Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.

In this paper, we propose a new asymptotically stabilizing control law for a four-wheeled vehicle with a steering limitation. We adopt a locally semiconcave control Lyapunov function (LS-CLF) for the system. To overcome the nonconvexity of the input-constraint set, we utilize a saturation function and a signum function in the control law. The signum function makes the vehicle velocity nonzero except at the origin so that the angular velocity can be manipulated within the input constraint. However, the signum function may cause a chattering phenomenon at certain points of the state far from the origin. Thus, we integrate a lazy-switching mechanism for the vehicle velocity into the control law. The mechanism makes a sign of the vehicle velocity maintain, and the new control input also decreases the value of the LS-CLF. We confirm the effectiveness of our method by a computer simulation and experiments.

The gathering problem of anonymous and oblivious mobile robots is one of the fundamental problems in the theoretical mobile robotics. We consider the gathering problem in unoriented and anonymous rings, which requires that all robots eventually keep their positions at a common non-predefined node. Since the gathering problem cannot be solved without any additional capability to robots, all the previous results assume some capability of robots, such as the agreement of local view. In this paper, we focus on the multiplicity detection capability. This paper presents a deterministic gathering algorithm with local-weak multiplicity detection, which provides a robot with information about whether its current node has more than one robot or not. This assumption is strictly weaker than that in previous works. Our algorithm achieves the gathering from an aperiodic and asymmetric configuration with 2 < k < n/2 robots, where n is the number of nodes. We also show that our algorithm is asymptotically time-optimal one, i.e., the time complexity of our algorithm is O(n). Interestingly, despite the weaker assumption, it achieves significant improvement compared to the previous algorithm, which takes O(kn) time for k robots.

Obtaining a compact representation of a large-size feature map built by mapper robots is a critical issue in recent mobile robotics. This “map compression” problem is explored from a novel perspective of dictionary-based data compression techniques in the paper. The primary contribution of the paper is the proposal of the dictionary-based map compression approach. A map compression system is presented by employing RANSAC map matching and sparse coding as building blocks. The effectiveness levels of the proposed techniques is investigated in terms of map compression ratio, compression speed, the retrieval performance of compressed/decompressed maps, as well as applications to the Kolmogorov complexity.

A min-max model predictive controller is developed in this paper for tracking control of wheeled mobile robots (WMRs) subject to the violation of nonholonomic constraints in an environment without obstacles. The problem is simplified by neglecting the vehicle dynamics and considering only the steering system. The linearized tracking-error kinematic model with the presence of uncertain disturbances is formed in the frame of the robot. And then, the control policy is derived from the worst-case optimization of a quadratic cost function, which penalizes the tracking error and control variables in each sampling time over a finite horizon. As a result, the input sequence must be feasible for all possible disturbance realizations. The performance of the control algorithm is verified via the computer simulations with a predefined trajectory and is compared to a common discrete-time sliding mode control law. The result shows that the proposed method has a better tracking performance and convergence.

In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios.

Detecting emergency situation is very important to a surveillance system for people like elderly live alone. A vision-based emergency response system with a paramedic mobile robot is presented in this paper. The proposed system is consisted of a vision-based emergency detection system and a mobile robot as a paramedic. A vision-based emergency detection system detects emergency by tracking people and detecting their actions from image sequences acquired by single surveillance camera. In order to recognize human actions, interest regions are segmented from the background using blob extraction method and tracked continuously using generic model. Then a MHI (Motion History Image) for a tracked person is constructed by silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. When an emergency is detected, a mobile robot can help to diagnose the status of the person in the situation. To send the mobile robot to the proper position, we implement mobile robot navigation algorithm based on the distance between the person and a mobile robot. We validate our system by showing emergency detection rate and emergency response demonstration using the mobile robot.

This paper presents an autonomous navigation system for a mobile robot using randomly distributed passive RFID tags. In the case of randomly distributed RFID tags, it is difficult to provide the precise location of the robot especially in the area of sparse RFID tag distribution. This, combined with the wide turning radius of the robot, can cause the robot to enter a zigzag exploration path and miss the goal. In RFID-based navigation, the key is to reduce both the number of RFID tags and the localization error for practical use in a large space. To cope with these, we utilized the Read time, which measures the reading time of each RFID tag. With this, we could estimate accurately the localization and orientation without using any external sensors or increasing the RFID tags. The average estimation errors of 7.8 cm in localization and 11 degrees in orientation were achieved with 102 RFID tags in the area of 4.2 m by 6.2 m. Our proposed method is verified with the path trajectories produced during navigation compared with conventional approaches.

