CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning

Autonomous navigation in offroad environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to remain hidden from outside observers remains an underexplored area. In this paper, we propose a novel Deep Reinforcement Learning (DRL) based algorithm, called CoverNav, for identifying covert and navigable trajectories with minimal cost in offroad terrains and jungle environments in the presence of observers. CoverNav focuses on unmanned ground vehicles seeking shelters and taking covers while safely navigating to a predefined destination. Our proposed DRL method computes a local cost map that helps distinguish which path will grant the maximal covertness while maintaining a low cost trajectory using an elevation map generated from 3D point cloud data, the robot's pose, and directed goal information. CoverNav helps robot agents to learn the low elevation terrain using a reward function while penalizing it proportionately when it experiences high elevation. If an observer is spotted, CoverNav enables the robot to select natural obstacles (e.g., rocks, houses, disabled vehicles, trees, etc.) and use them as shelters to hide behind. We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm. Additionally, we evaluate CoverNav's effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects. We observe competitive performance comparable to state of the art (SOTA) methods without compromising accuracy

Simultaneous RP-HPLC and UV Spectroscopic Method Development and Validation for Estimation of Ibandronate Sodium in Bulk and Pharmaceutical Dosage Form

The present study describes a simple, accurate, precise and cost effective UV-Vis Spectroscopic and RP-HPLC method for the estimation of Ibandronate sodium (IBN). The determination of Ibandronate sodium (IBN) was performed by both UV and RP-HPLC method using 215 nm as the determination wavelength. The drug was dissolved in NaOH solution (0.1N NaOH) for estimation in UV and in distilled water for the estimation in RP-HPLC using mobile phase 0.01 M Sodium dihydrogen phosphate (NaH2PO4): Acetonitrile (80:20), pH being adjusted to 3.3 with 10% ortho-phosphoric acid. A linear response was observed in the range of 10-50 μg ml-1 (R2 = 0.9981) for UV-Spectroscopy, whereas for RP-HPLC the linear response was observed in the range of 20-70 μg ml-1 (R2 = 0.9965). The limits of quantitation (LOQ) were estimated as 0.1 μg ml-1 and 0.05 μg ml-1, respectively for UV and RP-HPLC respectively. The recoveries of IBN from the marketed formulation were found to be within 100 ± 2% by both the methods. These methods were then effectively applied for the estimation of Boniva (tablet) and the results were obtained according to nominal content. The statistical analysis revealed that there is no significant difference (p > 0.05) between UV and HPLC methods regarding validation parameters and assay content

Estimation of serum malondialdehyde in oral cancer and precancer and its association with healthy individuals, gender, alcohol, and tobacco abuse

Background: Tobacco and alcohol induces generation of free radicals and reactive oxygen species, which are responsible for high rate of lipid peroxidation. Malondialdehyde is the most widely used marker of lipid peroxidation. The aim of the study was to estimate serum malondialdehyde level in oral precancer, oral cancer, and normal individuals. Materials and Methods: In this study serum malondialdehyde was measured according to the method of Ohkawa et al in 30 normal individuals and 30 patients each with histopathologically diagnosed oral precancer, and oral cancer. Results: The mean serum malondialdehyde level in the control group was found to be 5.107 \ub1 2.32 \u3b7mol/ml, whereas it was 9.33 \ub1 4.89 \u3b7mol/ml and 14.34 \ub1 1.43 \u3b7mol/ml in oral precancer and oral cancer, respectively. There was statistically significant increase in serum malondialdehyde levels in the oral precancer and oral cancer patients compared with the control group. Conclusion: Increased serum malondialdehyde in oral cancer and oral precancer would serve as a valuable marker for both preventive and clinical intervention, and may deserve further investigation for the early diagnosis, treatment, and prognosis

Estimation of serum malondialdehyde in oral cancer and precancer and its association with healthy individuals, gender, alcohol, and tobacco abuse

Background: Tobacco and alcohol induces generation of free radicals and reactive oxygen species, which are responsible for high rate of lipid peroxidation. Malondialdehyde is the most widely used marker of lipid peroxidation. The aim of the study was to estimate serum malondialdehyde level in oral precancer, oral cancer, and normal individuals. Materials and Methods: In this study serum malondialdehyde was measured according to the method of Ohkawa et al in 30 normal individuals and 30 patients each with histopathologically diagnosed oral precancer, and oral cancer. Results: The mean serum malondialdehyde level in the control group was found to be 5.107 ± 2.32 ηmol/ml, whereas it was 9.33 ± 4.89 ηmol/ml and 14.34 ± 1.43 ηmol/ml in oral precancer and oral cancer, respectively. There was statistically significant increase in serum malondialdehyde levels in the oral precancer and oral cancer patients compared with the control group. Conclusion: Increased serum malondialdehyde in oral cancer and oral precancer would serve as a valuable marker for both preventive and clinical intervention, and may deserve further investigation for the early diagnosis, treatment, and prognosis