Development of a dynamic intelligent recognition system for a real-time tracking robot

In flotation production, the surface texture of the flotation froth is an important visual information reflecting the ore grade, which is closely related to the flotation conditions and directly reflects the mineralization degree (grade level) of the froth layer. Aiming at the problem that the texture feature extraction of the existing flotation froth image does not consider the froth surface particle, a new local texture feature extraction method based on PCA (Principle Component Analysis) algorithm is proposed and a new texture feature granularity is defined. Firstly, the froth image is segmented, the region of interest is extracted and the particle factor is defined. Then the texture feature is extracted by PCA algorithm and the correlation between particle size and zinc concentrate taste is analyzed. The experimental results verify the algorithm effectiveness and indicating that it can more accurately regulate the position of minerals and guide flotation production.

Abstract. The use of neural networks in automatic control systems makes it possible to increase their compliance with the assigned tasks. Such automatic control systems are able not only to use the general control methods, but also offer their algorithmic approaches to solving the tasks related to the problem of identifying the parameters of the control object. In the article, adaptive control systems and control systems using neural networks are considered. An analysis of recent research in this direction has been conducted. A concept has been proposed for the development of an energy-efficient intelligent automatic control system for a multi-motor electric drive of a mobile industrial robot. Keywords: automatic control system, mobile industrial robot, nonlinearity, energy-efficiency, uncertainty, adaptability, neural network, mechatronic module.

This study was conducted to investigate the effect of light and dark conditions on feeding activity and eye adaptations of post larvae (PL5, PL10, PL20 and PL30) Penaeus vannamei fed with frozen Artemia. Shrimp PL were placed individually in beakers and after acclimatization under the light or dark condition, PL were left to ingest known number of Artemia for 30 minutes. Thereafter, each PL was subsequently anesthetized by putting an ice cube into the beakers followed by adding few drops of paraformaldehyde. The results showed that the PL5 ingested significantly more frozen Artemia under light condition compared to dark condition (P<0.05). The eye structures of PL5 comprises of crystalline cone, rhabdom and fasciculated zone. However, it was incomplete due to the lack of clear zone and no migration of the screening pigment granules was observed under light and dark conditions. On the contrary, the number of frozen Artemia ingested by the latter stages PL10, PL20 and PL30 showed no significant differences (P>0.05) under both light conditions and these PL have complete eye structures with define clear zone. The width of clear zone was found to increase proportionally with the growth of the PL. Besides that, the screening pigment granules were able to migrate under light and dark conditions. The ability of the PL10, PL20 and PL30 to ingest almost similar numbers of Artemia under light and dark conditions suggests that different lighting conditions did not affect the feeding activity of the PL and other sensory organs may play roles in detecting food, while PL5 need light to improve their feeding activity. Based on these results, we suggest that in aquaculture practice, during the rearing of early stage of PL (<PL5), a brighter environment or light should be provided to enhance larval feeding activities. Whereas, later stages of PL (>PL10) can be cultured under any light condition.

To solve the problem of heavy patrol workload and reduce the cost of maintaining public security, it is thought that mobile robots, which substitute for conventional patrolmen, will play an important role in the next decade. This paper describes a mobile patrol robot, including a positioning system using image sensors and a navigation system based on multi-sensor information fusion. The patrol robot can detect some intruders by means of an infrared sensor. If someone intrudes, the robot can shout loudly and light the alarm lamp. Once an emergency appears, the watchman in the safety center can deal with this case according to the environment image obtained from wireless transmission device. The robot, which is provided with an intelligent fire alarm system based on multi-sensor information fusion, can detect the fire precisely and reliably. Finally, assuming the robot will patrol in a flat area, a control algorithm for the patrol robot system is developed, and a field test is conducted around the teaching building to evaluate the constructed robot system. The test results show the robot system can meet the actual requirement for patrol and public security.

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

In this paper we present a vision-based approach to detect, track and identify people on a mobile robot in real time. While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and a fast contour model together with a particle filter. With this method a person can be detected independently from current light conditions and in situations where no skin color is visible (the person is not close or does not face the robot). Tracking in thermal images is used as an attention system to get an estimate of the position of a person. Based on this estimate we use a pan-tilt camera to zoom to the expected face region and apply a fast face tracker in combination with face recognition to identify the person.

