Potential field method to navigate several mobile robots

Navigation of mobile robots remains one of the most challenging functions to carry out. Potential Field Method (PFM) is rapidly gaining popularity in navigation and obstacle avoidance applications for mobile robots because of its elegance. Here a modified potential field method for robots navigation has been described. The developed potential field function takes care of both obstacles and targets. The final aim of the robots is to reach some pre-defined targets. The new potential function can configure a free space, which is free from any local minima irrespective of number of repulsive nodes (obstacles) in the configured space. There is a unique global minimum for an attractive node (target) whose region of attraction extends over the whole free space. Simulation results show that the proposed potential field method is suitable for navigation of several mobile robots in complex and unknown environments. Saroj Kumar Pradhan is faculty of Mechanical Engineering Department with N.I.T., Hamirpur, HP, India. He has received his B.E. degree in Mechanical Engineering from Utkal University and M.E. in Machine Design and Analysis from NIT Rourkela. He has published more than 17 technical papers in international journals and conference proceedings. His areas of research include mobile robots navigation and vibration of multilayred beams. Dayal R. Parhi is working as Assistant Professor at NIT Rourkela, India. He has obtained his first Ph.D. degree in “Mobile Robotics” from United Kingdom and Second Ph.D. in “Mechanical Vibration” from India. He has visited CMU, USA as a “Visiting Scientist” in the field of “Mobile Robotics”. His main areas of current research are “Robotics” and “Mechanical Vibration”. He is supervising five Ph.D. students in the fields of Robotics and Vibration. Email: dayalparhi@yahoo.com. Anup Kumar Panda Received his M.Tech degree from IIT, Kharagpur in 1993 and Ph.D. degree from Utkal University in 2001. He is currently an assistant professor in the Department of Electrical Engineering at National Institute of Technology, Rourkela, India. His areas of research include robotics, Machine Drives, harmonics and power quality. He has published more than 30 technical papers in journals and conference proceedings. He is now involved in two R&D projects funded by Government of India. R. K. Behera is a Senior Lecturer of Mechanical Engineering at National Institute of Technology, Rourkela, India. He has been working as lecturer for more than 10 years. He obtained his BE degree from IGIT, Sarang, of Utkal University. He obtained his ME and Ph.D degrees, both in the field of mechanical engineering from NIT Rourkela.     

Multiscale modeling of screening effects on conductivity of graphene in weakly bonded graphene-dielectric heterostructures

Graphene is often surrounded by different dielectric materials when integrated into realistic devices. The absence of dangling bonds allows graphene to bond weakly via the van der Waals interaction with the adjacent material surfaces and to retain its peculiar linear band structure. In such weakly bonded systems, however, the electronic properties of graphene are affected by the dielectric screening due to the long-range Coulomb interaction with the surrounding materials. Including the surrounding materials in the first principles density functional theory (DFT) calculations is computationally very demanding due to the large supercell size required to model heterogeneous interfaces. Here, we employ a multiscale approach combining DFT and the classical image-potential model to investigate the effects of screening from the surrounding materials (hBN, SiC, SiO₂, Al₂O₃, and HfO₂) on the dielectric function and charged impurity scattering limited conductivity of graphene. In this approach, the graphene layer is modeled using DFT and the screening from the surrounding materials is incorporated by introducing an effective dielectric function. The dielectric function and conductivity of graphene calculated using the simplified two-band Dirac model are compared with DFT calculations. The two-band Dirac model is found to significantly overestimate the dielectric screening and charged impurity scattering limited conductivity of graphene. The multiscale approach presented here can also be used to study screening effects in weakly bonded heterostructures of other emerging two-dimensional materials such as metal dichalcogenides.     

Thermal and Electrical Conductivity of Copper-Graphene Heterosystem: An Effect of Strain and Thickness

Copper-graphene (Cu/Gr) composite carries high thermal (κ) and electrical (σ) conductivities compared with pristine copper film/surface. For further improvement, strain is applied (compressive and tensile) and thickness is changed (of both copper and graphene). It is observed that electronic thermal conductivity (κ_e) and σ enhance from 320.72 to 869.765 W mK⁻¹ and 5.28 × 10⁷ to 23.01 × 10⁷ S m⁻¹, respectively, by applying 0.20% compressive strain. With the increase in copper thickness (three to seven layers) in Cu(111)/single-layer-graphene (SLG) heterosystem, κ_e increases from 320.72 to 571.81 W mK⁻¹ while electrical resistivity (ρ ∝ (1/σ)) decreases from 0.189 × 10⁻⁷ to 0.117 × 10⁻⁷ Ωm. Furthermore, with the increase in graphene thickness (one to four layers) in seven-layer Cu(111)/multilayer-graphene (MLG) heterosystem, κ_e enhances upto 126% while ρ decreases upto 70% compared with the three-layer Cu(111)/SLG. A large available state near Fermi level (of Cu/Gr heterosystem) offers the conduction of more electrons from valence to conduction bands. The increasing thickness broadens this state and enhances conduction electrons. The electron localization function decreases with increasing thickness, suggesting electrons are delocalized at copper-graphene junction, resulting in an increase of free electrons that enhance κ_e and σ. Herein, it is useful in advancing the thermal management of electronic chips and in applying hybrid copper-graphene interconnects.