Mobile Robot Obstacle Avoidance Research Articles

Design of Path Planning and Obstacle Avoidance for a Wheeled Mobile Robot

This paper proposed a path tracking method for wheeled mobile robot (WMR). This method includes path planning and controller design. In path planning, using B-spline instead of A* algorithm to create smooth and obstacle-avoidance path, so that the possibility of collision can be reduced statistically. In controller design, genetic algorithm (GA) and fuzzy logic control (FLC) are combined together for the velocity control for WMR, which is supposed to move in a certain environment. The suitable membership function of FLC according to the feature of environment, such as distance and angle of WMR, is based on the B-spline path, and the input and output of FLC can be adjusted by GA. Therefore, the WMR can move along the planned path steady, which means our method has good performance.

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.

Indoor Mobile Robot Navigation and Obstacle Avoidance Using a 3D Camera and Laser Scanner

Thanks to the developing sensor technology in mobile robot navigation and obstacle avoidance there are new and affordable solutions which can be used to accomplish smooth, safe and reliable robot movement in an indoor environment. A laser scanner is a commonly used sensor for detecting obstacles as well as for navigation, but a laser scanner is unable to detect all the different obstacles and terrain features by itself. In this article complementary sensors are examined, on a real mobile robot platform. Communication between the sensors and the controller and the basic control possibilities are also discussed. Finally, an advertising mobile robot is described.

Distance Determination Method for Normally Distributed Obstacle Avoidance of Mobile Robots in Stochastic Environments

Obstacle avoidance methods require knowledge of the distance between a mobile robot and obstacles in the environment. However, in stochastic environments, distance determination is difficult because objects have position uncertainty. The purpose of this paper is to determine the distance between a robot and obstacles represented by probability distributions. Distance determination for obstacle avoidance should consider position uncertainty, computational cost and collision probability. The proposed method considers all of these conditions, unlike conventional methods. It determines the obstacle region using the collision probability density threshold. Furthermore, it defines a minimum distance function to the boundary of the obstacle region with a Lagrange multiplier method. Finally, it computes the distance numerically. Simulations were executed in order to compare the performance of the distance determination methods. Our method demonstrated a faster and more accurate performance than conventional methods. It may help overcome position uncertainty issues pertaining to obstacle avoidance, such as low accuracy sensors, environments with poor visibility or unpredictable obstacle motion.

人間の障害物回避軌道の調査に基づいたファジィ推論による移動ロボットの障害物回避

人間の障害物回避軌道の調査に基づいたファジィ推論による移動ロボットの障害物回避

Path planning and obstacle avoidance for omni-directional mobile robot based on Kinect depth sensor

To enable robots to autonomously move and skirt obstacles in the field, the robots must be designed so that they can exercise self-localisation and path planning functions based on the results of sensor signal processing or image analysis. This research combines the Kinect depth sensor with the omni-directional camera to construct the vision system of FIRA RoboSot. We use the above two complementary devices to enable robots to position themselves and to detect their distance from obstacles. The data captured can be processed and used to construct the required digital map, assisting robots in completing path planning. For testing the performance of our approach, some experiment results show the ability of the proposed system to perform robust obstacle avoidance under indoor conditions. All these results have been proven for their excellence and feasibility.

Automatic Docking with Obstacle Avoidance of a Differential Wheel Mobile Robot

Automatic Docking with Obstacle Avoidance of a Differential Wheel Mobile Robot

Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance.

An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot.

Research on Autonomous Obstacle Avoidance of Mobile Robot Based on Hybrid System

The working environment of mobile robot has become more and more complex. Through researching on hybrid system, this paper presents a system of autonomous obstacle avoidance for mobile robot. A series of autonomous navigation behaviors is designed individually. By using hybrid automata, these designed autonomous navigation behaviors fuse together in the autonomous obstacle avoidance system. The Matlab simulation shows that the whole system is effective and feasible.

Nonlinear Control for Tracking and Obstacle Avoidance of a Wheeled Mobile Robot With Nonholonomic Constraint

This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme.

Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.

Path Planning and Obstacle Avoidance for Mobile Robots in a Dynamic Environment

Because traditional obstacle avoidance path planning methods have a lot of problems, such as large amount of calculation, low efficiency, poor optimization capability, and lack of dealing with dynamic obstacles, a new method which implements real-time path planning of mobile robot is presented. The method builds a neural network model for the robot workspace, and then it uses the model to obtain the relationship between the dynamic obstacles and the network output. It can choose the local optimal collision-free path by the path planning in a dynamic environment (PPIDE) algorithm to find the path between two points for dealing with obstacles. The proposed method is suitable for dynamic environment where both linear and planar obstacles exist. Simulation results prove its effectiveness.

