Implementing distributed control system for intelligent mobile robot

The control system of a mobile robot has a number of issues to deal with in real time, including motion control, mapping, localization, path planning, and sensor processing. Intelligent reasoning, task-oriented behaviors, human–robot interfaces, and communications add more tasks to be solved. This naturally leads to a complex hierarchical control system where various tasks have to be processed concurrently. Many low-level tasks can be handled by a robots onboard (host) computer. However, other tasks, such as speech recognition or vision processing, are too computationally intensive for one computer to process. In this case, it is better to consider a distributed design for the control system in networked environments. In order to achieve maximum use of the distributed environment, it is important to design and implement the distributed system and its communication mechanisms in an effective and flexible way. This article describes our approach to designing and implementing a distributed control system for an intelligent mobile robot. We present our implementation of such a distributed control system for a prototype mobile robot. We focus our discussion on the system architecture, distributed communication mechanisms, and distributed robot control.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Control of a nonholonomic mobile robot using an RBF network

This article deals with handling unknown factors, such as external disturbance and unknown dynamics, for mobile robot control. We propose a radial-basis function (RBF) network-based controller to compensate for these. The stability of the proposed controller is proven using the Lyapunov function. To show the effectiveness of the proposed controller, several simulation results are presented. Through the simulations, we show that the proposed controller can overcome the modelling uncertainty and the disturbances. The proposed RBF controller also outperforms previous work from the viewpoint of computation time, which is a crucial fact for real-time applications.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

A spoken language interface with a mobile robot

We describe a spoken dialogue interface with a mobile robot, which a human can direct to specific locations, ask for information about its status, and supply information about its environment. The robot uses an internal map for navigation, and communicates its current orientation and accessible locations to the dialogue system. In this article, we focus on linguistic and inferential aspects of the human–robot communication process. This work was conducted at ICCS, School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

A bacterial colony growth algorithm for mobile robot localization

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons. The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

Development of a stair-climbing mobile robot with legs and wheels

This paper deals with the development of a stair-climbing mobile robot with legs and wheels. The main technical issues in developing this type of robot are the stability and speed of the robot while climbing stairs. The robot has two wheels in the front of the body to support its weight when it moves on flat terrain, and it also has arms between the wheels to hook onto the tread of stairs. There are two pairs of legs in the rear of the body. Using not only the rorational torque of the arms and the wheels, but also the force of the legs, the robot goes up and down stairs. It measures the size of stairs when going up and down the first step, and therefore the measurement process does not cause this robot to lose any time. The computer which controls the motion of the robot needs no complicated calculations as other legged robots do. The mechanism of this robot and the control algorithm are described in this paper. This robot will be developed as a wheelchair with a stair climbing mechanism for disabled and elderly people in the near future. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Evolutionary computing based mobile robot localization

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated.     

A user study of command strategies for mobile robot teleoperation

This article presents a user study of mobile robot teleoperation. Performance of speed, position and combined command strategies in combination with text, visual and haptic feedback information were evaluated by experiments. Two experimental tasks were designed as follows: positioning of mobile robot and navigation in complex environment. Time for task completion and motion accuracy were measured and compared for different command strategies and types of feedback. Role of haptic, text and visual feedback information in combination with described command strategies is outlined.     

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.     

Detection of doors using a genetic visual fuzzy system for mobile robots

Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.     

Indoor navigation of a non-holonomic mobile robot using a visual memory

When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.

Robust omnidirectional mobile robot topological navigation system using omnidirectional vision

Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method.     

Fuzzy logic techniques for mobile robot obstacle avoidance

This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment. We will define reactive navigation as a mapping between sensory data and commands. Building a reactive navigation system means providing such a mapping. It can come from a family of predefined functions (like potential fields methods) or it can be built using ‘universal’ approximators (like neural networks). In this paper, we will consider another ‘universal’ approximator: fuzzy logic. We will explain how to choose the rules using a behaviour decomposition approach. It is possible to build a controller working quite well but the classical problems are still there: oscillations and local minima. Finally, we will conclude that learning is necessary for a robust navigation system and fuzzy logic is an easy way to put some initial knowledge in the system to avoid learning from zero.     

Real-time panospheric image dewarping and presentation for remote mobile robot control

The panospheric camera used by novice drivers in the United States to navigate the Nomad robot across a desert in Chile provides an omnidirectional, ground-to-sky view of the remote robot's environment. This paper describes the texture mapping techniques used to continuously dewarp and display the image for tele-explorers in Pittsburgh, PA and Mountain View, CA.     

