136 research outputs found
Corrigendum to ‘Toll-like receptors in immunity and inflammatory diseases: Past, present, and future’ [International Immunopharmacology 59 (2018) 391–412](S156757691830095X)(10.1016/j.intimp.2018.03.002)
No full textThe authors regret that the name of the author was mentioned incorrectly in the earlier version of the manuscript. It should be mentioned as Vijay Kumar. The authors would like to apologise for any inconvenience caused
Invigorating self-regulated learning strategies of mathematics among higher education students
No full textTeacher effectiveness in relation to type of ınstitution, emotional ıntelligence and teaching experience
No full textPresent study aims at studying the influence of the emotional intelligence, age and
qualification on the teacher effectiveness of the teachers. The study population included
secondary schools teachers working in Jalandhar and Ludhiana districts of Punjab, India.
Using a multi-stage random sampling method, a sample volume of 739 teachers was
determined. Two main instruments were used to measure the study variables: a 80-item
questionnaire by C R Darolia on emotional intelligence (five scales: Self-awareness,
Motivating Oneself, Managing Emotions, Empathy and Handling Relations, and a 60-
item Teacher Effectiveness Scale by Kumar and Mutha, 1985. The frequency, mean and
standard deviation values were calculated and a 3 way ANOVA analysis was performed
to evaluate the statistical significance of the findings. The study results revealed that:
(a) Differences in teacher effectiveness of groups of teachers’ based on school type i.e.
government and private secondary schools, are not significant. (b) Teachers with low
emotional intelligence are less effective in teaching than teachers with high emotional
intelligence. c) More experienced teachers are found to be more effective than less
experienced teachers. Thus, it can be concluded that emotional intelligence and teaching
experience matters in making of a teacher to be effective
Planning Natural Locomotion for Articulated Soft Quadrupeds
No full textEmbedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals
No full textIn unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive contouring control (LMPCC) to generate motion plans satisfying kino-dynamic and collision constraints in real-time while including regulation awareness. To incorporate regulations in the planning stage, we propose a cost function encouraging compliance with rules describing interactions with other vessels similar to COLlision avoidance REGulations at sea (COLREGs). These regulations are essential to make an ASV behave in a predictable and socially compliant manner with regard to other vessels. We compare the framework against baseline methods and show more effective regulation-compliant avoidance of moving obstacles with our motion planner. Additionally, we present experimental results in an outdoor environment.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
No full textSearch missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System
No full textThis paper discusses a low-cost, open-source and open-hardware design and performance evaluation of a low-speed, multi-fan wind system dedicated to micro air vehicle (MAV) testing. In addition, a set of experiments with a flapping wing MAV and rotorcraft is presented, demonstrating the capabilities of the system and the properties of these different types of drones in response to various types of wind. We performed two sets of experiments where a MAV is flying into the wake of the fan system, gathering data about states, battery voltage and current. Firstly, we focus on steady wind conditions with wind speeds ranging from 0.5 m S-1 to 3.4 m S-1. During the second set of experiments, we introduce wind gusts, by periodically modulating the wind speed from 1.3 m S−1 to 3.4 m S−1 with wind gust oscillations of 0.5 Hz, 0.25 Hz and 0.125 Hz. The “Flapper” flapping wing MAV requires much larger pitch angles to counter wind than the “CrazyFlie” quadrotor. This is due to the Flapper's larger wing surface. In forward flight, its wings do provide extra lift, considerably reducing the power consumption. In contrast, the CrazyFlie's power consumption stays more constant for different wind speeds. The experiments with the varying wind show a quicker gust response by the CrazyFlie compared with the Flapper drone, but both their responses could be further improved. We expect that the proposed wind gust system will provide a useful tool to the community to achieve such improvements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio
Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks
No full textRelative localization is an important ability for multiple robots to perform cooperative tasks in GPS-denied environments. This paper presents a novel autonomous positioning framework for monocular relative localization of multiple tiny flying robots. This approach does not require any groundtruth data from external systems or manual labeling. Instead, the proposed framework is able to label real-world images with 3D relative positions between robots based on another onboard relative estimation technology, using ultra-wideband (UWB). After training in this self-supervised manner, the proposed deep neural network (DNN) can predict relative positions of peer robots by purely using a monocular camera. This deep learning-based visual relative localization is scalable, distributed, and autonomous. We also built an open-source and lightweight simulation pipeline by using Blender for 3D rendering, which allows synthetic image generation of other robots, and generalized training of the neural network. The proposed localization framework is tested on two real-world Crazyflie2 quadrotors by running the DNN on the onboard AIdeck (a tiny AI chip and monocular camera). All results demonstrate the effectiveness of the self-supervised multi-robot localization method. Video: https://youtu.be/7arkaIblPpsGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio
Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots
No full textDistributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL) algorithms are promising for control policy design. However, how to ensure safety in terms of state constraints in RL remains a significant issue. This paper proposes a barrier function-based safe reinforcement learning algorithm for DMPC of nonlinear multi-robot systems under state constraints. The proposed approach is composed of several local learning-based MPC regulators. Each regulator, associated with a local system, learns and deploys the local control policy using a safe reinforcement learning algorithm in a distributed manner, i.e., with state information only among the neighbor agents. As a prominent feature of the proposed algorithm, we present a novel barrier-based policy structure to ensure safety, which has a clear mechanistic interpretation. Both simulated and real-world experiments on the formation control of mobile robots with collision avoidance show the effectiveness of the proposed safe reinforcement learning algorithm for DMPC.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Robot Dynamic
Locally Recoverable Streaming Codes for Packet-Erasure Recovery
No full textStreaming codes are a class of packet-level erasure codes that are designed with the goal of ensuring recovery in low-latency fashion, of erased packets over a communication network. It is well-known in the streaming code literature, that diagonally embedding codewords of a [τ+1,τ+1-a] Maximum Distance Separable (MDS) code within the packet stream, leads to rate-optimal streaming codes capable of recovering from a arbitrary packet erasures, under a strict decoding delay constraint τ. Thus MDS codes are geared towards the efficient handling of the worst-case scenario corresponding to the occurrence of a erasures. In the present paper, we have an increased focus on the efficient handling of the most-frequent erasure patterns. We study streaming codes which in addition to recovering from a>1 arbitrary packet erasures under a decoding delay τ, have the ability to handle the more common occurrence of a single-packet erasure, while incurring smaller delay r<τ. We term these codes as (a,τ,r) locally recoverable streaming codes (LRSCs), since our single-erasure recovery requirement is similar to the requirement of locality in a coded distributed storage system. We characterize the maximum possible rate of an LRSC by presenting rate-optimal constructions for all possible parameters {a,τ,r}. Although the rate-optimal LRSC construction provided in this paper requires large field size, the construction is explicit. It is also shown that our (a,τ=a(r+1)-1,r) LRSC construction provides the additional guarantee of recovery from the erasure of h, 1 ≤ h ≤ a, packets, with delay h(r+1)-1. The construction thus offers graceful degradation in decoding delay with increasing number of erasures
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