This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.

We propose a modified Hidden Markov Model (HMM) with a view to improve gesture recognition using a moving camera. The conventional HMM is formulated so as to deal with only one feature candidate per frame. However, for a mobile robot, the background and the lighting conditions are always changing, and the feature extraction problem becomes difficult. It is almost impossible to extract a reliable feature vector under such conditions. In this paper, we define a new gesture recognition framework in which multiple candidates of feature vectors are generated with confidence measures and the HMM is extended to deal with these multiple feature vectors. Experimental results comparing the proposed system with feature vectors based on DCT and the method of selecting only one candidate feature point verifies the effectiveness of the proposed technique.

An adaptive backstepping and high order sliding modes control algorithm is proposed for output tracking of mobile robots. The controller can greatly reduce the chattering due to conventional sliding modes technique. The proposed algorithm has certain robustness with respect to the external random disturbances and good adaptability with respect to the parametric uncertainty. The effectiveness of the proposed control strategy is demonstrated by simulations studies.

A computed torque controller for a dynamic model of nonholonomic mobile robots with bounded external disturbance is proposed to treat the adaptive tracking control problem using the separated design method. A velocity controller is first designed for the kinematic steering system to make the tracking error approaching to zero asympotically. Then, a computed torque controller is designed such that the true mobile robot velocity converges to the desired velocity controller. In each step, the controllers are designed independently, and this will simplify the design of controllers. A novel stability analysis involving the estimation of some differential inequalities is also given to guarantee the stability of the closed-loop system. Moreover, the regulation problem and the tracking problem will be treated using the proposed controller. In particular, the mobile robots can globally follow any path such as a straight-line, a circle and the path approaching to the origin. Furthermore, the problems of back-into-garage parking and the parallel parking problem can also be solved using the proposed controller. Some interesting simulation results are given to illustrate the effectiveness of the proposed tracking control law.

In our previous research, we proposed a nonlinear digital filter to Estimate the Smoothed and Differential values of the sensor inputs by using Sliding mode system (ESDS). This estimator is able to eliminate impulsive noise efficiently from time series data. We applied this filter to processing outputs of robot sensors, and it became possible to perform robust environment recognition. ESDS is designed using a theory of variable structure system (VSS) with sliding mode. In short, ESDS is a nonlinear filter. Therefore, it is very difficult to clarify the behavior of the system analytically. Consequentially, we deal with the step function with impulsive noise as an example, and we attempt to eliminate this impulsive noise by keeping the sudden shift of signals. In this case, there is a trade-off between the noise elimination ability and the tracking performance for an input signal. Although ESDS is a nonlinear filter, it has the same trade-off as linear filters such as a low-pass filter. In order to satisfy these two conditions simultaneously, we use two filters whose parameters are independent of each other. Furthermore, in order to repress the adverse affect of impulsive noise in the steady-state, we introduced the boundary layer. Generally, a boundary layer is used so as to inhibit the harmful effect of chattering. Chattering is caused in the sliding mode system when the state of the system vibrates on the switching line of a sliding mode system. By introducing the boundary layer to ESDS, we can repress the adverse effect of impulsive noise in the steady-state. According to these considerations, we clarify the relationship between these characteristics of ESDS and the arbitrary parameters.

This paper focuses on a global ultrasonic system for self-localization of a mobile robot. The global ultrasonic system consists of some ultrasonic generators fixed at some arbitrary position in the global coordinates and two receivers in the moving coordinates of the mobile robot. This system is used to obtain the state vector of the mobile robot in the global coordinates from the distance measurement between the ultrasonic generator and the receiver. In order to avoid the cross-talk and to synchronize the ultrasonic sensors, the sequential cuing technique using small-sized radio frequency module is adopted. An extended Kalman filter algorithm is used to process the noisy ultrasonic signal and to estimate the state vector. Computer simulations and experiments are conducted to verify the effectiveness of the proposed global ultrasonic system.