Considering the real employment situation of the disabled, it was concluded that there is demand for a robot system that can be used in a real manufacturing environment. To derive our mission statement, we conducted a survey with three criteria: `A. to assist the disabled as much as possible'; `B. to consider the real situation of employment for the disabled'; and `C. to provide assistance for the most necessary tasks for the disabled in the work space'. Based on the survey results, our goal can be stated as follows: To develop work assistive mobile robots to help persons with limb disabilities obtain jobs in manufacturing environments where they can perform simple manual labor. Here, simple labor includes such tasks as circuit inspection and moving boxes. For our goal, we developed two types of work assistive mobile robots, which are small enough to work in the confined environments of real factories. Work assistive mobile robot type I is equipped with a forklift to move boxes and a robotic arm to help with jobs requiring more dexterity. Robot type II is compact in size, adopting an omni-wheel driving mechanism, and is equipped with a lift having a foldable fork. We gathered feedback from empirical work done on the two robots.

The article programs the shortest path searching problems of the mobile robot in the complexity unknown environment, and uses the mobile robot to present the movement scenario from the start point to the target point in a collision-free space. The complexity environment contains variety obstacles, such as road, tree, river, gravel, grass, highway and unknown obstacle. We set the relative dangerous grade for variety obstacles. The mobile robot searches the target point to locate the positions of unknown obstacles, and avoids these obstacles moving in the motion platform. We develop the user interface to help users filling out the positions of the mobile robot and the obstacles on the supervised computer, such the initial point of the mobile robot, the start point and the target point. The supervised computer programs the motion paths of the mobile robot according to A* searching algorithm, flood-fill algorithm and 2-op exchange algorithm The simulation results present the proposed algorithms that program the shortest motion paths from the initial point approach to the target point for the mobile robot. The supervised computer controls the mobile robot that follows the programmed motion path moving to the target point in the complexity environment via wireless radio frequency (RF) interface.

This paper studies several robot navigation algorithms for mobile robots in indoor environment. These algorithms are based on local reactive methodology. By adaptively mixing these algorithms, a mobile robot only equipped with limited sensory devices can be easily driven at high speed and safely in a complex environment. The proposed approach has been extensively tested on a realistic simulation platform for the mobile robot: Pioneer 3, and a complex office environment. The simulation and experimental results show that the mixed-algorithm approach demonstrates better performance than using only one single algorithm.

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.

Research on mobile robots began in the late sixties with the Stanford Research Institute’s pioneering work. Two versions of SHAKEY, an autonomous mobile robot, were built in 1968 and 1971. The main purpose of this project was “to study processes for the realtime control of a robot system that interacts with a complex environment” 〈NIL 69〉. Indeed, mobile robots were and still are a very convenient and powerful support for research on artificial intelligence oriented robotics. They possess the capacity to provide a variety of problems at different levels of generality and difficulty in a large domain including perception, decision making, communication, etc., which all have to be considered within the scope of the specific constraints of robotics: on-line computing, cost considerations, operating ability, and reliability.

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.

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate.

An Electric Differential System for an Autonomous Mobile Robot Based on Neural Network Model

The human face exhibits a high level of complexity when it is regarded as a multidimensional visual model, leading to face recognition systems that require difficult and extensive computations for coding and decoding the face images. A well-established approach in this regard is based on using principle component analysis (PCA) for both feature extraction and face recognition, known as the eigenface approach. This technique, despite a good recognition rate, suffers from the disadvantage of high computation cost due to the complexity of the PCA algorithm. In this paper, we use a geometrical approximated PCA (gaPCA) algorithm for computing the eigenfaces for three different datasets. The face recognition task is performed using a similarity score based on the inverse Euclidean distance for the first two datasets and using a nerual network in the third case. All the results are compared to the case where standard PCA is used. These accuracy results show that gaPCA represents a viable alternative to the classical statistical approach for computing the principal components.