Abstract

The problem of predicting the 3D native conformation of a polypeptide given the primary sequence is of fundamental importance in contemporary biology. Here we present a novel Genetic algorithm method to obtain the optimal geometry of a pentapeptide. The pentapeptide moiety is found in wide set of configurations in protein databases. A genetic algorithm utilizes optimization procedure similar to natural genetic evolution. Unlike other genetic algorithms, which are constrained to operate on bit strings, the algorithm used here operates on real values. A random population of conformations is generated taking into account the constraints of Ramachandran plot. This population advances through successive generations in which the solutions evolve via genetic operators (Mutate, Variate and Crossover); in such a way that it gives both better survival chance to fitter soluctions and a larger diversity within the population. More attention will be given to favorable local structures while unfavorable local structures will be rapidly abandoned on the basis of their fitness functions (potential energy), which are calculated using the GROMOS-96 force field of Spdbv version-3.7. The SCWRL3.0 program developed at Dunbrack lab is used for placing side chains to a fixed backbone coordinates. This algorithm proved to be very efficient for the penta-peptide Met-Enkephalin, a natural Opioid polypeptide produced in the brain. The algorithm converged only in 41 minutes and in 23 generations keeping the population size fixed (20 individual per population). The converged structure is energetically as stable as the native conformation of the protein. Its shows a RMSD of 5.9 from the original native conformation.

Abstract

Controlling dynamics at the atomic level is a primary goal of chemists. In the last decade, Optimal control theory (OCT) has emerged as one comprehensive means of designing laser pulses for achieving prescribed dynamical goals. We have used OCT to obtain infrared laser pulses for the selective vibrational excitation of several different systems, from a simple diatomic to triatomic systems located in complex molecular environments. We will present our results obtained for the diatomic system HF applying OCT using different algorithmic methods, namely an iterative method, conjugate gradient method and genetic algorithms. The results of a new dynamic method for applying the penalty term needed to keep the power in the electric field of the laser within reasonable bounds will be discussed. For the triatomic FeCO, we use a two mathematical dimensional model of carboxy-myoglobin. Density functional theory is used to obtain the potential energy and dipole moment surfaces of the active site model. The Conjugate gradient method is employed to optimize the cost functional and to obtain the optimized laser pulses. Optimized laser fields are found which give virtually 100% excitation probability to preselected vibrational levels. Similar application to hydrogen bonding in a complex system will also be presented.

Abstract

Kinematically consistent paths for multiple mobile robots that are collision free for the next T steps are generated by forming collision free polygons (CFP). The polygons can be generated in a distributive fashion for each robot. All paths inside CFP that are kinematically feasible for a certain robot are collision free with all other robots in the same collision cluster for the next T time samples and with any other robot in the workspace in general. The construction of CFP is through two log-linear complex operations. The first one involves computing the intersection of the path container polygon (PCP) of a robot with PCP of other robots in the cluster. The second involves computing the convex hull of intersecting points arising from the intersection of PCPs. Once CFP is computed for a robot it chooses a point within the CFP (its next waypoint) that minimizes a cost function and moves towards the point. The process is repeated till the goal is reached. The main advantage of this method is that the best and worst-case times for finding a T-step ahead collision free path is always log-linear. In many randomized search methods, finding the paths till the next waypoint results in a worst case complexity exponential in number of robots. The efficacy of the current method is well portrayed through simulations. Comparative results show that the following method finds a collision free path to next way point much quicker.

Abstract

This paper studies the problem of traversing a rough terrain by wheeled vehicles. Here rough terrain implies terrain which is geometrically not ideal. The criterion for mobility of a wheeled vehicle in any terrain is formally developed, providing insights into the mechanical structure requirements of the vehicle. A vehicle structure with an actively articulated suspension is found as a solution to improved rough terrain mobility. The contact forces of the vehicle with the surface being traversed are identified as the critical factor in determining the traversability of the surface. Hence a control strategy involving the control of the contact forces (normal and traction) is proposed. The key feature of the locomotion strategy, thus developed, is that it provides a solution involving dynamics of the main body for improving mobility in rough terrain.

