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The motion control of unmanned ground vehicles (UGV) is a challenge in the industry of automation. The purpose of this paper is to propose a fuzzy inference system (FIS) based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.

The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the FIS, which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.

Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS. The reported simulation results are obtained using MATLAB software package. The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that is populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.

The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions. The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles. However, the methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.

This paper presents the design of a FIS and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. This facilitates an improved functionality of the operation of UGV.

The research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This paper aims to focus on the UAV/UGV cooperative target tracking and enclosing, considering the limits of detection and sensor failures.

The UAV/UGV cooperation structure is designed, contributing to homogeneous consistency and heterogeneous communication. The target tracking of UAVs is converted into a constraint optimization problem involving tracking cost, and the target enclosing of UGVs is modeled as formation control.

The energy estimation pigeon-inspired optimization is developed to generate control inputs for UAVs. And the controller combined with switchable topology is proposed, where the switching rule is flexible in dealing with some emergencies.

The proposed structure and algorithms can be easily applied to practice and help design the UAV/UGV control system.

The energy estimation mechanism is proposed for the target tracking of UAVs, and the rules of switching topologies ensure the target enclosing process of UGVs.

To demonstrate proof‐of‐concept of the Griffon man‐portable hybrid unmanned ground vehicle/unmanned aerial vehicle (UGV/UAV) based on the iRobot PackBot we developed the Griffon air mobility system consisting of a gasoline‐powered propeller engine, a steerable parafoil, and a radio‐controlled servo system. We integrated the AMS with a PackBot prototype, and we conducted ground and flight tests to validate this concept. The Griffon prototype was capable of remote‐controlled flight, take‐off, and landing. The Griffon achieved speeds of over 20 mph and altitudes of up to 200 feet. We demonstrated the feasibility of developing a man‐portable hybrid UGV/UAV. Future work may explore the possibilities for teleoperated, semi‐autonomous, and fully autonomous control using the Griffon concept. The parafoil wing limits the usability of this vehicle in windy conditions, but this could be addressed using a lightweight fixed wing instead. Man‐portable hybrid UGV/UAVs may be used by the military to perform reconnaissance and strike missions in urban environments, and by civilian teams to conduct search‐and‐rescue operations in hazardous terrain. This research provides the first demonstration of a man‐portable unmanned vehicle capable of both flight and ground locomotion, and it does so using a combat‐tested UGV platform.

Heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking and exploration. Exploration missions with heterogeneous robot teams (HeRTs) should acquire a common map for understanding the surroundings better. The purpose of this paper is to provide a unique approach with cooperative use of agents that provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, this method is suitable for real-time applications and autonomous path planning for exploration.

Lidar odometry and mapping and various similarity metrics such as Shannon entropy, Kullback–Leibler divergence, Jeffrey divergence, K divergence, Topsoe divergence, Jensen–Shannon divergence and Jensen divergence are used to construct a common height map of the environment. Furthermore, the authors presented the layering method that provides more accuracy and a better understanding of the common map.

In summary, with the experiments, the authors observed features located beneath the trees or the roofed top areas and above them without any need for global positioning system signal. Additionally, a more effective common map that enables planning trajectories for both vehicles is obtained with the determined similarity metric and the layering method.

In this study, the authors present a unique solution that implements various entropy-based similarity metrics with the aim of constructing common maps of the environment with HeRTs. To create common maps, Shannon entropy–based similarity metrics can be used, as it is the only one that holds the chain rule of conditional probability precisely. Seven distinct similarity metrics are compared, and the most effective one is chosen for getting a more comprehensive and valid common map. Moreover, different from all the studies in literature, the layering method is used to compute the similarities of each local map obtained by a HeRT. This method also provides the accuracy of the merged common map, as robots’ sight of view prevents the same observations of the environment in features such as a roofed area or trees. This novel approach can also be used in global positioning system-denied and closed environments. The results are verified with experiments.

This paper aims to provide an overview of robots presently in use by the military and an insight into some that are under development.

Following a short introduction, this paper first considers existing applications of robots in the military field, including details of Russian weaponised ground robots. It then highlights a range of military robot developments and concludes with a brief discussion.

Drones (unmanned aerial vehicles) and small unmanned ground vehicles (UGVs) are among the most widely used robots by the military. Russia is developing a growing armoury of heavily weaponised UGVs, some of which were recently deployed in Syria. Some topics of development include humanoid robots, powered exoskeletons, load-carrying robots, micro-air vehicles and autonomous land vehicles. Robots will play an ever-growing role in military actions, and while some developments offer longer-term prospects, others are expected to be deployed in the near future.

Robots are playing an increasingly important role in military conflicts, and this provides details of present-day and anticipated future uses of robots by the military.

The purpose of this paper is to provide a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, industry engineer-turned entrepreneur regarding his pioneering efforts in bringing a robotic invention to market. This paper aims to discuss these issues.

