V-REP

Autonomous mobile robot has been implemented to assist humans in their daily activity. Autonomous robots have also contributed significantly in human safety. Autonomous mobile robot have been implemented to assist humans in their daily activity. Autonomous robots Have also contributed significantly in human safety. An example of the autonomous robot in the human safety sector is the fire fighting robot, which is the main topic of this paper. As an autonomous robot, the fire fighting robot needs a robust navigation ability to execute a given task in the shortest time interval. Wall-following algorithm is one of several navigating algorithm that simplifies this autonomous navigation problem. As a contribution, we propose two methods that could be combined to make the existing wall-following algorithm more robust. The combined wall-flowing algorithm will be compared to the original wall-following algorithm. By doing so, we could determine which method has more impact on the robot’s navigation robustness. Our goal is to see which method is more effective when combined with the wall-following algorithm.

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autonomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh et al., 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.

Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming

This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.

A Reactive Wall Following Algorithm and Its Behavior of an Autonomous Mobile Robot with Sonar Ring

A robust wall following algorithm for an autonomous mobile robot with a sonar ring is presented. A sonar ring consists of multiple ultrasonic range sensors which are arranged on a disc. The proposed wall-following algorithm has an ability to make a robot follow walls in various shapes. The algorithm is described as a collection of reactions based on the data from the sonar ring. The autonomous mobile robot ""Yamabico"" is used to demonstrate the experimental behaviors of the proposed algorithm. Several experimental examples of the behaviors with this autonomous mobile robot are illustrated in this paper.