DOES PLAYING LOCATION-BASED AUGMENTED REALITY GAME INCREASES THE LEVEL OF PHYSICAL ACTIVITY?

Recently, there are an increasing trend in location-based augmented reality (AR) games that require players to move around physically to acquire the in-game features as well as game bonuses. The introduction of this location-based augmented reality (AR) games, specifically, Pokémon Go, has made the players physically move around to achieve higher levels and indirectly, improves the level of physical activity. Thus, the objective of the current study is to examine the association between the time spent playing location-based AR games specifically Pokémon Go, and the level of physical activity of the players in Malaysia. A self-administered questionnaire was circulated among Pokémon Go players and based on the inclusion and exclusion criteria, 47 players were recruited in the study. Global Physical Activity Questionnaire (GPAQ) was used to identify the level of physical activity. The association between time spent playing Pokémon Go and level of physical activity were examined using the Chi-square test. The results of the current study showed no significant association between days spent playing Pokémon Go and level of physical activity (p = .14), hours spent playing Pokémon Go and physical activity (p = .516), or between daily hours spent playing Pokémon Go and daily sedentary time (p = .283). Nevertheless, the mean of the study reported that the physical activity level of the players increased concurrently as the player’s game frequency increases. Further studies are required to shed light on how location-based AR games can be implemented as potential strategies to engage an active lifestyle

Neuromorphic event-based slip detection and suppression in robotic grasping and manipulation

Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise in real-time to improve slip detection. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. Results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz ($\Delta t = 500\mu s$) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.Comment: 18 pages, 14 figure

Neuromorphic vision based contact-level classification in robotic grasping applications

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification

Accelerated generation of elite inbreds in maize using doubled haploid technology

The creation of homozygous parental lines for hybrid development is one of the key components of commercial maize breeding programs. It usually takes up to 6 to 7 generations of selfing to obtain homozygous inbreds from the initial cross using the conventional pedigree method. Using doubled haploid (DH) method, concurrent fixation of all the genes covering entire chromosomes is possible within a single generation. For generation of DH lines, haploids are generated first by several means such as in-vitro method using tissue culture technique and in-vivo method using the haploid inducer (HI) lines. Of which, tissue culture-based methods have shown little promise for large-scale DH production as it needs good infrastructures and technical requirements. In contrast, inducer-based method provides more optimistic solutions for large-scale DH lines production. Due to its rapidity, DH technology is now being adopted in many countries including India for reducing the breeding cycle

Neuromorphic eye-in-hand visual servoing

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based camera sets a limitation on continuous visual feedback due to their low sampling rate and redundant data in real-time image processing, especially in the case of high-speed tasks. Event cameras give human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution ($1\mu s$) with low latency and wide dynamic range. In this paper, we present a visual servoing method using an event camera and a switching control strategy to explore, reach and grasp to achieve a manipulation task. We devise three surface layers of active events to directly process stream of events from relative motion. A purely event based approach is adopted to extract corner features, localize them robustly using heat maps and generate virtual features for tracking and alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space. The controller switches its strategy based on the sequence of operation to establish a stable grasp. The event based visual servoing (EVBS) method is validated experimentally using a commercial robot manipulator in an eye-in-hand configuration. Experiments prove the effectiveness of the EBVS method to track and grasp objects of different shapes without the need for re-tuning.Comment: 8 pages, 10 figure

Characterization of maize genotypes using microsatellite markers associated with QTLs for kernel iron and zinc

224-234Crop genetic resources rich in Fe and Zn provide sustainable and cost-effective solution to alleviate micronutrient malnutrition. Maize being the leading staple crop assumes great significance as a target crop for biofortification. We report here wide genetic variation for kernel Fe and Zn among 20 diverse maize inbreds lines, majority of which were bred for quality protein maize (QPM) and provitamin-A. Kernel Fe ranged from 30.0 - 46.13 mg/kg, while kernel Zn ranged from 8.68-39.56 mg/kg. Moderate but positive correlation was observed between the micronutrients. Characterization using 25 Single sequence repeats (SSRs) linked to QTLs for kernel Fe produced 58 alleles. Similarly, 86 alleles were identified from 35 SSRs linked to QTLs for kernel Zn. One unique allele for kernel Fe and three unique alleles for kernel Zn were identified. The mean polymorphic information content (PIC) was 0.40 for both kernel Fe and  Zn. Jaccard’s dissimilarity coefficients varied from 0.25 - 0.91 with a mean of 0.58 for kernel-Fe while 0.27- 0.88 with a mean of 0.57 for kernel Zn. Principal coordinate analysis depicted diversity of inbreds. Cluster analysis grouped the inbreds into three major clusters for both kernel Fe and Zn. Potential cross combinations have been proposed to develop micronutrient rich hybrids and novel inbreds with higher Fe and Zn. The information generated here would help the maize biofortification programme to develop nutritionally enriched hybrids

Decentralized Grasp Coordination and Kinematic Control for Cooperative Manipulation

Multi-robot systems have shown potential over single robot systems in handling long, large and heavy objects. Manipulation with multiple robots increases task precision and decreases required load capabilities of individual robots while saving cost and space. However, the limitations of multi-robot systems in communication, sensing and knowledge sharing and constraints that occur while performing cooperative manipulation pose challenges for coordination and control. This thesis focuses on multi-robot grasp planning and control of collaborative manipulation.  First, the thesis examines the question how to plan cooperative grasps given a known object for a group of robots that are decentralized and heterogeneous. Decentralized grasp planning approaches which account for incomplete embodiment knowledge and utilize imprecise local information from vision are presented. In addition, to tackle observation constraints, strictly decentralized approaches which only require role assignments are also proposed. Grasp decisions from the approaches are based on evaluation of traditional grasp quality measures under uncertainty. All approaches aim to maximize the quality of cooperative grasp under decentralization and heterogeneity. The approaches are further extended to include task specific information and their usefulness to particular tasks are studied. For experimental study, a complete system pipeline for decentralized grasp coordination is developed and physical metrics to evaluate the success of cooperative grasps are presented. The main contribution of this part is the first extensive investigation of grasp planning for decentralized multi-robot coordination.  Second, given a cooperative manipulation task for heterogeneous multi-robot system, the thesis studies, how to safely coordinate the motions under joint limit constraints. A kinematic controller that employs relative Jacobian for coordination of motions and a control strategy based on task prioritization and smooth activation to ensure safe collaborative manipulation and smooth joint limit avoidance are presented. Behaviour of the controller under different redundancy configurations and applicability in practice for collaborative manipulation are demonstrated. 

Rediscovery of rare porcellanid crab <em>Pseudoporcellanella manoliensis</em> Sankarankutty, 1961, from Palk Bay, Tamil Nadu, India

2182-2185Routine field survey in the Palk Bay landings yielded a single ovigerous specimen of Pseudoporcellanella manoliensis after a gap of nearly 55 years in Indian coastal waters. Rediscovery of this species in ovigerous condition from the Palk Bay reassures that the population still exist in these ecosystems since its description by Sankarankutty in 1961