The interviewee is Geoff Howe, Senior Vice President of Howe & Howe, Inc., a subsidiary of Textron Systems and a leader in advanced robotic platform solutions and applications built and proven for the most extreme conditions in the world. Geoff and Michael Howe founded Howe & Howe Technologies in 2001 and was acquired by Textron Systems in 2018. In 2010, Howe and Howe developed one of the world’s first robotic fire-fighting solutions. Geoff Howe describes the evolution of the Thermite robotic firefighter’s commercial development, along with the challenges of breaking ground in this new industry.

Geoff and his identical twin brother, Michael Howe, are inventors, military contractors, actors and entrepreneurial businessmen famous for their philanthropic drive to give back to their community. When Geoff and Mike were just six years old, they were known as “Howe and Howe Construction.” At the age of eight, Mike and Geoff built their own one room log cabin with the power tools their mom had given them for their birthday. At 16 years old, they started tinkering with vehicles before they even had their drivers’ licenses. They both graduated from Maine high school and colleges with honors. The company’s portfolio includes the RIPSAW® , Thermite, the Badger, Subterranean Rover and other extreme vehicles used for numerous applications. In 2010, Howe and Howe completed three new vehicles. First was the Thermite™ which entered the unmanned ground vehicle (UGV) market as the USA’s first firefighting UGV. The second vehicle was Ripchair™, the development of an off-road wheelchair for those that have become disabled and are unable to walk. The third vehicle was Riptide, the amphibious version of the RIPSAW. Year 2015 saw the commercial development of the Big Dog Extreme 4x4 fire truck and the Thermite RS1 and RS3 firefighting robots. The Big Dog is an off-road truck and also serves as an all-terrain multi-use firetruck. The Thermite provides firefighters and first responders immediate eyes inside the fire as well the ability to safely attack industrial, chemical and HAZMAT fires from their core. The Thermite robot provides safety and inside access on containing and defeating fires of any magnitude.

Howe & Howe Technologies first gained notoriety in 2001, with the development of the world’s fastest tank, the RIPSAW. Successful demonstrations soon followed, which eventually allowed the Howes, at the age of 31, to be named among the youngest in history to ever receive a multi-million dollar military contract from the USA. Soon after, in 2010, Howe & Howe received a Guinness World Record for developing the world’s smallest armored vehicle, the Badger. By the time the Howes were 36, they had one world record, multiple patents pending for their product developments, as well as military contracts. The Howes also had their own reality television show on a major US network. In 2010, they completed the Thermite, Fire Fighting Unmanned Ground Vehicle. In 2012, the Howes founded “Outdoors Again,” a nonprofit 501c3 organization that holds outdoor events and social activities for those who require the use of a wheelchair.

In relation to rapid development of possible applications of unmanned vehicles, new opportunities for their use are emerging. Among the most dynamic, we can distinguish package shipments, rescue and military applications, autonomous flights and unattended transportation. However, most of the UAV solutions have limitations related to their power supplies and the field of operation. Some of these restrictions can be overcome by implementing the cooperation between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The purpose of this paper is to explore the problem of sensor fusion for autonomous landing of a UAV on the UGV by comparing the performance of precision landing algorithms using different sensor fusions to have precise and reliable information about the position and velocity.

The difficulties in this scenario, among others, are different coordination systems and necessity for sensor data from air and ground. The most suitable solution seems to be the use of widely available Global Navigational Satellite System (GNSS) receivers. Unfortunately, the position measurements obtained from cheap receivers are encumbered with errors when desiring precision. The different approaches are based on the usage of sensor fusion of Inertial Navigation System and image processing. However most of these systems are very vulnerable to lightning.

In this paper, methods based on an exchange of telemetry data and sensor fusion of GNSS, infrared markers detection and others are used. Different methods are compared.

The subject of sensor fusion and high-precision measurements in reference to the autonomous vehicle cooperation is very important because of the increasing popularity of these vehicles. The proposed solution is efficient to perform autonomous landing of UAV on the UGV.

The purpose of this paper is to study the path-planning problem of an unmanned ground vehicle (UGV) in a predefined, structured environment.

In this investigation, the environment chosen was the roadmap of the National Institute of Technology, Rourkela, obtained from Google maps as reference. An UGV is developed and programmed so as to move autonomously from an indicated source location to the defined destination in the given map following the most optimal path.

An algorithm based on linear search is implemented to the autonomous robot to generate shortest paths in the environment. The developed algorithm is verified with the simulations as well as in experimental environments.

Unlike the past methodologies, the current investigation deals with the global path-planning strategy as the line following mechanism. Moreover, the proposed technique has been implemented in a real-time environment.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

The great success of unmanned aerial vehicles (UAVs) in performing near-real time tactical, reconnaissance, intelligence, surveillance and other various missions has attracted broad attention from military and civilian communities. A critical contribution to the increase and extension of UAV applications, resides in the separation of pilot and vehicle allowing the operator to avoid dangerous and harmful situations. However, this apparent benefit has the potential to lead to problems when the role of humans in remotely operating “unmanned” vehicles is not considered. Although, UAVs do not carry humans onboard, they do require human control and maintenance. To control UAVs, skilled and coordinated work of operators on the ground is required.