The Impact of Mapping Algorithms on Mobile Robot CPU Temperature and Computational Performance

This paper describes the methodology used to develop a communication platform that enhances interoperability between different types of components irrespective of the manufacturers and of the software platform. This framework is intended to be used on a distributed system where software and hardware modules are designed to control an autonomous Unmanned Ground Vehicle (UGV). A messaging architecture based on the Joint Architecture for Unmanned Systems (JAUS) was developed in Node.js to ensure platform independence. It was deployed on hardware platforms such as the Raspberry Pi and the BeagleBone Black in order to access various sensors on the platform and control hardware like stepper motor. This messaging architecture can also be implemented on conventional laptops running Windows operating system or Linux that run algorithms for localisation, terrain mapping and path planning. Initially regarded as a very limited language, JavaScript's true nature and power have only recently been appreciated in depth, A major move is now underway to apply this language in new and fascinating contexts. The ultimate goal of the framework was to structure communication and inter-operation of UGV components and a sensor suite within a network. The framework was implemented on the G-Bat, a UGV platform developed at CSIR DPSS Landward Sciences to test and simulate the communication part of an autonomous navigation system. The test was a successful step that paves the way for a more robust implementation of the framework in the future work.

The use of unmanned ground vehicles (mobile robots) and unmanned aerial vehicles (drones) in the civil infrastructure asset management sector: Applications, robotic platforms, sensors, and algorithms

Unmanned vehicles perform critical mission functions. Today, fielded unmanned vehicles have restricted operations as a single asset controlled by a single operator. In the future, however, it is envisioned that multiple unmanned air, ground, surface and underwater vehicles will be deployed in an integrated unmanned (and "manned") team fashion in order to more effectively execute complex mission scenarios. To successfully facilitate this transition from single platforms to an integrated unmanned system concept, it is essential to first develop the required base technologies for multi-vehicle mission requirements, as well as test and evaluate such technologies in tightly controlled field experiments. Under such conditions, advances in unmanned technologies and associated system configurations can be empirically evaluated and quantitatively measured against relevant performance metrics. A series of field experiments will be conducted for unmanned force protection system applications. A basic teaming scenario is: Unmanned aerial vehicles (UAVs) detect a target of interest on the ground; the UAVs cue unmanned ground vehicles (UGVs) to the area; the UGVs provide on-ground evaluation and assessment; and the team of UAVs and UGVs execute the appropriate level of response. This paper details the scenarios and the technology enablers for experimentation using unmanned protection systems.

Purpose The unmanned ground vehicle (UGV) described in this manuscript is a robot designed by the authors to map the underground mine environments. The UGV works to develop a computational intelligence-based cyber-physical system (CPS)-based analytical framework for mining operations. The UGV demonstrated excellent semi-autonomous navigation capabilities in the absence of GNSS signals. The UGV has a suite that works in unison to provide relevant information. These sensors are integrated to form a robust sensor fusion-based architecture, creating a CPS with a wide range of capabilities such as data acquisition and navigation in challenging underground environments. UGVs can be used to enhance the efficacy of safety inspections, rescue during underground emergencies and assist miners in hazardous conditions. Design/methodology/approach In this research, an UGV was constructed whose operations are enabled by sensors including a D415i Red Blue Green (RGB) depth camera, a LiDAR, a FLIR C5 infrared camera and smart air quality sensors. This sensor fusion-based architecture forms a CPS. Data obtained remotely are processed by deep learning algorithms to achieve overall capabilities such as real-time image analysis for miner identification, object detection, posture analysis and identifying threats of roof falls and overhangs. Simultaneous localization and mapping (SLAM) algorithms create a 3D map, facilitate autonomous navigation and build a decision support system for delivering mine rescue support. Findings The aim of this study is to include this capacity in training situations when it has been validated and authorized by the Directorate General of Mines Safety (DGMS) Indian government regulatory agency for safety in mines and oil fields. The longwall demo mine, at IIT (ISM) is being used as the site of the first operations. Once approved by the respective enforcement agencies, this technology and the accompanying rescue and training process can be used in underground operations. Originality/value In fact, this paper is the first attempt at remotely operated UGVs based on CPSs, the CPS–UGV in Indian mine conditions, so as to revolutionize Indian mines based on the idea of Industry 4.0.

