Recent times are witnessing an emergence of sites that are hazardous for human access. This has created a global demand to equip agents with the ability to autonomously inspect such environments by computing a coverage path effectively and efficiently. However, inspection of such sites requires agents to consider the correlation of work, providing precedence provision in visiting regions. The current approaches to compute coverage path in the hazardous sites, however, do not consider precedence provision. To this end, coverage path planning strategies are proposed, which provide precedence provision. To meet the challenges, the problem is divided into two phases: inter-region and intra-region path planning. In the ‘inter-region’ path planning of the approach, the site comprising of multiple disjoint regions is modelled as connectivity graph. Two novel approaches, Mixed Integer Linear Programming (MILP) solution and heuristic based techniques, are proposed to generate the ordered sequence of regions to be traversed. In the ‘intra-region’ path planning of the approach, each region is decomposed into a grid and Boustrophedon Motion is planned over each region. The ability of combined approach to provide complete coverage is proved under minor assumption. An investigative study has been conducted to elucidate the efficiency of the proposed approach in different scenarios using simulation experiments. The proposed approach is evaluated against baseline approaches. The results manifest a significant reduction in cost and execution time, which caters to inspection of target sites comprising of multiple disjoint regions with precedence provision.

Growing population and migration to cities have given birth to multiple urban issues. Traffic congestion is one of the most prominent ones with severe side effects like fuel wastage, loss of lives, and slow productivity. The traditional traffic control system deploys programming logic control (PLC) which uses round-robin scheduling algorithm. However, few recent works have proposed IoT-based framework which requires the deployment of a series of sensors. In this paper, we propose an IoT-based framework that uses the existing network of CCTV cameras at the junction. An edge device is used to estimate the traffic density and detect emergency vehicles using YOLO v3 -Efficient Net. These two parameters are used as an input to a novel traffic control algorithm. The performance of the proposed framework has been evaluated by analyzing its properties using the UA-DETRAC dataset. The proposed framework achieves 68.10% vehicle detection accuracy.

Recent times are witnessing the emergence of indoor sites with extenuating circumstances that place a strict time constraint on mobile robots to reach a target while covering a given area. This has created a global demand to equip mobile robots with the ability to autonomously plan a coverage path to reach the static target effectively and efficiently. The current approaches to achieve such tasks, however, are either time-consuming or human-operator dependent. To this end, an offline-online strategy is proposed to meet the speeding-up challenge by efficiently modelling the environment using a priori information. In the ‘offline’ stage of the strategy, the layout of the environment is segmented into a set of regions. The corners and dead-ends are identified based on the spatial mobility of the regions. The global path is then computed by deriving a graph-structured, road map using the segmented regions. In the ‘online’ stage, the global path is traversed by selecting frontiers which concurrently minimizes the covered area and time. In case the path is obstructed, a re-planning strategy is deployed. The proposed strategy is evaluated by various experiments against two baseline search approaches in three simulated environments. The results manifest a significant reduction in time to reach the goal and coverage area which caters to the strict time constraint for mobile robots.