Virtual force generation method for remote control system in mobile robot

In this paper we present a vision-based approach to detect, track and identify people on a mobile robot in real time. While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and a fast contour model together with a particle filter. With this method a person can be detected independently from current light conditions and in situations where no skin color is visible (the person is not close or does not face the robot). Tracking in thermal images is used as an attention system to get an estimate of the position of a person. Based on this estimate we use a pan-tilt camera to zoom to the expected face region and apply a fast face tracker in combination with face recognition to identify the person.

In this research, a remote control system has been developed and implemented, which combines autonomous obstacle avoidance in real-time with force-reflective tele-operation. A tele-operated mobile robot is controlled by a local two-degrees-of-freedom force-reflective joystick that a human operator holds while he is monitoring the screen. In the system, the force-reflective joystick transforms the relation between a mobile robot and the environment to the operator as a virtual force which is generated in the form of a new collision vector and reflected to the operator. This reflected force makes the tele-operation of a mobile robot safe from collision in an uncertain and obstacle-cluttered remote environment. A mobile robot controlled by a local operator usually takes pictures of remote environments and sends the images back to the operator over the Internet. Because of limitations of communication bandwidth and the narrow view-angles of the camera, the operator cannot observe shadow regions and curved spaces frequently. To overcome this problem, a new form of virtual force is generated along the collision vector according to both distance and approaching velocity between an obstacle and the mobile robot, which is obtained from ultrasonic sensors. This virtual force is transferred back to the two-degrees-of-freedom master joystick over the Internet to enable a human operator to feel the geometrical relation between the mobile robot and the obstacle. It is demonstrated by experiments that this haptic reflection improves the performance of a tele-operated mobile robot significantly.

The purpose of this work is to control a remote mobile robot in real time by detecting and avoiding obstacles in the navigation environment using an RGB-D camera. To this end, an advanced signal and image processing are applied to the acquired environment images in the aim to define the optimal motion path. the experimental results prove that the mobile robot follows the optimal trajectory and avoids all the obstacles in the navigation environment.

Aiming at improving the response speed and robustness of wheeled mobile robots, this paper uses neural networks to identify the dynamic functions of mobile robots, and proposes an improved adaptive super-twisting sliding mode controller. First, this paper improves the sliding mode surface of super-twisting sliding mode control, which effectively speeds up the response speed of the system. Second, the robust adaptive law is utilized to eliminate the influence of uncertain parameters in super-twisting sliding mode control, which improves the robustness of the system and greatness reduces the chattering. In addition, the use of a high-gain observer to estimate the speed information of the mobile robot in real time avoids the shortcomings of direct measurement of speed information and realizes the output feedback control of the system.

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

Innovative and quality education for all is a major precondition for smart nations. Children and youth are fascinated by mechanisms, machines and robots. Therefore, robotics using inexpensive open source hardware and software is increasingly being used in innovative STEM education. But, developing countries in Asia, Africa and Latin America are well behind the curve in the use of robots in schools and colleges. An innovative and extremely affordable robotic system is developed in this paper, whereby school children anywhere can go online and race multiple indoor mobile robots over the Internet. Virtual reality and force feedback are used to make the racing realistic and interactive. Children will be introduced to building their own inexpensive mobile robots using commercial, off-the-shelf components and materials. Advanced learners, e.g., college and university students, can modify and improve the hardware and software for Internet-based teleoperation. The project can be used to introduce or improve robotics education at a low cost among school children, while providing opportunities in service learning for college/university students.

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.

This paper describes a navigation system which unites three important capabilities: it enables a mobile robot to avoid obstacles, map the environment, and plan local paths around more complex obstacles while navigating. This system extends and integrates the histogram grid representation and the modified A* algorithm. Our system uses the histogram grid representation and updating method to represent the robot's environment and update a map quickly and reliably with the data from a laser range scanner. Our system also uses the modified A* algorithm to search this grid to plan and replan local paths. The navigation system that we have developed enables a mobile robot to smoothly avoid obstacles while navigating towards a goal at speeds of 0.15 to 0.35 meters per second, speeds which are faster than those typically obtained in similar systems. >

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.

The behavior of wall-following for mobile robots has extended applications, such as intelligent inspection, security patrol and venue service. In this paper, we propose a novel wall-following algorithm based on dynamic virtual walls for mobile robots. The information of environments is firstly obtained by a laser range finder, which is attached to the mobile robot. Then partial sensor data is selected to construct a virtual environment, which is a dynamic virtual wall in this paper. A PD controller is successively utilized to control mobile robots to move along the virtual wall smoothly at adaptively adjusted speed. Meanwhile, obstacle avoidance is also achieved in this one general framework with no switching scheme introduced. Simulations and experiments in various environments have been carried out to verify the effectiveness and evaluate the performance of the proposed algorithm.

This paper proposes a method to navigate a mobile robot in human environment via an interaction through virtual 3D lines, which are handwritten in the air by a robot user who coexists with the robot. The objective of this navigation is to develop the flexible instruction of the appropriate route for a mobile robot in adaptation of various situations easily by embodied interaction between robots and users. The developed system was also integrated with the aerial 3D drawing interface and control system of robots. Mobile robots and a drawing hand position are measured by 6DOF sensors simultaneously in real time. The 3D drawing interface allows a user not only to draw 3D lines in the air based on the sensed position in the real world, but also to push, grasp and throw a drawn line manually on site. Each mobile robots are distinguished by an association with a color information of each drawn line, and search the own line in real time individually. Therefore, in this method, it was achieved that the user individually sets, and selects a specific route and change the route by drawing or manipulating lines while the robots are moving. We conducted some navigation experiments using two omni-wheel mobile robots and examined the aforementioned navigation function. Additionally, we tried some advanced navigation that can be achieved only by drawing, such as the dog-walking by likening a virtual line to string. As a result this method enables a user to instruct some complex path easily and change the root flexibly with interaction through virtual lines in response to changing environments.

