Mobile Robot Obstacle Avoidance Research Articles

An Improved Dynamic Window Approach with Environment Awareness for Local Obstacle Avoidance of Mobile Robots

An Improved Dynamic Window Approach with Environment Awareness for Local Obstacle Avoidance of Mobile Robots

Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance

This paper addresses the trajectory tracking and obstacle avoidance control problems for a class of mobile robot systems. Two classes of controllers are designed for the mobile robot system in the free motion, respectively. A new hybrid position virtual-force controller is designed to adjust the distance between the mobile robot and the obstacles. Since the uncertainties between the mobile robot dynamics model and obstacles degrade the performance of the obstacle avoidance system, a multi-layer feed-forward neural networks (NNs) deep learning method with hybrid position and virtual-force is proposed, such that the distance between the mobile robot and the obstacles converges to an adjustable bounded region. It is shown that the proposed controller in this paper is smooth, effective, and only uses the system output. The control design conditions are relaxed because of the developed multi-layer feed-forward NNs deep learning compensator. The simulation results and obstacle avoidance cases are performed to show the effectiveness of the proposed method.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

Abstract This work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization

This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.

Research on simultaneous localization and mapping of indoor mobile robot

Simultaneous localization and mapping (SLAM) is the key technology to fulfill mobile robot obstacle avoidance and autonomous navigation. As cameras such as binocular cameras or RGB-D cameras exist wide-angle limitations, the use of such sensors prone to map mismatches and localization errors. In order to solve the aforementioned problems, 2D laser radar and odometer are used as the core sensor to design an experimental system for indoor SLAM, which is configured on a mobile robot to examine the effect of indoor SLAM. Through the Rao-Blackwellized [1] particle filter algorithm, the data of laser radar and odometer are fused with real-time processing, which realizes the construction of map as well as localization and navigation of the mobile robot in the unknown environment. Experiments on simulation and physical verification were carried out with open source robot operating system (ROS). The simulation and experimental results confirm the feasibility and practicability of the platform.

Real-time obstacle avoidance of mobile robots using state-dependent Riccati equation approach

In this paper, state-dependent Riccati equation (SDRE) method-based optimal control technique is applied to a robot. In recent years, issues associated with the robotics have become one of the developing fields of research. Accordingly, intelligent robots have been embraced greatly; however, control and navigation of those robots are not easy tasks as collision avoidance of stationary obstacles to doing a safe routing has to be taken care of. A moving robot in a certain time has to reach the specified goals. The robot in each time step needs to identify criteria such as velocity, safety, environment, and distance in respect to defined goals and then calculate the proper control strategy. Moreover, getting information associated with the environment to avoid obstacles, do the optimal routing, and identify the environment is necessary. The robot must intelligently perceive and act using adequate algorithms to manage required control and navigation issues. In this paper, smart navigation of a mobile robot in an environment with certain stationary obstacles (known to the robot) and optimal routing through Riccati equation depending on SDRE is considered. This approach enables the robot to do the optimal path planning in static environments. In the end, the answer SDRE controller with the answer linear quadratic controller will be compared. The results show that the proposed SDRE strategy leads to an efficient control law by which the robot avoids obstacles and moves from an arbitrary initial point × 0 to a target point. The robust performance of SDRE method for a robot to avoid obstacles and reach the target is demonstrated via simulations and experiments. Simulations are done using MATLAB software.

Application of matching optical flow method in obstacle avoidance of mobile

Optical flow method is an important method to study the obstacle avoidance of mobile robot. Optical flow calculation is the core problem of robot obstacle recognition, which can be used to realize the obstacle avoidance by using the balance strategy after calculating the optical flow. In this paper, a point matching method based on optimal estimation is used. It is based on a certain similarity between the two points of the optimal estimation of the characteristics, so as to find out the characteristics of the points and calculate the optical flow method. This method has a good performance in the research of moving target detection. In this paper, we apply it to the research of obstacle avoidance, which can improve the speed of optical flow calculation and make the obstacle avoidance process faster.

