Remote navigation of a mobile robot in an RFID-augmented environment

In the current article, we address the problem of constructing radiofrequency identification (RFID)-augmented environments for mobile robots and the issues related to creating user interfaces for efficient remote navigation with a mobile robot in such environments. First, we describe an RFID-based positioning and obstacle identification solution for remotely controlled mobile robots in indoor environments. In the robot system, an architecture specifically developed by the authors for remotely controlled robotic systems was tested in practice. Second, using the developed system, three techniques for displaying information about the position and movements of a remote robot to the user were compared. The experimental visualization techniques displayed the position of the robot on an indoor floor plan augmented with (1) a video view from a camera attached to the robot, (2) display of nearby obstacles (identified using RFID technology) on the floor plan, and (3) both features. In the experiment, test subjects controlled the mobile robot through predetermined routes as quickly as possible avoiding collisions. The results suggest that the developed RFID-based environment and the remote control system can be used for efficient control of mobile robots. The results from the comparison of the visualization techniques showed that the technique without a camera view (2) was the fastest, and the number of steering motions made was smallest using this technique, but it also had the highest need for physical human interventions. The technique with both additional features (3) was subjectively preferred by the users. The similarities and differences between the current results and those found in the literature are discussed.     

Fuzzy logic techniques for navigation of several mobile robots

In this paper, navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic are investigated in a totally unknown environment. Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of ultrasonic sensors for measuring the distances of obstacles around it and an infrared sensor for detecting the bearing of the target. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently. Amongst the techniques developed, FLC having Gaussian membership function is found to be most efficient for mobile robots navigation.     

Navigation system for an autonomous robot using an ocellus camera in an indoor environment

Autonomous and mobile robots are being expected to provide various services in human living environments. However, many problems remain to be solved in the development of autonomous robots that can work like humans. When a robot moves, it is important that it be able to have self-localization abilities and recognize obstacles. For a human, the present location can be correctly checked through a comparison between memorized information assuming, it is correct, and the present situation. In addition, the distance to an object and the perception of its size can be estimated by a sense of distance based on memory or experience. Therefore, the environment for robotic activity assumed in this study was a finite-space such as a family room, an office, or a hospital room. Because an accurate estimation of position is important to the success of a robot, we have developed a navigation system with self-localization ability which uses only a CCD camera that can detect whether the robot is moving accurately in a room or corridor. This article describes how this system has been implemented and tested with our developed robot.     

Navigation strategy of multiple mobile robot systems based on the null-space projection method

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.     

Image-based homing navigation with landmark arrangement matching

Many insects and animals exploit their own navigation systems to navigate in space. Biologically-inspired methods have been introduced for landmark-based navigation algorithms of a mobile robot. The methods determine the movement direction based on a home snapshot image and another snapshot from the current position. In this paper, we suggest a new landmark-based matching method for robotic homing navigation that first computes the distance to each landmark based on ego-motion and estimates the landmark arrangement in the snapshot image. Then, landmark vectors are used to localize the robotic agent in the environment and to choose the appropriate direction to return home. As a result, this method has a higher success rate for returning home from an arbitrary position than do the conventional image-matching algorithms.     

Using roadmaps to classify regions of space for autonomous robot navigation

The roadmap approach to robot path planning is one of the earliest methods. Since then, many different algorithms for building roadmaps have been proposed and widely implemented in mobile robots but their use has always been limited to planning in static, totally known environments. In this paper we combine the use of dynamic analogical representations of the environment with an efficient roadmap extraction method, to guide the robot navigation and to classify the different regions of space in which the robot moves. The paper presents the general reference architecture for the robotic system and then focuses on the algorithms for the construction of the roadmap, the classification of the regions of space and their use in robot navigation. Experimental results indicate the applicability and robustness of this approach in real situations.     

Dynamic omnidirectional vision for mobile robots

Mobile robotic devices hold great promise for a variety of applications in industry. A key step in the design of a mobile robot is to determine the navigation method for mobility control. The purpose of this paper is to describe a new algorithm for omnidirectional vision navigation. A prototype omnidirectional vision system and the implementation of the navigation techniques using this modern sensor and an advanced automatic image processor is described. The significance of this work is in the development of a new and novel approach—dynamic omnidirectional vision for mobile robots and autonomous guided vehicles.     

A mobile robot employing insect strategies for navigation

The ability to navigate in a complex environment is crucial for both animals and robots. Many animals use a combination of different strategies to return to significant locations in their environment. For example, the desert ant Cataglyphis is able to explore its desert habitat for hundreds of meters while foraging and return back to its nest precisely and on a straight line. The three main strategies that Cataglyphis is using to accomplish this task are path integration, visual piloting and systematic search. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of Cataglyphis. Inspired by the insect’s navigation system we have developed mechanisms for path integration and visual piloting that were successfully employed on the mobile robot Sahabot 2. On the one hand, the results obtained from these experiments provide support for the underlying biological models. On the other hand, by taking the parsimonious navigation strategies of insects as a guideline, computationally cheap navigation methods for mobile robots are derived from the insights gained in the experiments.     