This paper presents the work being conducted at Cal Poly Pomona on the collaboration between unmanned aerial and ground vehicles for precision agriculture. The unmanned aerial vehicles (UAVs), equipped with multispectral/hyperspectral cameras and RGB cameras, take images of the crops while flying autonomously. The images are post processed or can be processed onboard. The processed images are used in the detection of unhealthy plants. Aerial data can be used by the UAVs and unmanned ground vehicles (UGVs) for various purposes including care of crops, harvest estimation, etc. The images can also be useful for optimized harvesting by isolating low yielding plants. These vehicles can be operated autonomously with limited or no human intervention, thereby reducing cost and limiting human exposure to agricultural chemicals. The paper discuss the autonomous UAV and UGV platforms used for the research, sensor integration, and experimental testing. Methods for ground truthing the results obtained from the UAVs will be used. The paper will also discuss equipping the UGV with a robotic arm for removing the unhealthy plants and/or weeds.

The research potential in the field of mobile mapping technologies is often hindered by several constraints. These include the need for costly hardware to collect data, limited access to target sites with specific environmental conditions or the collection of ground truth data for a quantitative evaluation of the developed solutions. To address these challenges, the research community has often prepared open datasets suitable for developments and testing. However, the availability of datasets that encompass truly demanding mixed indoor–outdoor and subterranean conditions, acquired with diverse but synchronized sensors, is currently limited. To alleviate this issue, we propose the MIN3D dataset (MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications) which includes data gathered using a wheeled mobile robot in two distinct locations: (i) textureless dark corridors and outside parts of a university campus and (ii) tunnels of an underground WW2 site in Walim (Poland). MIN3D comprises around 150 GB of raw data, including images captured by multiple co-calibrated monocular, stereo and thermal cameras, two LiDAR sensors and three inertial measurement units. Reliable ground truth (GT) point clouds were collected using a survey-grade terrestrial laser scanner. By openly sharing this dataset, we aim to support the efforts of the scientific community in developing robust methods for navigation and mapping in challenging underground conditions. In the paper, we describe the collected data and provide an initial accuracy assessment of some visual- and LiDAR-based simultaneous localization and mapping (SLAM) algorithms for selected sequences. Encountered problems, open research questions and areas that could benefit from utilizing our dataset are discussed. Data are available at https://3dom.fbk.eu/benchmarks.

Mobile microgrid formation using multiple Unmanned Ground Vehicles (UGV) can establish surface power sources and integrate mission infrastructure autonomously. Advancement of this technology is martialed by the expedited Artemis mission to return to the Moon by 2024, with residence there by 2028. Many of the infrastructure deployment tasks to support this mission will depend on advance assembly by robotic agents. Consider the deployment of multiple Kilopower reactors to supply power to a lunar habitation module, with UGVs to position the reactors and connect power grid elements with cables. To accomplish this complex task, a number of robotic controllers have been integrated on all-terrain UGVs. These controllers comprise waypoint navigation, visual docking, electrical connection coupling, and electrical cable deployment. Hardware which supports the mission includes the UGV platform, odometry and perception sensors, distributed communications, and electrical grid infrastructure. Robot Operating System (ROS) provides a core development architecture. Subsystem tests in an unstructured terrestrial environment have been performed, with a full system test including multiple UGVs planned. This work reports on the current operating capability of subsystems in an outdoor terrestrial environment, and suggests the system-level validation test objectives for power grid formation.

The coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is a proactive research topic whose great value of application has attracted vast attention. This paper outlines the motivations for studying the cooperative control of UAVs and UGVs, and attempts to make a comprehensive investigation and analysis on recent research in this field. First, a taxonomy for classification of existing unmanned aerial and ground vehicles systems (UAGVSs) is proposed, and a generalized optimization framework is developed to allow the decision-making problems for different types of UAGVSs to be described in a unified way. By following the proposed taxonomy, we show how different types of UAGVSs can be built to realize the goal of a common task, that is target tracking, and how optimization problems can be formulated for a UAGVS to perform specific tasks. This paper presents an optimization perspective to model and analyze different types of UAGVSs, and serves as a guidance and reference for developing UAGVSs.