Though the final goal of mobile robot navigation is to be autonomous, operators' intelligent and skillful decisions are necessary when there are many scattered obstacles. There are several limitations even in the camera-based tele-operation of a mobile robot, which is very popular for the mobile robot navigation. For examples, shadowed and curved areas cannot be viewed using a narrow view-angle camera, especially in bad weather such as on snowy or rainy days. Therefore, it is necessary to have other sensory information for reliable tele-operations. In this paper, sixteen ultrasonic sensors are attached around a mobile robot in a ring pattern to measure the distances to obstacles. A collision vector is introduced in this paper as a new tool for obstacle avoidance, which is defined as a normal vector from an obstacle to the mobile robot. Based on this collision vector, a virtual reflection force is generated to avoid the obstacles and then the reflection force is transferred to an operator who is holding a joystick to control the mobile robot. Relying on the reflection force, the operator can control the mobile robot more smoothly and safely. For this bi-directional tele-operation, a master joystick system using a hall sensor was designed to resolve the existence of nonlinear sections, which are usual for a general joystick with two motors and potentiometers. Finally, the efficiency of a force reflection joystick is verified through the comparison of two vision-based tele-operation experiments, with and without force reflection.

The article considers the problem of navigating mobile robots and finding the best way to the goal in real-time in a space surrounded by unknown objects. The motor actions of the robot must be defined and adapted to changes in the environment. When using only laser scanners on mobile work, objects above or below the lasers' level will remain obstacles to the robot. Current algorithms and principles of navigation are considered. Extended the existing real-time interference detection system using lasers by adding a camera that calculates the length of objects. The new system has been successfully implemented and tested in a mobile robot, ensuring the passage of the road providing collision-free paths. The obtained simulation results are presented in the article. The existing problems of navigation of mobile robots, which are moving in the particular area from their position to the specified destination on the map, were investigated. The current problem is the inability to spot objects that are not on the same level as the mobile robot's lasers. Moreover, the task is complicated when you need to recognize such objects while the robot is moving in real time. The current algorithms and principles of navigation given by previous research and publications are analyzed. As a result of the work, the existing system of recognition and avoidance of obstacles was expanded. Prior to that, the system used only odometry and information obtained from laser scanners, without obtaining data from other sources of environmental information. The idea of development was to use a camera, which was already part of the components of the researched mobile robot. It has become possible to generate a pointcloud relative to the environment, using a depth sensing camera to calculate the distance to objects. Because the density of the received data in the form of a pointcloud is too high for further processing, a downsample VoxelGrid filter was used, which reduces the density of the point cloud. VoxelGrid belongs to the PCL library. Another problem was the removal of information about unnecessary objects in the camera's field of view. These include the floor, ceiling, parts of the robot (such as a manipulator). The PassThrough filter from the PCL library was used to solve this problem. The next step is to process the filtered data using OctoMap. As a result, an octree is generated. A top-down projection is created from the octree generated in the previous step. The resulting projection must be processed and converted into polygonal obstacles. Only then they will be marked by teb_local_planner as obstacles. The developed system was successfully implemented and tested both in the Gazebo simulation and in the researche mobile robot. The path with obstacles will be completed without collisions. The paper presents the obtained test results.

Successful operation of a mobile robot in the navigation area highly depends on the environmental information gathered by its exteroceptive sensors such as laser rangefinder, ultrasonic sensors, CCD cameras, etc. Typically, geometric information derives from the surface of objects in the environment, such as planes, edges, vertices, etc. In general, only partial and imprecise information can be obtained from real sensors, thus integration of multiple observations of the same geometric entity is required to compute an estimation of its location with respect to the vehicle. Partiality refers to the degrees of freedom associated to the representation of different geometric entities and how they determine the location of other entities related to them. Imprecision refers to the accuracy in the estimation of the location of geometric entities. Adequately representing such environmental information is a crucial aspect in mobile robotics. Dealing with uncertain geometric information has given rise to a variety of models which can be classified into either set-based models or probabilistic-models. On the one hand, set-based models describe the imprecision in the localization of a geometric entity by a bounded region, which correspond to the set of feasible locations for the geometric entity. Fusion of geometric information is performed using algebraic operations on the regions limited by the error bounds. On the other hand, probabilistic models represent the uncertain location of a geometric element using a probability distribution, usually Gaussian. Here, fusion is carried out using estimation methods.

This paper presents a method that integrates the geometric path tracking and the obstacle avoidance for nonholonomic mobile robot. The mobile robot follows the path by moving through the turning radius given from the pure pursuit method which is the one of the geometric path tracking methods. And the obstacle generates the obstacle potential, from this potential, the virtual force is obtained. Therefore, the turning radius for avoiding the obstacle is calculated by proportional to the virtual force. By integrating the turning radius for avoiding the obstacle and the turning radius for following the path, the mobile robot follows the path and avoids the obstacle simultaneously. The effectiveness of the proposed method is verified through the real experiments for path tracking only, static obstacle avoidance, dynamic obstacle avoidance.