Mobile Robot Navigation using Fuzzy Limit-Cycles in Cluttered Environment

— This paper proposes a hybrid approach based on limit-cycles method and fuzzy logic controller for the problem of obstacle avoidance of mobile robots in unknown environment. The purpose of hybridization consists on the improvement of basic limit-cycle method in order to obtain safe and flexible navigation. The proposed algorithm has been successfully tested in different configurations on simulation.

A Mobile Robot Obstacle Avoidance Using Fuzzy Logic and Model Predictive Control

This paper presents a method of solving the problem of mobile robot Obstacle avoidance and path planning in an unknown dynamic environment. A linear model of the two-wheeled nonholonomic robot controlled using Model predictive control controller. For obstacle avoidance Fuzzy logic control is used. The ultrasonic sensors are used for positioning and identifying an obstacle. The proposed method is successfully tested in simulations. Obstacle avoiding technique is very useful in real life, this technique can also use as a vision belt of blind people by changing the IR sensor by a kinetic sensor ,which is on type of microwave sensor whose sensing range is very high and the output of this sensor vary in according to the object position changes.

Dynamic Obstacle Avoidance Algorithm for the Mobile Robot Based on Depth Image of Kinect Sensor

In order to solve the problem of the dynamic obstacle avoidance of the mobile robot in indoor environment, a new approach based on depth information is presented in this paper. The depth information of surrounding environment was collected and used to set the robots obstacle avoidance warning area by a Kinect sensor. When the moving obstacle accessed into the warning area, the robots obstacle avoidance direction was determined preliminary by the obstacles position, and then an improved Kalman filter algorithm was used to optimize the avoidance path. Experiments show that this approach can overcome the potential problem of path selection, and realize the mobile robot obstacle avoidance behavior in the dynamic environment.

Robust ground plane region detection using multiple visual cues for obstacle avoidance of a mobile robot

SUMMARYThis paper presents a vision-based obstacle avoidance design using a monocular camera onboard a mobile robot. A novel image processing procedure is developed to estimate the distance between the robot and obstacles based-on inverse perspective transformation (IPT) in an image plane. A robust image processing solution is proposed to detect and segment a drivable ground area within the camera view. The proposed method integrates robust feature matching with adaptive color segmentation for plane estimation and tracking to cope with variations in illumination and camera view. After IPT and ground region segmentation, distance measurement results are obtained similar to those of a laser range finder for mobile robot obstacle avoidance and navigation. The merit of this algorithm is that the mobile robot can have the capacity of path finding and obstacle avoidance by using a single monocular camera. Practical experimental results on a wheeled mobile robot show that the proposed imaging system successfully obtains distances of surrounding objects for reactive navigation in an indoor environment.

Study on Improved APF Algorithm for Autonomous Mobile Robot

In algorithms of obstacle avoidance for autonomous mobile robot, APF algorithm is simple, real-time and smooth, but has some limitations for solving problems. For example, the local minimum point may trap mobile robots before reaching its goal. Even though many improved APF algorithms have been put forward, few articles describe the process in detail to show how these algorithms are applied. Considering above factors, this paper focuses on embodiment of abstract improved theory for APF algorithm by showing some changes with formulas and parameters. The whole work has been done in simulation environment. According to the results this paper draws a conclusion.

A New Approach for Autonomous Robot Obstacle Avoidance Using PSD Infrared Sensor Combined with Digital Compass

This paper presents a new approach for obstacle avoidance of small mobile robots, which combine the position sensitive detector (PSD) with digital compass. It is important for an autonomous robot to explore its surroundings in performing the task of localization and navigation for searching. Because of the complexity of the environment, one simple kind of sensors is not sufficient for robot to accomplish these tasks. In this paper, the small mobile robots are enabled to identify barriers and distinguish surroundings by using the angle signal from the digital compass which is generally mounted on the robot. Experimental results indicate that this approach based on digital compass shows great potential in autonomous robot obstacle avoidance.

A Mobile Robot Obstacle Avoidance Method Based on Improved Potential Field Method

Proposing an obstacle avoidance method for mobile robot under unknown environment, which makes use of multiple ultrasonic sensors coordinating with each other and collects position information of obstacles followed by information fuzzy processing, applies artificial potential field method with improved potential function to project the travel path of the robot. It has solved the "deadlock" problem of the traditional artificial potential field method and achieved obstacle avoidance of mobile robot under unknown environment. By simulation analysis, robot obstacle avoidance can be implemented flexibly using this method.

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

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 and the concepts. The sensing is a binary vector which provides information about obstacles detection, while the provides information about a possible nearest gap the robot can pass through it. Keywordsmobile robot; autonomous navigation; path planning ; obstacle avoidance; hybrid approach.