Today’s military intelligence, surveillance, and reconnaissance (ISR) missions routinely employ unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) as versatile, real-time, remote sensing platforms. One of the main challenges of deploying such unmanned systems for large-scale military operations is the lack of interoperability among different types of unmanned systems. This constraint introduces even greater problems as we move toward unmanned missions that require multiple platforms to work cooperatively, even in environments where human operators cannot readily provide any mission inputs. If we expect cooperative unmanned systems technologies to advance rapidly in the future, it is essential that we, the unmanned systems community, remove this interoperability constraint. To this end, we developed a scalable, onboard unmanned system called cooperative autonomous system (CAS) capable of mounting on multiple vehicle types to address key aspects of the interoperability problem. The CAS includes a comprehensive collection of hardware and software modules designed for real-time coordination of unmanned aircraft and autonomous ground vehicles. These modules are essential capabilities to perform cooperative missions: to navigate and control the platform, to communicate with other unmanned systems, to process information transmitted by neighboring unmanned systems, and to fuse data collected from other unmanned systems with its local sensor data. We created CAS interfaces for several commercially available autopilots, mobile robot platforms, and industry-standard sensor and communication devices. The CAS software is based on an open-source, multi-threaded, event-driven framework that combines cross-platform compatibility with object-oriented software that encapsulates a variety of asynchronous control, sensing, and communication mechanisms. The hardware of CAS is made of modular units to allow a plug-and-play ability, depending on a particular mission application. We successfully demonstrated the interoperability of CAS by implementing the system on two different unmanned aircraft and an unmanned mobile ground platform. We show results from an ISR experiment with three different mobile platforms and a set of stationary sensors. Sensors employed were electro-optical and infrared cameras as well as radio frequency receivers.

The use of Unmanned Arial Vehicles (UAVs) is becoming increasingly popular due to a variety of applications such as security, delivery, search, and recovery. The limited battery capacity of the UAVs becomes one of the most crucial problems which calls for a solution of automatic recharging and battery optimization to extend the UAVs’ operation time. This paper provides the design and implementation of a UAGVR (Unmanned Aerial and Ground Vehicle Recharging) system which allows the UAV to receive on-demand wireless recharging from an unmanned ground vehicle (UGV). An object tracking camera on the UGV allows it to automatically maneuver towards the UAV to accept an autonomous landing and recharging. We present a cooperative mission planning algorithm for the UAGVR system. A proof of concept system is developed using a small ground vehicle and a miniature quadcopter. Experimental results confirm the validity of our system design and suggest future work.

Foster-Miller's unmanned ground vehicle, TALON, was originally developed under DARPA's Tactical Mobile Robotics (TMR) program. TALON has evolved over the years and has proven to be a robust, mobile, universal platform. As a result of the advances made in the evolution of TALON, new and far-reaching opportunities have been realized for unmanned ground vehicles. In recent conflicts such as in Afghanistan and Iraq, unmanned systems have played an important role and have extended the reach and capabilities of the War fighter. Technological advances have transformed unmanned vehicles in to useful tools and in some cases are used in lieu of sending in a soldier. Unmanned ground vehicles have seen recent and persistent success, as shown in theater, in the explosive ordinance disposal (EOD) and improvised ordinance disposal (IED) missions. Foster-Miller's TALON has experienced over ten thousand EOD and IED missions in Iraq alone. The success of the unmanned system has resulted in the doctrine Send the robot in first. Foster-Miller has taken the role of the unmanned vehicle in yet another direction. Foster-Miller has transformed the TALON from a practical to tactical system. Through the combined efforts of Foster-Miller and the US Army, TALON has been involved in a weaponization program. To date, Foster-Miller has outfitted the TALON with 11 systems. As one can see, the unmanned ground vehicle is much more than a mobility platform.

