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

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

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

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

We present in this paper a methodology for computing the maximum velocity profile over a trajectory planned for a mobile robot. Environment and robot dynamics as well as the constraints of the robot sensors determine the profile. The planned profile is indicative of maximum speeds that can be possessed by the robot along its path without colliding with any of the mobile objects that could intercept its future trajectory. The mobile objects could be arbitrary in number and the only information available regarding them is their maximum possible velocity. The velocity profile also enables one to deform planned trajectories for better trajectory time. The methodology has been adopted for holonomic and nonholonomic motion planners. An extension of the approach to an online real-time scheme that modifies and adapts the path as well as velocities to changes in the environment such that both safety and execution time are not compromised is also presented for the holonomic case. Simulation and experimental results demonstrate the efficacy of this methodology.

Abstract

RS paths for a robot with both front and rear wheel steer. We call such robots as FR steer. The occurrence of such paths is due to the additional maneuver possible in such a robot which we call parallel steer, in addition to the ones already present in a vehicle with only front wheel steering. Hence we extend the optimal path set, containing only a single element to a set, containing n elements, thereby extending its configuration set along the optimal path from the initial to the final configuration. This extension of the set to set is made possible by introducing a special set, which we call the Parallel Steer (PS) Set. Such an extension of the configuration set would increase the size of the final configuration set achievable by a path that is optimal in free space. In the following discussion, we shall term all paths whose length is equal to an RS path as optimal.

Abstract

Different methodologies exist to direct the motion of sensors to detect targets moving across an environment in various scenarios. However, some of these do not model that navigation scenario in an environment in which obstacles are present. We extend our earlier algorithm for optimal target detection, as well as other algorithms reported in literature, to this case, and make a detailed comparison of their performance. This makes clear that the current algorithm is competitive in applications where target statistics are known in advance; otherwise, a heuristic technique by Sukhatme and Jung performs best.

Abstract

We present a strategy for resolving multiple hypotheses of a robot's state during global localization. The strategy operates in two stages. In the first stage a unique direction of the motion is sought that resolves or eliminates maximum number of hypotheses. In the second stage, among the frontier areas arising from the multiple hypotheses states, that frontier is chosen which resolves the maximum number of the hypotheses. The two stages are alternated till a unique hypothesis emerges. Simulation and experimental results verify the efficacy of this method. A comparison with other methods based on entropy minimization, and minimum distance travel portrays the advantage of the current methodology. A convergence proof for the algorithm is also presented.

Abstract

We present an extension of the FCM over the loss functions used in the M-estimators of robust statistics akin to the generalization of the fuzzy-C-means algorithms over the norm distances [1]. The effect of these estimators in reducing the bias of the outliers while estimating the cluster prototypes are studied and compared. The comparisons have been done over synthetic data as well as simulated data consisting of range sensor readings representative of the objects in the neighborhood of a navigating mobile robot. For the synthetic data set the comparison is attempted over the popular FCM algorithm. For the sensory data set the Adaptive Fuzzy Clustering (AFC) algorithm [2] has been employed and extended over the loss functions of robust statistics. The AFC is utilized considering the shape of the objects encountered by the robot in a typical indoor environment.

Abstract

We present a framework for modeling and analysis for a surveillance network consisting of multiple sensors. Sensors monitor targets that crisscross a rectangular surveillance zone. When a sensor pursuits a target it leaves areas unguarded through which other targets can get past undetected. A methodology that computes the tracking time for a sensor such that a fraction of the targets expected to cross its home area is detected to an arbitrary probabilistic guarantee is presented based on the framework. Targets enter the surveillance zone according to Poisson statistics. The time spent by a target within a sensor's home area follows uniform random statistics. The home area of the sensor is the area guarded by it when it is stationed at its home position, its default position when it is not in pursuit of a target. The framework is further extended to situations where multiple sensors monitor the same home area. Simulation results presented corroborate with the probabilistic framework developed and verify its correctness for single as well as multi-sensor cases.