To avoid risking the lives of victims and rescue team personnel in the event of disasters like earthquakes, volcanic eruptions, hurricanes, etc., search and rescue (SAR) robots are increasingly incorporated into the operation. One of the major challenges in integrating SAR robots into rescue operations is the potentially severely damaged infrastructure within the disaster site. A critical component of this is a functional communication system which is essential for exchanging real-time information between the robots in the field and the base station (BS). Given that there might be either limited coverage or a complete failure of standard communication systems in a severely affected disaster site, a novel disaster management system named Titan disaster management system (DMS), which is based on low-power wide-area networks (LPWANs) LoRa technology is proposed. Its development consists of three solutions, which are Titan coordination, communication, and navigation solutions. The Titan coordination solution based on a robot platoon configuration has been developed to assist the rescue team in establishing a BS within the disaster-affected area. The victims, the BS, and the SAR robots may all communicate with one another using the Titan communication system. For guiding the SAR robot through the devastated disaster scene, the Titan navigation solution named fuzzy inference system vector field histogram (FISVFH) was developed. The different phases of the development of Titan DMS are discussed, and the results obtained from field tests are presented in this article.

Autonomous Vehicles society is blooming and evolving as driving safety is expected to improve by autonomous vehicles (AVs) compared with human driving. Intersection mobility is one of the challenging tasks for AVs; thus, this paper proposes two algorithms. The first proposed algorithm uses a driving space to generate a motion trajectory based on LiDAR data and road geometry. The other algorithm provides C2smooth path Bézier curve by joining two cubic Bézier curve segments. Information pieces from Polaris car's position during turning are utilized to compute the intersection road's corner radius. The proposed algorithms have been tested in multiple simulation environments with different traffic conditions in Gazebo using Polaris car. Simulation results show that Polaris car can turn continuously and smoothly through the intersection square, demonstrating the effectiveness of the proposed algorithms.

Robots that perform autonomous maneuvering in a disaster environment usually dont have perfect understanding of the environment in advance. The robot is continuously evaluating the environment as it proceeds, deciding the optimal way to traverse the environment to get to the goal. A critical aspect of this decision is the robot estimate of the terrain accessibility index, which quantifies how easy it is to navigate through the immediate terrain. This paper represents a new method to calculate terrain accessibility index based on obstacle distance to the robots position. In addition, a Fuzzy Inference System Vector Field Histogram (FISVFH) method has been designed for automating the selection of the robots linear and angular velocities in the VFH (Vector Field Histogram) algorithm, based on the calculated sector accessibility index. The proposed method is a two-input and two-output Fuzzy Inference System, where the current robot heading, and sector accessibility index serve as the input, and the corresponding linear and angular velocities to the VFH algorithm are outputs. The VFH, VFH + and FISVFH are tested both in simulation and experimentation in 4 environments that are known to result in failures in VFH and VFH +, for comparison purposes. In addition, the algorithm was verified through experimental setup of a disaster prone environment. In both simulation and experimentation the results show that FISVFH outperforms VFH and VFH +. It is also shown that the FISVFH algorithm is capable of handling the disaster prone environment. Overall, the FISVFH algorithm enables the robot to get to the goal faster and also produces a smoother path while doing so.

This paper introduces the minimum-time arbitrarily constrained distributed scheduling problem. The optimal distributed nonsequential backtracking algorithm is used by agents to schedule tasks where the tasks are related by constraints and where the assignment of tasks to agents may change during scheduling. The problem is distributed in that no agent has full knowledge either of the communication network or of the scheduling constraints. Each agent knows the assignments of the agents with which it can communicate and the scheduled times of the tasks assigned to those agents. The minimum-time arbitrarily constrained distributed scheduling problem is for the agents to use this local knowledge to find a schedule that satisfies all constraints and minimizes the time needed to complete all tasks. The optimal distributed nonsequential backtracking algorithm is able to find minimum-time schedules that satisfy all constraints. This algorithm is designed to work concurrently with a distributed assignment algorithm that satisfies certain communication requirements detailed in this paper. This paper presents proof of the algorithm's correctness, completeness, and optimality.

Wireless Sensor Networks (WSN) perform collaborative distributed sensing of a moving object in an environment for the purpose of classification. This requires collecting measurements from as many sensors as possible to classify the object. However, there is a tradeoff between the value of information contained in a distributed set of measurements and the energy cost of acquiring these measurements, fusing them into a belief and transmitting the updated belief. To manage this tradeoff, sensor selection schemes are used. In this paper, the sensor selection scheme proceeds as a sequence of rounds as follows. The network chooses an active set of sensors at each round based on physical proximity to the object. From this set, one sensor is selected by bidding to take a measurement. This measurement is transmitted to the sensor selected at the next round. The main contribution is to dynamically optimize the classification performance for a given cost of sensing, communication and computation. The method is illustrated by an example.