Unmanned vehicles (UxV) operate in numerous environments, with air, ground and marine representing the majority of the implementations. All unmanned vehicles, when traversing unknown space, have similar requirements. They must sense their environment, create a world representation, and, then plan a path that safely avoids obstacles and hazards. Traditionally, each unmanned vehicle class used environment specific assumptions to create a unique world representation that was tailored to it operating environment. Thus, an unmanned aerial vehicle (UAV) used the simplest possible world representation, where all space above the ground plane was free of obstacles. Conversely, an unmanned ground vehicle (UGV) required a world representation that was suitable to its complex and unstructured environment. Such a clear cut differentiation between UAV and UGV environments is no longer valid as UAVs have migrated down to elevations where terrestrial structures are located. Thus, the operating environment for a low flying UAV contains similarities to the environments experienced by UGVs. As a result, the world representation techniques and algorithms developed for UGVs are now applicable to UAVs, since low flying UAVs must sense and represent its world in order to avoid obstacles. Defence R&D Canada (DRDC) conducts research and development in both the UGV and UAV fields. Researchers have developed a platform neutral world representation, based upon a uniform 2¹/₂-D elevation grid, that is applicable to many UxV classes, including aerial and ground vehicles. This paper describes DRDC's generic world representation, known as the Global Terrain map, and provides an example of unmanned ground vehicle implementation, along with details of it applicability to aerial vehicles.

The ability to move from one point to the destination point autonomously is very necessary in AMR robots, to be able to meet this, the robot must be able to detect the surrounding environment and know its location to the environment, the Hector SLAM algorithm is added using the LIDAR sensor, and to find out the ability of the LIDAR sensor with the Hector SLAM and computer specifications in order to process properly, a simulation of the HECTOR SLAM with the LIDAR sensor was made. Simulation is carried out by creating an environment map on the Gazebo. Then explore environmental mapping using Hokuyo LIDAR which has been added to the turtlebot3 model waffle_pi to the simulated environment map. In this study, a model of the second floor lobby environment and Brail of the Batam State Polytechnic was used which was made in the form of a simulation on the Gazebo, where robots that have used LIDAR will be controlled with a keyboard around the simulation environment, where simultaneously the mapping and localization process runs and the process can be seen on the Rviz in real-time, where LIDAR will send data in the form of distance readings that will be received by Hector SLAM. The results of this study are expected that Hector SLAM using LIDAR sensor simulation can produce environmental mapping and localization in the simulation environment and obtain a minimum computer specification to process data from the SLAM Hector process using LIDAR sensors.

In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of the proposed system and landing scheme.

At present, there is a situation when the practice of creating modern devices, units and systems of ground unmanned vehicles (GUV) has preceded the theory of information analysis and synthesis of complex systems. Some existing solutions for the information support of the GUV need to be generalized, standardized and unified, the definition of new special requirements for the creation of computer systems and networks in transport should be made. Therefore, it is necessary and actual to develop information and communication technology for intelligent control of multi-purpose GUV. Goal. The purpose of the article is to develop information and communication technology for intelligent control of multi-purpose GUV, which work in conditions of intensive loads, difficult operating conditions, increased responsibility of mechanisms, resulting in reduced human losses and energy consumption, increased machine reliability and control accuracy. Methodology. The methodology of tasks is based on the use of a synergetic approach to the creation of information and communication technology for intelligent control of multi-purpose GUV, as the development of a systematic approach taking into account the adaptation and integration of the vehicle to transport infrastructure and/or rough terrain. The principles of synergetics underlie the construction of mechatronic systems – a combination in one unit of components of different technical nature (mechanical, electrical, computer), which adaptively interact with the external environment as a single functional and structural organism. The synergetic approach deals with phenomena and processes, as a result of which the system – as a whole – may have properties that none of the parts has. Results. The concept of intelligent control of multi-purpose GUV on the basis of artificial hybrid neuro-phase regulators with the use of cloud computing services and deep learning technology is proposed, substantiated and implemented, which allows to qualitatively increase the efficiency of both one vehicle and the transport system on the base combining a synergetic approach and evolutionary methods of learning multilayer artificial neural networks by objectively forming the architecture of these networks on the basis of learning functionalities and appropriate control objectives. Originality. Use of cloud computing services based on deep learning for intelligent control technology of GUV. Practical value. The proposed information and communication technology of intelligent control of multi-purpose GUV will significantly reduce energy consumption, psychophysical stress on crew members, as well as increase the accuracy of location.