Performance analysis of real-time and general-purpose operating systems for path planning of the multi-robot systems

In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system istime-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.

Analysis and Research on the Timeliness of Virtual Reality Sports Actions in Football Scenes

In the complex and changing situation on the soccer field, players must always be aware of their teammates, opponents, and the position of the ball during the game, constantly updating and analyzing the strategic information of the opponent in order to make appropriate tactical decisions. This ability to track multiple objects at the same time is also a prerequisite for high-level soccer players to be able to react quickly and appropriately during the game. Therefore, it is essential to examine the attentional ability of soccer players in dynamic scenarios. This study compares soccer players’ performance in 2D planar and 3D virtual reality dynamic tracking tasks in two dimensions. They are correct tracking rate and tracking speed. This paper examines the tracking performance and spatial attention allocation characteristics of soccer players in different dynamic tracking tasks and the differences with the average college students by manipulating different types of 2D dynamic tracking tasks and incorporating a point detection paradigm. It was found that there were no differences in correct tracking and detection stimulus awareness between soccer players and college students in different 2D dynamic tracking tasks, showing consistency across populations. In terms of correct tracking rates, both soccer players and university students showed the highest correct tracking rates in the location MIT task, followed by the MOT task, and the worst in the identity MIT task. This indicates that the good dynamic attention ability of soccer players was not reflected in the above 2D dynamic tracking process. However, soccer players and college students showed consistent characteristics across populations in different dynamic tracking tasks. The results of detection stimulus awareness showed that soccer players and college students had the same trend of attention allocation between dynamic tracking tasks, i.e., more attention to the blank area of the screen and the target object and less attention to the distractor. This suggests that there was a distractor suppression effect between different dynamic tracking tasks.

The Potential of Naturalistic Eye Movement Tasks in the Diagnosis of Alzheimer’s Disease: A Review

Extensive research has demonstrated that eye-tracking tasks can effectively indicate cognitive impairment. For example, lab-based eye-tracking tasks, such as the antisaccade task, have robustly distinguished between people with Alzheimer’s disease (AD) and healthy older adults. Due to the neurodegeneration associated with AD, people with AD often display extended saccade latencies and increased error rates on eye-tracking tasks. Although the effectiveness of using eye tracking to identify cognitive impairment appears promising, research considering the utility of eye tracking during naturalistic tasks, such as reading, in identifying cognitive impairment is limited. The current review identified 39 articles assessing eye-tracking distinctions between people with AD, mild cognitive impairment (MCI), and healthy controls when completing naturalistic task (reading, real-life simulations, static image search) or a goal-directed task involving naturalistic stimuli. The results revealed that naturalistic tasks show promising biomarkers and distinctions between healthy older adults and AD participants, and therefore show potential to be used for diagnostic and monitoring purposes. However, only twelve articles included MCI participants and assessed the sensitivity of measures to detect cognitive impairment in preclinical stages. In addition, the review revealed inconsistencies within the literature, particularly when assessing reading tasks. We urge researchers to expand on the current literature in this area and strive to assess the robustness and sensitivity of eye-tracking measures in both AD and MCI populations on naturalistic tasks.

A Semiparametric Model-Based Friction Compensation Method for Multi-Joint Industrial Robot

Industrial robots have received enormous attention due to their widespread uses in modern manufacturing. However, due to the frictional discontinuous and other unknown dynamics in robotic system, existing researches are limited to simulation and single- or double-joint robot. In this paper, we introduce a semiparametric controller combined by a radial basis function neural network (RBFNN) and complete physical model considering joint friction. First, to extend the NN controller to real-world problems, the continuously differentiable friction (CDF) model is adopted to bring physical information into the learning process. Then, RBFNN is employed to approximate the model error and other unmolded dynamics, and the parameters of CDF model are updated online according to its learning ability. The stability of the robot system can be guaranteed by the Lyapunov theory. The primary parameters of CDF model are determined by the identification experiment and subsequently iteratively updated by the NN. Real-time tracking tasks are performed on a six degree of freedom (DoF) manipulator to follow the desired trajectory. Experimental results demonstrate the effectiveness and superiority of the proposed controller, especially at low speed.

Pedestrian Re-identity Based on ResNet Lightweight Network

With the development of deep learning technology, pedestrian re-identity technology has been widely used in multi-target tracking and cross mirror tracking tasks. In this paper, the classical deep learning ResNet18 network is used for pedestrian re-identity tasks. The advantage of the network is that it can easily realize lightweight deployment. In addition, the labeled smooth cross entropy loss function and migration learning technology are used in the process of training the network, which can realize the accuracy of map 67.8 on the Market1501 data set while lightening the network, and promote the engineering landing of pedestrian re-identity network.

Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking

A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive control of biological (human-like) systems that deliver suboptimal executions for tasks outside of their current knowledge base, by using previously memorized experience. However, biological systems do not solve explicit mathematical equations for solving learning and prediction tasks. This stimulates the proposed hierarchical cognitive-like learning framework, based on state-of-the-art model-free control: (1) at the low-level L1, an approximated iterative Value Iteration for linearizing the closed-loop system (CLS) behavior by a linear reference model output tracking is first employed; (2) an experiment-driven Iterative Learning Control (EDILC) applied to the CLS from the reference input to the controlled output learns simple tracking tasks called ‘primitives’ in the secondary L2 level, and (3) the tertiary level L3 extrapolates the primitives’ optimal tracking behavior to new tracking tasks without trial-based relearning. The learning framework relies only on input-output system data to build a virtual state space representation of the underlying controlled system that is assumed to be observable. It has been shown to be effective by experimental validation on a representative, coupled, nonlinear, multivariable real-world system. Able to cope with new unseen scenarios in an optimal fashion, the hierarchical learning framework is an advance toward cognitive control systems.

Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking

In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where N(N=3,N=5) agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking.

A Fully Featured Thermal Energy Harvesting Tracker for Wildlife

In this paper, we describe a novel animal-tracking-system, solely powered by thermal energy harvesting. The tracker achieves an outstanding 100W of electrical power harvested over an area of only 2 times 20.5cm2, using the temperature difference between the animal’s fur and the environment, with a total weight of 286g. The steps to enhance the power income are presented and validated in a field-test, using a system that fulfills common tracking-tasks, including GPS with a fix every 1,1h–1,5h, activity and temperature measurements, all data wirelessly transmitted via (LoRaWAN) at a period of 14min. Furthermore, we describe our ultra low power design that achieves an overall sleep power consumption of only 8W and is able to work down to temperature differences of 0.9K applied to the TEG.