Preferential Multi-Target Search in Indoor Environments using Semantic SLAM

In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearchComment: 6 pages, 8 figure

An object-oriented navigation strategy for service robots leveraging semantic information

Simultaneous localization and mapping (SLAM) have been an essential requirement for the autonomous operation of mobile robots for over a decade. However, in the wake of recent developments and successes of deep neural networks and machine learning, the conventional task of SLAM is gradually being replaced by Semantic SLAM. Extracting semantic information (such as object information) from sensory data can enable the robot to distinguish different environmental regions beyond the conventional grid assignments of free and occupied. This level of scene awareness is essential for performing higher-level navigation and manipulation tasks and enhancing human-robot interactions. This paper presents an integrated framework that not only builds such maps of indoor environments but also facilitates the execution of ‘Go to object’ tasks with high-level user input. We also present a method to extract meaningful endpoints of navigation based on object class. Our modular stack leverages well-known object detectors (YOLOv3), RGB-D SLAM techniques (RTABMapping) and local navigation planners (TEB) to perform ObjectGoal navigation tasks. We also validate the results of experiments in real environments

E Understanding the Stability of Mixed-Anion Deposits and Effects on Reactions with Advanced Coating Materials

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Idiopathic extensive spontaneous venous thrombosis (a case report).

Two patients with extensive spontaneous venous thrombosis are reported. Both had documented evidence of polyserositis, transient abnormalities of liver function tests along with normal coagulograms. Although one patient had a short, self-limiting illness, the other required treatment with coumarin derivatives. The relevant literature is discussed

Analysis of VANET Protocols for Urban and Rural Area Using QualNet Simulator

Vehicular Ad-hoc Network (VANET) a type of a wireless network is utilized for the cause of communication amid the vehicles and the Road Side Units (RSU). Essential applications of VANET are in Intelligent Transport System (ITS) that embraces safety-related services. VANET is a subset of Mobile Ad-hoc Network (MANET) which provides dynamic changes in topology and mobility along with high speed. VANET performs the network operation with greater efficiency and is highly reliable. The performance of VANET is tested with different protocols in urban and rural areas.  The scenarios for urban and rural areas are different. In this work, protocols considered are Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR) and Dynamic MANET On-demand (DYMO). The simulation of urban and rural areas is done considering the parameters: throughput, end to end delay, jitter, and messages received using QualNet. The analysis of different protocols with these parameters is carried out for urban and rural areas; it is found that DSR protocol is best suitable for Urban as well as Rural areas