Real Time Mapping and Dynamic Navigation for Mobile Robots

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 obstacle avoidance and navigation are important tasks for a mobile robot in applications such as industry, military, exploration and automated vehicles. This paper presents an implementation of mapping and navigation of autonomous mobile robot using Robot Operating System (ROS) and Orbbec Astra camera. In recent days, Lidar is used for the mapping and navigation problem by sensing the objects which are above floor level by using only a single horizontal scanning line and this may result in inaccurate generation of map leads to collision during autonomous navigation of mobile robots. In order to overcome limitation of Lidar, Orbbec Astra (Kinect) camera is used for mapping of indoor environment which can detect objects which are above and below the floor level and navigation of mobile robot from start to target location without colliding with obstacles. Experimental results show the map of the indoor environment generated using Orbbec Astra camera is matched with real indoor environment, and generated map is tested experimentally to navigate the mobile robot from start to target location without collision with obstacles.KeywordsMobile robotsMappingNavigationOrbbec Astra cameraROS

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

Robot navigation and obstacle avoidance are from the most important problems in mobile robots, especially in unknown environments. The autonomous navigation of mobile robots has attracted a number of researchers over the years. A wide variety of approaches and algorithms have been proposed to tackle this complex problem. There are many successful localization methods that can determine the robot's position relative to a map using sonar, laser and camera data; however, most localization methods fail under common environmental conditions. In this paper we present a novel method for navigation of autonomous mobile robots in several indoor environments that optimize our old navigation approach, using fuzzy Kalman filter based on extracted lines and fusing data from laser scanner sensors. In this approach, the sonar data and laser measurement are combined using the fuzzy Kalman filter. Finally, the simulation results show that the proposed algorithm offers advantages over previous methods and has improved performance.

Aiming at the positioning and navigation system of indoor mobile robot, a fuzzy control method of indoor location navigation based on UWB communication technology is proposed. Firstly, based on the UWB radio signal, the mobile robot localization information is obtained by the twice triangular centroid localization algorithm, and the reactive fuzzy controller is used to realize the local path planning. An improved contour following method is proposed to make the mobile robot get out of the trap area quickly and reach the target. This method can effectively drive the mobile robot to reach the target in a complex and unknown environment with reasonable path. Finally, a fourwheel indoor mobile robot is taken as an example in the indoor environment of 10m × 10m. The experiment proves that the method can effectively drive the mobile robot to reach the target point in the unknown environment with the better path.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

A particle swarm optimization (PSO) algorithm associated with a histogram navigation method is proposed in this paper for real-time map building and path planning for multiple goals applications. In real world applications such as rescue robots, service robots, mining mobile robots, and mine searching robots, etc., an autonomous mobile robot needs to reach multiple goals with a shortest path that, in this paper, is capable of being implemented by a PSO method with minimized overall distance. Once a global trajectory is planned, a foraging-enabled trail is created to guide the robot to the multiple goals. A histogram-based local navigation algorithm is employed to plan a collision-free path along the trail planned by the global path planner. A replanning-based algorithm aims to generate path while a mobile robot explores through a terrain with map building in unknown environments. In this paper, simulation and experimental results demonstrate that the real-time concurrent mapping and multi-goal navigation of an autonomous robot is successfully performed under unknown environments.

Controlling mobile robot navigation system that operates in an unknown and uncertain environment is a difficult operation. Much of this difficulty is due to environmental inconsistencies and sensor inadequacies. We present a new neurofuzzy controller that controls the navigation system of a mobile robot to move safely in an unknown environment in presence of obstacles. Training data is accumulated from robot’s sensors to generate a set of fuzzy rules that govern the robot navigation system on-line.KeywordsArtificial IntelligenceTraining DataMobile RobotNavigation SystemFuzzy RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

Mobile robot has been a major role to the application in military, industrial and agricultural purposes. Mobile robot should navigate through desire route and avoid the obstacle within the path. Many researcher come with the solution by using the various type of control and instrumentation system. The complexity of mobile robot system can make the system cost intensive and high risk. Proposed is a mobile robot equipped with GPS navigation system and obstacle avoidance system with low cost mobile structure, GPS module and sonar sensor. The combination GPS and Sonar will determine the position and obstacle avoidance for the mobile robot. Mobile robot should navigate according to waypoint that preset to the GPS module and sonar sensor detects the obstacle during mobile robot navigation by triggering the sonar sensor in sequence by using commanded loop daisy chaining application method. Mobile Robot can navigate through desired waypoint and at the same time apply the obstacle avoidance rules

The path planning and obstacle avoidance are the most important tasks for an autonomous mobile robot moving in an unknown environment. This paper presents a simple fuzzy logic controller which involves searching target and path planning with obstacle avoidance. In this contest, fuzzy logic controllers are constructed for target searching behavior and obstacle avoidance behavior based on the distance and angle between the robot and the target as inputs for the first behavior and the distance between the robot and the nearest obstacle for the second behavior; then a third fusion behavior is developed to combine the outputs of the two behaviors to compute the speed of the mobile robot in order to fulfill its task properly. Simulation results show that the proposed approach is efficient and can be applied to the mobile robots moving in unknown environments.

The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

Mobile robot, especially wheeled mobile robot, with its simple mechanical structure and inherent agility, has attracted significant attentions for dynamic environment applications in the last two decades (Pin et al. 1994 and Purwin 2006). A general mobile robot Guidance, Navigation and Control (GNC) system is illustrated in the following figure. The main function of guidance system is to generate a feasible trajectory command, usually in multiple dimension of freedom (DOF) to achieve a robot task. The objective of control system is to drive the mobile robot following the commanded trajectory with acceptable tracking errors and stability margin. The function of robot navigation system is to provide accurate position, velocity and/or orientation information for the control system and guidance system. A stable and accurate navigation system is the bridge between the guidance system and control system of mobile robots, which ultimately determines the robot performance.