Modeling unknown environments with a mobile robot

The exploration of an unknown environment is an important task for the new generation of mobile service robots. These robots are supposed to operate in dynamic and changing environments together with human beings and other static or moving objects. Sensors that are capable of providing the quality of information that is required for the described scenario are optical sensors like digital cameras and laserscanners. In this paper sensor integration and fusion for such sensors is described. Complementary sensor information is transformed into a common representation in order to achieve a cooperating sensor system. Sensor fusion is performed by matching the local perception of a laserscanner and a camera system with a global model that is being built up incrementally. The Mahalanobis-distance is used as matching criterion and a Kalman-filter is used to fuse matching features. A common representation including the uncertainty and the confidence is used for all scene features. The system's performance is demonstrated for the task of exploring an unknown environment and incrementally building up a geometrical model of it.     

模糊控制在移动机器人路径规划中的应用

针对移动机器人最优路径规划问题,设计了一种模糊智能控制方法。利用超声波传感器对机器人周围环境进行探测,得到关于障碍物和目标的信息。通过设计模糊控制器,把得到的障碍与目标位置信息模糊化,建立模糊规则并解模糊最终使机器人可以很好地避障,并且解决了模糊算法存在的死锁问题,从而实现了移动机器人的路径规划。仿真实验结果表明了模糊算法优于人工势场法,具有有效性和可行性。     

Lyapunov function-based obstacle avoidance scheme for a two-wheeled mobile robot

An obstacle avoidance scheme of a two-wheeled mobile robot is shown by selecting an appropriate Lya- punov function. When considering the obstacle, the Lyapunov function may have some local minima. A method which erases the local minima is proposed by using a function which covers the minima with a plane surface. The effectiveness of the proposed method is verified by numerical simulations.     

Path planning of a mobile robot by optimization and reinforcement learning

At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach, the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

Timing control of the mobile robot using tau-margin

Recently, autonomous robots which are designed on the basis of biological mechanism have attracted much attention. In this paper, we focus on the mechanism of timing control studied by ecological psychology, and apply the framework to timing control of a mobile robot. Experiments using real robots have been conducted and effective behaviors have been realized. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

An autonomous educational mobile robot mediator

So far, most of the applications of robotic technology to education have mainly focused on supporting the teaching of subjects that are closely related to the Robotics field, such as robot programming, robot construction, or mechatronics. Moreover, most of the applications have used the robot as an end or a passive tool of the learning activity, where the robot has been constructed or programmed. In this paper, we present a novel application of robotic technologies to education, where we use the real world situatedness of a robot to teach non-robotic related subjects, such as math and physics. Furthermore, we also provide the robot with a suitable degree of autonomy to actively guide and mediate in the development of the educational activity. We present our approach as an educational framework based on a collaborative and constructivist learning environment, where the robot is able to act as an interaction mediator capable of managing the interactions occurring among the working students. We illustrate the use of this framework by a 4-step methodology that is used to implement two educational activities. These activities were tested at local schools with encouraging results. Accordingly, the main contributions of this work are: i) A novel use of a mobile robot to illustrate and teach relevant concepts and properties of the real world; ii) A novel use of robots as mediators that autonomously guide an educational activity using a collaborative and constructivist learning approach; iii) The implementation and testing of these ideas in a real scenario, working with students at local schools.

Wheeled inverted pendulum type assistant robot: design concept and mobile control

This paper describes the design concept of the human assistant robot I-PENTAR (Inverted PENdulum Type Assistant Robot) aiming at the coexistence of safety and work capability and its mobile control strategy. I-PENTAR is a humanoid type robot which consists of a body with a waist joint, arms designed for safety, and a wheeled inverted pendulum mobile platform. Although the arms are designed low-power and lightweight for safety, it is able to perform tasks that require high power by utilizing its self-weight, which is the feature of a wheeled inverted pendulum mobile platform. I-PENTAR is modeled as a three dimensional robot; with controls of inclination angle, horizontal position, and steering angle to achieve high mobile capability. The motion equation is derived considering the non-holonomic constraint of the two-wheeled mobile robot, and a state feedback control method is applied for basic mobile controls wherein the control gain is calculated by the LQR method. Through several experiments of balancing, linear running, and steering, it was confirmed that the robot could realize stable mobile motion in a real environment by the proposed controller.