Hazardous area navigation with temporary beacons

A new area expansion algorithm for the localization scheme, using temporary beacons, is proposed in this paper. The effective area of the active beacons is limited by the strength of the ultrasonic signals in a noisy environment. When a mobile robot needs to move into a hazardous area or into an unstructured environment where the beacons with pre-specified position information are not available, the localization may solely rely on dead reckoning sensors such as encoders. To overcome the error accumulation by using dead-reckoning, a new scheme is developed, in this paper, in which the mobile robot carries a few temporary beacons which do not have any pre-stored position information. When the mobile robot encounters a dangerous or unstructured environment, it utilizes the temporary beacons to localize itself. An auto-calibration algorithm has been developed to provide the position information to the temporary beacons before they are used for the localization. With these temporary beacons and the auto-calibration algorithm, mobile robots can safely pass unstructured areas. The effectiveness of the temporary beacons and auto-calibration algorithm is verified through real experiments of mobile robot navigation.     

A virtual centrifugal force based navigation algorithm for explorative robotic tasks in unknown environments

In many robotic tasks, there is no a priori knowledge of the environment. This makes it necessary for robots to explore the environment. Navigation algorithms for robots to map the environment completely in a short time play a very important role in the robotic task completion. A navigation algorithm based on virtual centrifugal force is proposed to complete the robotic exploration of the unknown environment using rang sensors in this paper. Collisions between a robot and an obstacle or between robots can be avoided with the application of the proposed navigation rules. The kinematics and dynamics equations of robots adopting the algorithm are also given. The simulation experiments demonstrate the operation of the algorithm. Several simulation experiments of various representative robotic tasks are carried out, based on the explorative navigation algorithm, which successfully validate the virtual centrifugal force based navigation algorithm.     

Nonlinear trilateral teleoperation stability analysis subjected to time-varying delays

A trilateral teleoperation system facilitates the collaboration of two users to share control of a single robot in a remote environment. While various applications of shared-control trilateral haptic teleoperation systems have recently emerged, they have mostly been studied in the context of single-DOF, LTI robotic systems. On the other hand, robotic manipulators with multiple degrees of freedom (DOF) and therefore nonlinear dynamics have recently found many applications such as in robotic-assisted surgery and therapy, space exploration and navigation systems. In this paper, considering the full nonlinear dynamical models of multi-DOF robots, stability analysis of a dual-user haptic teleoperation system is considered over a communication network subjected to asymmetrical time varying delays and through a dominance factor suitable for trainer–trainee applications. Stability in free motion and contact motion and asymptotic position tracking of the trilateral haptic teleoperation system in free motion are proven via Lyapunov stability analysis and Barbalat's lemma where operators and the environment are assumed to be passive. Simulation and experimental results concerning robot position tracking and user-perceived forces for three 2-DOF robots and experimental analysis of user-perceived stiffnesses for three 3-DOF robots validate the theoretical findings pertaining to the system stability and demonstrate the efficiency of the proposed controller.     

Navigation of Mobile Robots Using a Fuzzy Logic Controller

This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.     

Laying and sensing odor markings as a strategy for assisting mobilerobot navigation tasks

After observing the use of odor by insects for navigation and location, the authors have developed an odor marking and detection system for mobile robots. Such a system will provide the basis for investigating the use of odor markings as an aid to mobile robot navigation     

Swarm robotic network using Lévy flight in target detection problem

One approach in swarm robotics is homogeneous system which is embedded with sensing, computing, mobile and communication components. In this study, a target detection problem, which is one of navigation problems, was employed. Once a robot detects a target, robots immediately communicate with a base station via intermediate relay robots due to the multi-hop transmission of wireless communication. Therefore, this control task is completed with connectivity of the network. In a target detection problem, we must improve the performance of exploration as well as connectivity of the network. This study investigates the performances of the two types of random walk algorithm in navigation while loosely ensuring connectivity of the robotic network based on our previous study.     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.     

一个基于全景视觉的移动机器人导航系统的设计与实现

针对移动机器人路径规划与导航的实际应用,设计了一个基于全景视觉的移动机器人路径规划导航系统．首先,对导航系统的体系结构和功能进行描述．然后,分别就如何采用全景视觉传感器进行环境探索与地图创建,基于回归神经网络的广度优先搜索法和Voronoi骨架图法两种路径规划算法原理,以及如何实现按规划路径实施导航这三个方面进行了详细阐述．最后,结合实际机器人进行导航实验,评估导航系统的性能和路径规划算法的有效性．     

Efficient vision-based navigation

In this article, we present a novel approach to learning efficient navigation policies for mobile robots that use visual features for localization. As fast movements of a mobile robot typically introduce inherent motion blur in the acquired images, the uncertainty of the robot about its pose increases in such situations. As a result, it cannot be ensured anymore that a navigation task can be executed efficiently since the robot’s pose estimate might not correspond to its true location. We present a reinforcement learning approach to determine a navigation policy to reach the destination reliably and, at the same time, as fast as possible. Using our technique, the robot learns to trade off velocity against localization accuracy and implicitly takes the impact of motion blur on observations into account. We furthermore developed a method to compress the learned policy via a clustering approach. In this way, the size of the policy representation is significantly reduced, which is especially desirable in the context of memory-constrained systems. Extensive simulated and real-world experiments carried out with two different robots demonstrate that our learned policy significantly outperforms policies using a constant velocity and more advanced heuristics. We furthermore show that the policy is generally applicable to different indoor and outdoor scenarios with varying landmark densities as well as to navigation tasks of different complexity.