Global Hough localization for mobile robots in polygonal environments

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

A control strategy for real-time collision avoidance of a mobile robot in an environment containing moving obstacles is proposed. Objects, including the robot, are modelled as convex polyhedra. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from offline planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Consistency with the nominal plan is desirable. The process is formulated as an optimization problem and a close to optimal solution is obtained. >

This paper presents a robust method for localization of mobile robots in environments that may be cluttered and that not necessarily have a polygonal structure. The estimation of the position and orientation of the robot relies on the minimization of the modified Hausdorff distance between ladar range measurements and a map of the environment. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot in a populated environment, thus demonstrating that the approach provides a reliable and computationally feasible alternative for mobile robot localization and autonomous navigation.

A new hybrid navigation algorithm for mobile robots in environments with incomplete knowledge

Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area.

Purpose of reseach. The article deals with the study of the operation of the extended Kalman filter (EKF), supplemented with an adaptive digital filter in order to compensate for the error in the operation of the EKF when performing data integration of the mobile robot control system.Methods. The adaptive digital filter (ADF) is a self-tuning filter that iteratively changes its variable parameters to achieve the optimal desired values of the output data. The EKF supplemented with the ADF with the NLMS adaptation algorithm will be called the EKF–ADF system or the EKF+NLMS digital filter. An important task is the selection of the number of frames and ADF weighting coefficients at which the optimum quality of noise suppression and the convergence rate of the adaptation algorithm are achieved.Results. With various options of organizing the operation of the ADF buffer memory, the adjusted values for assessing the state of the ‘mobile robot–environment’ system may differ. When the number of input data frames and ADF weighting coefficients are small, low quality noise suppression will be observed. With an increase in the number of frames and weighting coefficients, the quality of noise suppression is improved, and the convergence rate of the adaptation algorithm decreases.Conclusion. The EKF+NLMS digital filter algorithm takes an intermediate place between the EKF algorithm and the serial filtering of the EKF and ADF signals with the NLMS algorithm according to the criteria for estimating the mean square error, mean absolute error, signal-to-noise ratio, and convergence rate.

Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.

A key issue in mobile robot applications involves building a map of the environment that may be used by the robot for path planning. In this paper, a new approach for uneven environment reconstruction based on the information supplied by a millimetre wave (MMW) radar has been proposed. The returned raw data from the MMW are stored in a database and then transferred to the CAD model, and represented as objects using an algorithm. The non-uniform rational B-splines have been used to extract the polygonal mesh decomposition equivalent to the 3D uneven environment and obstacle surfaces. The polygonal mesh decomposition helps to locate the different obstacles and to build the optimal path to reach the target starting from any initial position, by taking into consideration the terrain traversability. The optimal path is obtained by applying distance calculation method and speed limits. Indeed, the returned path is smooth which is a crucial performance consideration, especially for practical service robots.

Aiming at the problem of navigation in unknown environment of autonomous mobile robot, the self-made mobile robot was taken as research object. The integrated navigation technology was adapted to integrate the SLAM navigation system, DR navigation system and MEMS micro inertial navigation system possessed by the studied robot to construct the SLAM/DR/MEMS integrated navigation system of mobile robot. The experiments were carried to test the method, and the results show that the precision of navigation and location of mobile robot was developed by the integrated navigation scheme.

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

The rapid expansion in the industrial revolution has been aroused to utilize of the robot in various high accuracy and productivity applications. Optimal path planning is an important concern in navigation of mobile robot interesting with finding an optimal path. The firefly algorithm is considered as an increasingly promising tool of Swarm Intelligence, which is used in various optimization areas. The objective of the proposed method is to find the free-collision points in mobile robot environment and generate the optimal path based on firefly algorithm and D* algorithm. D* algorithm has been applied to find the shortest path. The essential function of applying the firefly algorithm and Quadratic parametric equations is to iterative globally search and determine the accurate positions of a set of intermediate via points within the free space and generating the corresponding smooth trajectory at a specified time respectively. The collision with obstacles can be efficiently avoided by constructing the free Cartesian space, which guarantee a free collision path planning. The simulation results illustrate the efficiency of proposed methods performance in finding and determining the optimal path and trajectory even in various degrees of environmental complexity.

In this paper, a multi-input multi-output (MIMO) noise suppression (NS) algorithm for sound source localization (SSL) is proposed in mobile robot environment. Especially for the mobile robot, some ego noise (e.g., motor noise) is naturally generated and it becomes a dominant signal at microphone array, which seriously damages the performance of the SSL. Therefore, the proposed MIMO NS algorithm is designed not only to suppress the noise but also to preserve spatial information of sound sources. To this end, the recent MIMO time-domain audio separation network (TasNet) with preserving spatial cues for binaural source separation is extended to tetrahedral microphone array in the proposed MIMO NS algorithm. Furthermore, weighted phase error (WPE) between inter-channels is additionally employed in the loss function, which can improve the performance especially in preserving inter-channel time difference (ITD). The performance of the proposed approach is verified within the context of the robot vacuum cleaner, which shows from simulations and real experiments that the proposed approach can preserve the spatial information as well as remove the vacuum noise.

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.

In this paper, the use of passive Radio Frequency Identification (RFID) as a support technology for mobile robot navigation and environment mapping is investigated. A novel method for localizing passive RFID tags in a geometric map of the environment using fuzzy logic is, first, described. Then, it is shown how a mobile robot equipped with RF antennas, RF reader, and a laser range finder can use such map for localization and path planning. Experimental results from tests performed in our institute suggest that the proposed approach is accurate in mapping RFID tags and can be effectively used for vehicle navigation in indoor environments.