Wheeled Mobile Robot Obstacle Avoidance Using Compass and Ultrasonic

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.

Obstacle avoidance of mobile robot based on study of human obstacle avoidance by simulator using image

Obstacle avoidance of mobile robot based on study of human obstacle avoidance by simulator using image

Physical-consistent behavior embodied in B-spline curves for robot path planning

Abstract In this paper, a path planning algorithm for smooth obstacle avoidance of mobile robots is presented and discussed. The idea is based on B-splines, which are trajectories defined by a linear combination of basis functions weighted by constants called control points. Since the overall curve depends locally only on the position of a limited set of control points, B-splines are ideal tools for generating online reference trajectories able to react to local changes of the environment (e.g. mobile or spawning obstacles). The via-points of the reference B-spline are treated as dynamic agents with customized dynamic properties. These agents interact with each other and with the environment in such a way to generate the correct reference for the B-spline generator and thus for the robot. The high flexibility and the low computational burden of the proposed algorithm allow to develop applications in partially unknown or dynamically changing environments since a local modification of the trajectory does not require an entire re-computation. Experimental results performed with a KUKA Youboot mobile robot within a ROS based set-up are reported.

Dynamic Obstacle Avoidance of Mobile Robot by Tube-based Model Predictive Control

Dynamic Obstacle Avoidance of Mobile Robot by Tube-based Model Predictive Control

Obstacle Avoidance and Navigation Planning of a Wheeled Mobile Robot using Amended Artificial Potential Field Method

Trajectory planning is a prime method in the research on mobile robot navigation. Sampling-based algorithms can generate trajectories which help the robot to reach the target avoiding obstacles. In this proposed work, an Enhanced Artificial Potential Field (E-APF) generates the trajectories for mobility of mobile robots and simultaneously guarantees the effectiveness and continuity of the trajectory. Aiming at the problem that the classical APF cannot adapt to the complex trajectory planning and fall a as prey to the local optimal solution, E-APF method is proposed for Wheeled Mobile Robot (WMR) route planning. In this research work, this method does not consider the influence of traditional attraction and repulsive forces. The repulsive potential is built by repulsive function for discretizing outline of an arbitrarily shaped obstacle with their boundary points. This process describes the workspace of the wheeled mobile robot more precisely. The reliability is proved for most of the cases by discussing the convergence of this proposed technique. Finally, an efficient obstacle avoidance based action has been performed in the chosen navigable trajectory. Trajectories that have been generated using the proposed E-APF satisfy constraints approach of the direction on both the starting and goal points. Consequently, the trajectories that are generated by the Wheeled Mobile Robot are geometrically and dynamically feasible. Simulation results performed confirm the viability of the proposed E-APF algorithm that it can be effectively utilized in trajectory planning of wheeled mobile robots and can be applied in real-time scenarios.

An Adaptive Unscented Kalman Filter-based Controller for Simultaneous Obstacle Avoidance and Tracking of Wheeled Mobile Robots with Unknown Slipping Parameters

A novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problems of a wheeled mobile robot (WMR) with unknown wheeled slipping. The longitudinal and lateral slipping are processed as three time-varying parameters and an Adaptive Unscented Kalman Filter (AUKF) is designed to estimate the slipping parameters online More specifically, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the Unscented Kalman Filter (UKF) context. A stable unified controller is applied to simultaneously handle tracking and obstacle avoidance for this WMR system to compensate for the unknown slipping effect. Applying Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotically convergent regardless of unknown slipping, the tracking errors converge to the zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated through simulation and experimental results.