This paper addresses a high-order sliding mode control strategy for output tracking of nonholonomic mobile robots. First, we introduce the dynamic model of robots, driving motors and nonslipping kinematics constraint conditions. Second, we decompose the system into linear and nonlinear components via diffeomorphism and nonlinear input transformation. Also we consider parameter variations of robots and deduce the uncertain model of robots. Third, we design a high order sliding mode controller for output tracking of known and uncertain systems, respectively. Finally, we perform numerical simulations, demonstrating that the proposed high-order sliding mode control not only reduces the chattering problem of sliding mode systems, but also has certain robustness properties with respect to uncertainties of robots.

In order to navigate a mobile robot or an autonomous vehicle in indoor environment, which includes several kinds of obstacles such as walls, furniture, and humans, the distance between the mobile robot and the obstacles have to be determined. These obstacles can be considered as walls with complicated edges. This paper proposes a mobile-robot-navigation method by using the polar coordinate transformation from an omnidirectional image. The omnidirectional image is obtained from a hyperboloidal mirror, which has the prominent feature in sensing the surrounding image at the same time. When the wall image from the camera is transformed by the transformation, the straight lines between the wall and the floor appear in the curve line after transformation. The peak point represents the distance and the direction between the robot and the wall. In addition, the wall types can be classified by the pattern and number of peak points. They are one side wall, corridor and corner. To navigate the mobile robot, in this paper, it starts with comparing a peak point obtained from the real image with the reference point determined by designed distance and direction. If there is a difference between the two points, the system will compute appropriate wheel angle to adjust the distance and direction against the wall by keeping the peak point in the same position as the reference point. The experiments are performed on the prototype mobile robot. The results show that for the determining distance from the robot to the wall between 70-290 cm, the average error is 6.23 percent. For three types of the wall classification, this method can correctly classify 86.67 percent of 15 image samples. In the robot movement alongside the wall, the system approximately consumes the 3 frame/s processing time at 10 cm/s motion speed. The mobile robot can maintain its motion alongside the wall with the average error 12 cm from reference distance.

A general tracking control problem for mobile robots is proposed and solved using the backstepping technique. A global result is given for the kinematic steering system to make the tracking error approaching to zero asymptotically. Based on our efforts, the proposed controller can solve both the tracking problem and the regulation problem of mobile robots. In particular, mobile robots can now globally follow any differentiable with bounded velocities path such as a straight line, a circle and the path approaching to the origin using the proposed controller. Moreover, the problem of back-into-garage parking is also solved by our approach. Some interesting simulation results are given to illustrate the effectiveness of the proposed tracking control laws.

A hybrid system in which mobile terminals (MTs) themselves select whether to communicate directly or via base stations (BSs) from communication conditions, "cellularad-hoc united communication system," has been proposed by the authors. It has been confirmed to have effective channel usage and battery consumption, especially for MTs which tend to communicate with partners which are close together, such as operational mobile robots. In this paper, the hybrid system is examined with respect to handoff rate. This hybrid system uses two modes, a cellular mode and an ad-hoc mode. The former mode is for communication using BSs and the latter is for direct communication. Thus, there will be two kinds of handoffs. One is the ordinary one between BSs and the other is between the two modes. Such a system may suffer from frequent handoffs. Thus the handoff rate of the system was examined. However, by the addition of a simple hysteresis characteristic, the handoff rate was held down closer to that of a pure cellular system in a multipath environment.

This paper presents a novel technique for analyzing and designing local communication systems for distributed mobile robotic systems (DMRS). Our goal is to provide an analysis-base guideline for designing local communication systems to efficiently transmit task information to the appropriate robots. In this paper, we propose a layered methodology, i. e. , design from spatial and temporal aspects based on analysis of information diffusion by local communication between robots. The task environment is classified so that each analysis and design is applied in a systematic way. The spatial design gives the optimal communication area for minimizing transmission time for various cooperative tasks. In the temporal design, we derive the information announcing time to avoid excessive information diffusion. The designed local communication is evaluated in comparison with global communication. Finally, we performed simulations and experiments to demonstrate that the analysis and design technique is effective for constructing an efficient local communication system.

We discuss optimal estimation of the current location of a mobile robot by matching an image of the scene taken by the robot with the model of the environment. We first present a theoretical accuracy bound and then give a method that attains that bound, which can be viewed as describing the probability distribution of the current location. Using real images, we demonstrate that our method is superior to the naive least-squares method. We also confirm the theoretical predictions of our theory by applying the bootstrap procedure.