Mobile Robot For Obstacle Avoidance Research Articles

Modular System to Train and Test an Evolutionary Mobile Robot for Obstacle Avoidance

Modular System to Train and Test an Evolutionary Mobile Robot for Obstacle Avoidance

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

PurposeWheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments.Design/methodology/approachGiven a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR.FindingsThe authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments.Originality/valueIn this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

Implementation of Robot Operating System in Beaglebone Black based Mobile Robot for Obstacle Avoidance Application

The Robot Operating System (ROS) is a collection of tools, libraries, and conventions that focus on simplifying the task of creating a complex and advanced robotics system. Its standard framework can be shared with another robotics system that has a similar platform and suitable for being introduced as an educational tool in robotics. However, the problems found out in the current robot platform available in the market are expensive and encapsulated. The development of an open source robot platform is encouraged. Therefore, this research is carried out to design and develop an ROS based obstacle avoidance system for existing differential-wheeled mobile robot. The ROS was installed under Ubuntu 14.04 on a Beaglebone Black embedded computer system. Then, the ROS was implemented together with the obstacle avoidance system to establish the communication between program nodes. The mobile robot was then designed and developed to examine the obstacle avoidance application. The debugging process was carried out to check the obstacle avoidance system application based on the communication between nodes. This process is important in examining the message publishing and subscribing from all nodes. The obstacle avoidance mobile robot has been successfully tested where the communication between nodes was running without any problem.

Design and Fabrication of an Autonomous Mobile Robot for Obstacle Avoidance and Path Planning in Partially Known Environment

In this paper, an autonomous mobile robot has been designed and fabricated which can be used in both indoor and outdoor for industrial and household applications. Here using six servo motors and four DC motors with their controllers (servo controller and L293D DC Motor controller) the mobile robot can pick any object from its workspace and by avoiding collision it can place the object in the desired location. ASCII ultrasonic sensor and motion sensor are used along with ATmega 2560 microcontroller which is programmed to take the sensors output as its input and controls the dc motor and servo motors to pick and place objects and avoid obstacle during motion of the mobile robot. Here low-cost solar panels have been used to recharge the Li-ion batteries used for the motors and microcontroller in case of outdoor environment. The obstacle avoidance and path planning algorithms have been developed and a case study has been presented in this paper.

Intelligent omni-directional vision-based mobile robot fuzzy systems design and implementation

An evolutional particle swarm optimization (PSO)-learning algorithm is proposed to automatically generate fuzzy decision rules. Due to the development of the fuzzy rule-based system, it actually regulates the omni-directional vision-based mobile robot for obstacle avoidance and desired target approximation as soon as possible. In the proposed image processing algorithm, an image direct transformation method is applied to convert the omni-directional scene into panoramic normal-view. Thus, the objects positions of obstacle and target are detected by the proposed color image segmentation. Human knowledge-based fuzzy systems demonstrate their well adaptability for nonlinear and time-variant features of the mobile robot to actually approach the desired location whatever it is surrounded in a known or unknown environment. In software simulations, the omni-directional mobile robot can move toward desired targets from different initial positions and various block sizes. In hardware implementations, the fuzzy control system embedded in actual mobile robot platform is used to real-time manipulate the omni-directional wheels through the motor drivers by the captured image positions of the obstacle and target. The selected fuzzy rules are efficient to control the direction and speed of omni-directional wheels to achieve the desired targets.

원 궤적 경로 기법을 이용한 이동로봇의 주행

In this paper, we proposed a navigation algorithm of the mobile robot for obstacle avoidance using a circular path planning method. The proposed method makes circular paths in order to avoid obstacles in the front side of the mobile robot. An optimal path for approaching to the target is selected and the linear and angular speeds for stable moving of the mobile robot are controlled. Obstacles are detected by image processing which reduce image data obtained from a web camera. Performance of the proposed algorithm is shown by experiments with application to the Pioneer-2DX mobile robot.

2P2-G05 強化学習を用いた移動ロボットの障害物回避軌道の探索 : A*アルゴリズムと強化学習の統合(進化・学習とロボティクス)

2P2-G05 強化学習を用いた移動ロボットの障害物回避軌道の探索 : A*アルゴリズムと強化学習の統合(進化・学習とロボティクス)

Development of Autonomous Mobile Robot for Obstacle Avoidance

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

Fuzzy control of a mobile robot for obstacle avoidance

This paper discusses fuzzy control for obstacle avoidance of a mobile robot which has two independent driving wheels. The feedback control system is used to maintain speed relatively constant even though disturbing torques may occur. Therefore, the control inputs in order to avoid obstacles, which are outputs of the fuzzy controller, are commands for the speed control system of the left wheel and the right one. The inputs to the fuzzy controller are derived by processing the floor image in front of the robot, which is obtained with a CCD camera. Fuzzy control rules which were derived on the basis of a human's driving actions were modified by computer simulation. Experiments were performed on a robot constructed for the purpose.