Abstract

The algorithm presented in this paper is designed to be used in automated multi-sensor surveillance systems which require observation of targets in a bounded area to optimize the performance of the system. There have been many approaches which deal with multi-sensor tracking and observation, but there haven't been many which deal purely with targets detections i.e. each target needs to only be detected once. The metric used to gauge the performance of the system is percentage of targets detected among those that enter the area. Targets enter the area through source points on the side of the area according to Poisson distribution, the rate of entry is constant for all sources. The algorithm presented here uses target arrival information, sensor positions to generate an optimal motion strategy for the multi-sensor system every T time-steps i.e. every T time-steps, the probability of finding undetected targets is estimated, the optimal sensor paths for the next T time-steps are calculated. The algorithm performs robustly and optimally detecting around 80% of the targets that enter the area

Abstract

A methodology for real-time navigation of an all terrain vehicle capable of navigation between floors of a building mediated by a wireless sensor network is detailed in this paper. The map of the building is unknown to the robot. Sensor motes scattered in the building guide the robot to its desired destination. The guidance is in the form of specifying the next waypoint to the goal by the network. Local navigation between waypoints is achieved by a fuzzy logic based navigation system

Abstract

A cooperative methodology for collision avoidance of multiple wheeled robots, having respective goals to reach, is detailed in this paper. The paths executed by the robots are continuous not only in terms of positions reached by the robots but also in terms of their velocities. A navigation function is designed that, apart from taking into account goal reaching and avoidance behaviors, also implicitly captures cooperative behavior amongst robots by identifying spaces where collisions between robots tend to be minimized. A search in the joint space of linear and angular velocities of the robots results in selection of a linear and angular velocity tuple for each robot that minimizes the navigation function. Simulated results portray the efficacy of the methodology

Abstract

This paper presents a methodology for achieving SLAM onto the occupancy grid framework with the data only from sonar sensors. Sonar data are highly noisy and unpredictable. Sonar does not give the consistent readings for a point from two different positions, so the approaches which rely on correspondence reading matching will prone to fail without exhaustive mathematical calculations of sonar modeling and environment modeling. Also, if features are being use to localize then the robot needs to revisit those features exactly, to localize, which itself will not be accurate because robot will not be at the exact position from where that feature has been detected. Hence it will not get back those feature readings using sonar. Here we are presenting a hybrid approach based on feature chain. Instead of relying completely on feature mapping and point matching, it finds the links between features to localize. It will drastically reduce the need of revisiting a feature to localize and hence reducing the exploration overhead, while handling other issues of problems with point or feature matching. We map features onto occupancy grid (OG) framework taking advantage of its dense representation of the world. Combining features onto OG overcomes many of its limitations such as the independence assumption between cells and provides for better modeling of the sonar implicitly providing more accurate maps

Abstract

Collision avoidance is one of the essential pillars of a wheeled robotic system. A wheeled mobile robot (called mobile robot for conciseness henceforth) requires for effective functioning an integrated system of modules for (i) map building,(ii) localization,(iii) exploration,(iv) planning and (v) collision avoidance. Often (i) and (ii) are entailed to be done simultaneously by robots resulting in a vast array of literature under the category SLAM, simultaneous localization and mapping. In this chapter we focus on the aspect of collision avoidance specifically between multiple robots, the remaining themes being too vast to even get a brief mention here. We present a cooperative conflict resolution strategy between multiple robots through a purely velocity control mechanism (where robots do not change their directions) or by a direction control method. The conflict here is in the sense of multiple robots competing for the same space over an overlapping time window. Conflicts occur as robots navigate from one location to another while performing a certain task. Both the control mechanisms attack the conflict resolution problem at three levels, namely (i) individual,(ii) mutual and (iii) tertiary levels. At the individual level a single robot strives to avoid its current conflict without anticipating the conflicting robot to cooperate. At the mutual level a pair of robots experiencing a conflict mutually cooperates to resolve it. We also denote this as mutually cooperative phase or simply cooperative phase succinctly. At tertiary level a set of robots cooperate to avoid one or more conflicts amidst them

Abstract

Different methodologies have been employed to solve the multi-sensor multi-target detection problem in a variety of scenarios. In this paper, we devise a time-step optimal algorithm for this problem when all but a few parameters of the sensor/target system are unknown. Using the concept of covering graph, we find an optimum solution for a single sensor, which is extended to multiple sensors by a tagging operation. Both covering graph and tagging are novel concepts, developed in the context of the detection problem for the first time, and bring a mathematical elegance to its solution. Furthermore, an implementation of the resulting algorithm is found to perform better than other notable approaches. The strong theoretical foundation, combined with the practical efficacy of the algorithm, makes it a very attractive solution to the problem