Energy-optimal trajectory planning for car-like robots

When a battery-powered robot needs to operate for a long period of time, optimizing its energy consumption becomes critical. Driving motors are a major source of power consumption for mobile robots. In this paper, we study the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed during motion. We start with an established model for energy consumption of DC motors. We first study the problem of finding the energy optimal velocity profiles, given a path for the robot. We present closed form solutions for the unconstrained case and for the case where there is a bound on maximum velocity. We then study a general problem of finding an energy optimal path along with a velocity profile, given a starting and goal position and orientation for the robot. Along the path, the instantaneous velocity of the robot may be bounded as a function of its turning radius, which in turn affects the energy consumption. Unlike minimum length paths, minimum energy paths may contain circular segments of varying radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii. Our algorithm uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves. In addition, we present a calibration method to find energy model parameters. Finally, we present results from experiments conducted on a custom-built robot for following optimal velocity profiles.     

Energy‐efficient Path Planning for Solar‐powered Mobile Robots*

We explore the problem of energy‐efficient, time‐constrained path planning of a solar‐powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desirable. We present a method that uses Gaussian Process regression to build a solar map in a data‐driven fashion. Using this map and an empirical model for energy consumption, we perform dynamic programming to find energy‐minimal paths. We validate our map construction and path‐planning algorithms with outdoor experiments, and we perform simulations on our solar maps to further determine the limits of our approach. Our results show that we can effectively construct a solar map using only a simple current measurement circuit and basic GPS localization, and this solar map can be used for energy‐efficient navigation. This establishes informed solar harvesting as a viable option for extending system lifetime even in complex environments with low‐cost commercial solar panels.     

Expert-based maintenance: a study of its effectiveness

Monitoring of computer-based systems by a supervisory computer is common for high-availability systems. Expert-based supervisory systems are being proposed which are able to use dynamic information of the system to operate them with increased reliability. This paper brings out the functional capabilities of expert-based maintenance, and presents an analytic model to evaluate the effectiveness of the expert system in maintenance. The abilities of the expert system to maintain the host are parameterized and their effects on the performance of the system are studied. The results show possible improvement in the performance of a host due to expert-based maintenance     

Cautious Greedy Strategy for Bearing‐only Active Localization: Analysis and Field Experiments

We study the problem of optimally choosing bearing measurement locations for localizing a stationary target in minimum time. The targets are transmitting radio tags, and bearing measurements are acquired from radio signal strength by a robot carrying a direction‐sensitive radio antenna. Actively localizing radio tags has many applications in surveillance, search and rescue, and environmental monitoring. Our work is motivated by the task of monitoring radio‐tagged invasive fish using autonomous vehicles. An active localization algorithm is provided in order to locate a target up to the desired uncertainty. The time required to locate the target includes time spent traveling as well as taking measurements. Since bearing measurements inferred from radio signals have an inherent ambiguity associated with them, the proposed algorithm chooses measurements to minimize the effect of ambiguous measurements on the target estimate. We present a closed‐form bound on the time required to locate a target using the presented active localization strategy. We also present the first known lower bound on the time required by any active localization algorithm (including the unknown optimal). Finally, we bound the ratio of the upper and lower bounds, showing that the expected cost of our algorithm is within a constant factor of the expected cost of the optimal solution. Robust initialization strategies that are motivated by practical sensing limitations are also provided. Our algorithm is shown to reliably locate radio tags to a desired uncertainty in simulations and multiple field experiments.     

Radiation search operations using scene understanding with autonomous UAV and UGV

Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data are used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g., road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.     

Thermal model for dc characteristics of algan/gan hemts including self-heating effect and non-linear polarization

A thermal model based on the polynomial relationship of n_s and E_F is presented. The effect of temperature rise due to self-heating is studied on various parameters viz. polarization, electron mobility, velocity saturation, low-field mobility and thermal conductivity of substrate. Parasitic resistances and channel length modulation were also taken into consideration. The relationship between self-heating effect and device parameters was studied. The model is based on closed-form expressions and does not require elaborate computation. After including self-heating effect in calculations of current–voltage characteristics, our results agreed well with published experimental data.     

Algorithms and experiments on routing of unmanned aerial vehicles with mobile recharging stations

We study the problem of planning a tour for an energy‐limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged at a site or along an edge either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines the best locations to place stationary charging stations. We study three variants for charging: Multiple stationary charging stations, single mobile charging station, and multiple mobile charging stations. As the problems we study are nondeterministic polynomial time (NP)‐Hard, we present a practical solution using Generalized TSP that finds the optimal solution that minimizes the total time, subject to the discretization of battery levels. If the UGVs are slower than the UAVs, then the algorithm also finds the minimum number of UGVs required to support the UAV mission such that the UAV is not required to wait for the UGV. Our simulation results show that the running time is acceptable for reasonably sized instances in practice. We evaluate the performance of our algorithm through simulations and proof‐of‐concept field experiments with a fully autonomous system of one UAV and UGV.     

Discriminate,locate and mitigate DDoS traffic in presence of Flash Crowd in Software Defined Network

The Journal of Supercomputing - Discrimination of Flash crowd and Distributed Denial of Service (DDoS) traffic has been addressed already in legacy network and Software Defined Network (SDN), and...