A model predictive controller for robots to follow a virtual leader

SUMMARYIn this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.</jats:p

Multisensor-based human detection and tracking for mobile service robots

The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments

Multisensor data fusion for joint people tracking and identification with a service robot

Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution

IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT

This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p

Visual Localisation of Mobile Devices in an Indoor Environment under Network Delay Conditions

Current progresses in home automation and service robotic environment have highlighted the need to develop interoperability mechanisms that allow a standard communication between the two systems. During the development of the DHCompliant protocol, the problem of locating mobile devices in an indoor environment has been investigated. The communication of the device with the location service has been carried out to study the time delay that web services offer in front of the sockets. The importance of obtaining data from real-time location systems portends that a basic tool for interoperability, such as web services, can be ineffective in this scenario because of the delays added in the invocation of services. This paper is focused on introducing a web service to resolve a coordinates request without any significant delay in comparison with the sockets

Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

The complex detection background and lesion features make the automatic detection of dermoscopy image lesions face many challenges. The previous solutions mainly focus on using larger and more complex models to improve the accuracy of detection, there is a lack of research on significant intra-class differences and inter-class similarity of lesion features. At the same time, the larger model size also brings challenges to further algorithm application; In this paper, we proposed a lightweight skin cancer recognition model with feature discrimination based on fine-grained classification principle. The propose model includes two common feature extraction modules of lesion classification network and a feature discrimination network. Firstly, two sets of training samples (positive and negative sample pairs) are input into the feature extraction module (Lightweight CNN) of the recognition model. Then, two sets of feature vectors output from the feature extraction module are used to train the two classification networks and feature discrimination networks of the recognition model at the same time, and the model fusion strategy is applied to further improve the performance of the model, the proposed recognition method can extract more discriminative lesion features and improve the recognition performance of the model in a small amount of model parameters; In addition, based on the feature extraction module of the proposed recognition model, U-Net architecture, and migration training strategy, we build a lightweight semantic segmentation model of lesion area of dermoscopy image, which can achieve high precision lesion area segmentation end-to-end without complicated image preprocessing operation; The performance of our approach was appraised through widespread experiments comparative and feature visualization analysis, the outcome indicates that the proposed method has better performance than the start-of-the-art deep learning-based approach on the ISBI 2016 skin lesion analysis towards melanoma detection challenge dataset

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Optimization and Sequence Search Based Localization in Wireless Sensor Networks

Localization is critical for various applications of Wireless Sensor Networks. This paper presents a 3D localization algorithm for high accuracy localization of a wireless sensor network, which consists of three parts: pre-localization, refinement and sequence search. It is clear that the localization accuracy partly depends on the localization sequence of the unknown nodes, which has not been investigated so far. The proposed novel algorithm aims to address this problem by searching a localization sequence corresponding to a high localization accuracy and a robust algorithm. The simulation results show that the proposed algorithm can get rid of the flip ambiguity and is more robust than several existing algorithms in terms of the localization accuracy

EKF Based Mobile Robot Localization

Localization plays a significant role in the autonomous navigation of a mobile robot. This paper investigates mobile robot localization based on Extended Kalman Filter(EKF) algorithm and a feature based map. Corner angles in the environment are detected as the features, and the detailed processes of feature extraction are described. Then the motion model and odometry information are elaborated, and the EKF localization algorithm is presented. Finally, the experimental result is given to verify the feasibility and performance of the proposed localization algorithm