Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds

A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy

Spontaneous pneumomediastinum, pneumothorax and subcutaneous emphysema in COVID-19: case report and literature review

Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide. Numerous studies have shown its typical and atypical CT findings. We report one COVID-19 patient who presented with a transient pneumothorax, spontaneous pneumomediastinum (SP), as well as subcutaneous emphysema during hospitalization. Chest CT andclinical findings were discussed, and a literature review is presented. The probable cause of SP in COVID-19 was alveolar damage. Once pneumothorax and SP were present, the patient should be carefully monitored to prevent respiratory deterioration, especially when lung lesions are severe

Optimal Operation of an Integrated Electricity-heat Energy System Considering Flexible Resources Dispatch for Renewable Integration

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricity-heat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored

Cloning and expression of pineapple sucrosephosphate synthase gene during fruit development

A 1132-base pairs (bp) polymerase-chain-reaction product of sucrose-phosphate synthase (SPS) (EC 2.3.1.14) from pineapple (Ananas comosus cv. Comte de paris) fruit was cloned and nominated as Ac- SPS1. The sequence encodes a putative 377 amino acids protein containing two serine conserved features that had been found in other plant SPS genes: the presence of a 14-3-3 protein special binding domain and an activated site of osmosis stress, which can been activated by phosphorylation and dephosphorylation. The Neighbour-joining tree revealed that Ac-SPS1 belonged to the first kind of sucrose phosphate synthase gene. The results indicated that, the Ac-SPS1 expression was low in the earlier period of fruit growth, then, increasing from 20 days after anthesis and gradually a falling on 40 days, reached the peak with the highest value around 70 days. The SPS activity and sucrose content reached their maximum 80 days after anthesis. It proved that the  accumulation of sucrose was correlated with SPS activity and mRNA content and it maximally occurred at 10 d after SPS mRNA and activity had reached its maxima. These results indicated that Ac-SPS1 gene played a key role in sucrose accumulation during the pineapple fruit development and transcriptional activation with increase in Ac- SPS1 expression might be important regulatory events of sugar during pineapple fruit maturation.Key words: Pineapple fruit, sucrose phosphate synthase, gene cloning, expression