Combing VFH with bezier for motion planning of an autonomous vehicle

Vector Field Histogram (VFH) is a method for mobile robot obstacle avoidance. However, due to the nonholonomic constraints of the vehicle, the algorithm is seldom applied to autonomous vehicles. Especially when we expect the vehicle to reach target location in a certain direction, the algorithm is often unsatisfactory. Fortunately, the Bezier Curve is defined by the states of the starting point and the target point. We can use this feature to make the vehicle in the expected direction. Therefore, we propose an algorithm to combine the Bezier Curve with the VFH algorithm, to search for the collision-free states with the VFH search method, and to select the optimal trajectory point with the Bezier Curve as the reference line. This means that we will improve the cost function in the VFH algorithm by comparing the distance between candidate directions and reference line. Finally, select the closest direction to the reference line to be the optimal motion direction.

Direct Optimal Motion Planning for Omni-directional Mobile Robots under Limitation on Velocity and Acceleration

This paper describes a low computational direct approach for optimal motion planning and obstacle avoidance of Omni-directional mobile robots within velocity and acceleration constraints on the robot motion. The main purpose of this problem is the minimization of a quadratic cost function while limitation on velocity and acceleration of robot is considered and collision with any obstacle in the robot workspace is avoided. This problem can be formulated as a constraint nonlinear optimal control problem. To solve this problem, a direct method is utilized which employs polynomials functions for parameterization of trajectories. By this transforming, the main optimal control problem can be rewritten as a nonlinear programming problem (NLP) with lower complexity. To solve the resulted NLP and obtain optimal trajectories, a new approach with very small run time is used. Finally, the performance and effectiveness of the proposed method are tested in simulations and some performance indexes are computed for better assessment. Furthermore, a comparison between proposed method and another direct method is done to verify the low computational cost and better performance of the proposed method.

Real-time Obstacle Avoidance of Non-holonomic Mobile Robots Using Expanded Guide Circle Method

Real-time Obstacle Avoidance of Non-holonomic Mobile Robots Using Expanded Guide Circle Method

CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot

Exploration in a known or unknown environment for a mobile robot is an essential application. In the paper, we study the mobile robot obstacle avoidance problem in an indoor environment. We present an end-to-end learning model based Convolutional Neural Network (CNN), which takes the raw image obtained from camera as only input. And the method converts directly the raw pixels to steering commands including turn left, turn right and go straight. Training data was collected by a human remotely controlled mobile robot which was manipulated to explore in a structure environment without colliding into obstacles. Our neural network was trained under caffe framework and specific instructions are executed by the Robot Operating System (ROS). We analysis the effect of the datasets from different environments with some marks on training process and several real-time detect experiments were designed. The final test result shows that the accuracy can be improved by increase the marks in a structured environment and our model can get high accuracy on obstacle avoidance for mobile robots.

Obstacle avoidance for mobile robot based on improved dynamic window approach

Obstacle avoidance for mobile robot based on improved dynamic window approach

不確実な環境下の移動ロボットへ向けたロバストモデル予測制御の性能評価

This paper presents the performance evaluation of robust model predictive control (robust MPC) for a mobile robot. The mobile robot is affected by stationary or moving obstacles and exogenous disturbances, which are road surface slipperiness, fluid resistance under water and current disturbance. Since these disturbances make it difficult to accurately control the trajectory of the mobile robot, robustness is required in the control framework. The robust MPC is one of the methods for mobile robot control. Several previous studies about the robust MPC have mainly focused on Gaussian noise. We conduct a simulation to test mobile robot obstacle avoidance under three different non-Gaussian noise environments, and confirm that the robust MPC is effective given the exogenous disturbances of affecting the mobile robot.

Mobile Robot Obstacle Avoidance Algorithms Based on Information Fusion of Vision and Sonar

In view of the problem of AS-R autonomous wheeled mobile robot obstacle avoidance, a rapid convergence of sonar and binocular stereovision sensor distance information in order to detect and avoid obstacle algorithm is proposed in this paper. The algorithm first uses binocular camera (CCD) to get three-dimensional image of the real environment, through the stereo matching and V-disparity method which is used to calculate disparity map, then obstacles is extracted by Hough lines detection algorithm, finally we will get information about obstacles and sonar return information with T-S fuzzy neural network fusion, then it will output walking controlled decisions. Experimental results proved that the algorithm is effective and practical.