The path planning algorithms for a mobile robot based on the occupancy grid map of the environment — A comparative study

The article describes a new auto-charging system designed to enhance successful docking rates for mobile robots while at the same time monitoring the status of the charging current between the mobile robot and the docking station. The communication interface of the mobile robot and the docking station uses a wireless RF interface. The docking station is designed with one active degree of freedom and two passive degrees of freedom. Users tune the location of the charging pins on the docking station to make it possible for the mobile robot to execute an auto-recharging process. The auto-recharging process uses multiple sensors and a laser range finder located on the mobile robot. The laser range finder searches for a landmark to guide the mobile robot towards the docking station. In the experiment, the power of the mobile robot is under its threshold value. The mobile robot transmits a charging command to the docking station via a wireless RF interface. The docking station transmits a location command back to the mobile robot via this wireless RF interface. The mobile robot uses its laser range finder to search for the landmark of the assigned docking station and programs a motion trajectory to move forward to the docking station. The docking station supplies a charging current to the mobile robot by means of a charger. The power detection module simultaneously detects the charging current and the voltage values of the charging process in both the docking station and the mobile robot. The system can monitor the status of the charging process between the docking station and the mobile robot at any time. The power of the mobile robot is enough to be detected by the power detection module. When the charging process is complete, the docking station turns off the charging current and triggers the mobile robot to leave the docking station via the wireless RF interface.

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

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

Robust simultaneous localization and map building (SLAM) is a key issue for mobile robot in presence of faults. In the paper, an adaptive evolutionary particle filter is designed to achieve robust SLAM for wheeled mobile robot when the laser range finder is subjected to errors. Firstly, a robust perception model for laser range finder is presented. The robustness of the provided model is two folds, (1) error beams of laser range finder are filtered out with segment analysis method, and (2) beams occluded by dynamical objects are filtered out with a high pass filter. Secondly, an adaptive mutation scheme is adopted to recover the diversity of the particles after resampling stage. Lastly, the presented method is testified in a real mobile robot.

This paper proposes map building method. Sensors such as a laser range finder, a gyroscope, encoders multiply compose the system. Generally, mobile robot can measure its relative position using a gyroscope and encoders in an environment. However, in this case, a large number of errors occur due to accumulative errors of sensor over time. Therefore, a map based on feature points is used. And an absolute position is measured by the feature points and geometrics. As combining the relative position and the absolute position, mobile robot can recognize its position. According to compositional data of a map, in case of a laser range finder, it takes a long time when adding a map or calculating a position of mobile robot. Therefore, it is necessary to arrange the feature points for computational time and map building. In this paper, a map was formed by laser range finder and features of geometrics. As a result of proposed method, the map was built efficiently in an aspect of time.

The recent research trend in deploying mobile robots in the indoor environments require the development of efficient map presentation techniques. A novel method is proposed for building maps of the indoor structured environments by using line-segment relation extracted from laser range scans. Compared with the more common grid representation, the map built with the line segments is more compact and more suitable for the structured environments. The core of the method consists of three parts: local map building, robot pose estimation, and global map update. Compared to two other traditional methods, the proposed method is effective and robust for the indoor environment mapping.

Human-following in a cluttered environment is one of the challenging issues for mobile service robot applications. Since a laser range finder (LRF) is commonly installed for autonomous navigation, it is advantageous to adopt an LRF for detection and tracking humans. In this paper, we aim at the reliable human tracking performances in a dynamic cluttered environment. The key idea is to develop a hierarchical sample-based joint probabilistic data association filter (HSJPDAF) by focusing on the leg positions as well as human positions. The proposed HSJPDAF consists of two levels in order to consider the interdependence between targets. Possible locations of multiple human targets can be simultaneously estimated on the basis of Bayesian filtering. Comparison with the general technique was carried out to verify the performance of HSJPDAF in both artificial indoor and real-world environments. Owing to the hierarchical framework, the proposed method shows the improved robustness by reducing the target loss rate significantly in a dynamic cluttered environment.

