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Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of 0.6 m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.

Sleep quantity affects an individual's personal health.The g old standard of measurin g sleep and dia g nosin g sleep disorders is Polysomno g raphy (PSG). Althou g h PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emer g ed in the recent past as an alternative to PSG for re g ular sleep monitorin g directly by the user. These devices are intrusive and cause discomfort besides bein g expensive. In this work, we present an al g orithm to detect sleep usin g a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states -Sleep and Wake. In the first approach, we take an equation from Kushida's al g orithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) trainin g .Althou g h all the three approaches are suitable for a phone's resources, each approach demands different amount of resources.While Kushida's equation-based approach demands the least, the HMM trainin g -based approach demands the maximum.We collected data from mobile phone's accelerometer for four subjects for twelve days each. We compare accuracy of sleep detection usin g each of the three approaches with that of Zeo sensor, which is based on Electroencephalo g ram (EEG) sensor to detect sleep. EEG is an important modality in PSG. We find that HMM trainin g -based approach is as much as 84% accurate. It is 15% more accurate as compared to Kushida's equation-based approach and 10% more accurate as compared to statistical method-based approach. In order to concisely represent the sleep quality of people, we model their sleep data usin g HMM. We present an analysis to find out a tradeoff between the amount of trainin g data and the accuracy provided in the modelin g of sleep. We find that six days of sleep data is sufficient for accurate modelin g . We compare accuracy of our HMM trainin g based al g orithm with a representative third party app SleepTime available from Goo g le Play Store for Android. We find that the detection done usin g HMM approach is closer to that done by Zeo by 13% as compared to the third party Android application SleepTime. We show that our HMM trainin g -based approach is efficient as it takes less than ten seconds to g et executed on Moto G Android phone.

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Experiments show that people learned riskaverse solutions without communication With and without communication, robot pairs learned risky, but efficient, outcomes Human-robot pairs often learned risky, but efficient, solutions with communication Without communication, behavioral asymmetries inhibited human-robot cooperation

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