Developing autonomous mobile robots that can coexist with human in populated environments is still considered a big challenge. To address this problem the authors propose a novel scheme to assist mobile robots by providing localization information externally. This scheme combines the autonomous navigation and target tracking research fields to arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment is sensed using a laser range finder and camera based sensor unit. Using Rao-Blackwellized particle filter technique, the robots that need assistance are continuously tracked. In contrast with conventional laser range finder based tracking systems, the placement of sensor is changed to a level above average human height and the mobile robots are modified by attaching a cylindrical pole. The experiments show the validity of the proposed scheme for simultaneous localization assistance for multiple mobile robots. Two mobile robots were simultaneously navigated in given trajectories using assistance data, successfully.

A new concept for safely performing and qualitatively evaluating assessments in domestic unsupervised environments, especially when utilizing mobile service robots, is presented. The presented approach is based on the idea that classical geriatric assessments, especially from the domain of mobility, may be divided in components which happen naturally throughout the day in domestic environments. Those components are measured separately and are recombined to com-plete assessment tests later on. In order for physicians to decide how reliable assessment results from the domestic do-main are, we define technical quality criteria for the components of the Timed Up&Go assessment test. The approach is evaluated within an experiment in a living lab utilizing sensors, especially a laser range scanner, equipped with a mobile robot. Results show that the presented approach may be used to separate sensor measurements promising good assess-ment results from those containing insufficient data. Additionally, using mobile robots to perform assessments in domestic environments holds the potential danger for the inhabitant to stumble over the robot. Therefore, the paper also evaluates the aspect of inhabitants’ safety during domestic assessments based on the experiment’s data. A novel approach to navigate safely using the previously presented approach of optimal observation lots (OOL) is presented.

Local Map Building for Mobile Robot Autonomous Navigation by Using a 2D Laser Range Sensor

The objective of this study is to develop a robust human tracking method for mobile robot using only one laser range finder (LRF). When a mobile robot runs along a human, it is always necessary to observe positions of the human. Therefore, the LRF must be mounted on a position easy to recognize human. Furthermore, the LRF may lose the position of the target human, and another human might enter between the robot and the target human. Therefore, this paper discusses the mounting position of the LRF, as well as a robust tracking method using the LRF.

The automated inspection is desired to detect the vehicle in large parking space, which is difficult to survey by fixed the vision-sensor. We purposed mobile robot surveillance in parking space for parking lot inspection. To obtain the parking lot information, vision sensor is wide useful for parking lot state checking which measurement is difficult to obtain the positioning of vehicle while achieved by the laser range finder (LRF). In this research aims a laser range finder is acquired the positioning with directly measure the object in range scanner, which is obtains the data in the parking place. This paper purposed on ability of a LRF sensor with including reflection intensity data for data measuring in the parking lot by using a mobile robot which performs patrolling on multiple times in parking place for a parking lot inspection. We present experimental results to evaluate the effectiveness of the occupancy state recognition.

This paper describes a remote control system with a virtual force feedback in a mobile robot. In the mobile robot, a laser range finder is utilized to recognize environmental information. By using this environmental information and the specification of the mobile robot, the safety velocity area (SVA) is calculated in real-time. On the other hand, an operator generates the velocity commands for the mobile robot in real time. By using the relation between SVA and the velocity commands, the virtual force is generated. If the velocity commands are out of SVA, the operator can feel the virtual force. In the previous research, the virtual force is generated based on the only velocity differences. Therefore, the chattering of virtual force commands may occur. In order to solve this problem, this paper proposes the virtual force generation method with hysteresis. Therefore, the operability of the remote control system is increased. From experimental results, the validity of the proposed method was confirmed.

SUMMARYA new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

This paper proposes a robust 3D map-building system for a mobile robot using a LRF (Laser Range Finder). The LRF can be utilized and installed on the mobile robot for the map building and the autonomous navigation. The scanning operations are performed while the mobile robot is moving on the ground without stops. The effectiveness of the proposed map building scheme has been demonstrated through the real navigation experiments in the outdoor environment.