Obemannade markfarkoster är ännu ganska ovanliga i den svenska Försvarsmakten men borde kunna bli allt vanligare. UGV:er används traditionellt till att utföra smutsiga, tråkiga och farliga arbetsuppgifter. Kan de då vara användbara i Afghanistan mot den motståndare som FM möter där idag? I studien undersöker författaren möjligheterna för obemannade markfarkoster att bidra med militär nytta inom området logistiktransporter. De obemannade transportfordonen kan från grunden utgöras av standardlastbilar i FM som har utrustats med så kallade UGV-kit. Dessa UGV-kit har till uppgift att göra standardfordonen fjärrstyrda, autonoma eller både och. Samma princip gäller för eskortfordonen som följer med till stöd för logistiktransporten, en Galt ska exempelvis kunna agera UGV. Den irreguljära och lågteknologiska motståndaren använder ofta IED:er vid eldöverfall vilket har gjort landsvägstransporter till riskfylld verksamhet för personal ute på vägarna. Personalsäkerhet är prioriterad verksamhet i Försvarsmakten och författarens antagande är att UGV:er kan bidra till att göra logistiktransporter och eskortförfaranden till mindre riskabel verksamhet. / Unmanned ground vehicles are still quite rare within in the Swedish Armed Forces but they should become more common. UGV´s are used traditionally for performing dirty, dull and dangerous tasks. Could they also be usefull against the enemy in Afghanistan that the Swedish Armed Forces are confronting there today? In this study the author investigates the possibilities for unmanned ground vehicles to contribute with military benefits to the area of logistics transportation. The unmanned transport vehicles can be ordinary standard trucks from the beginning which have been equipped with a so called UGV-kit. This UGV-kit´s task is to make the standard vehicles remotely controlled, autonomous or both. The same principle applies to the escorting vehicles, a Galt should for example also be able to act as a UGV. The irregular and low technology enemy often uses IED’s when attacking, which have made road transportation to hazardous activities for the personnel on the road. Personnel safety are prioritized activity within the Swedish Armed Forces, and the authors assumtion is that UGV’s can help making logistics transportation and escorting procedures in to less risky activities.

Unmanned ground vehicles (UGVs) have been widely used in many areas such as agriculture, mining, construction and military applications. This results from the fact that UGVs can not only be easily built and controlled, but also be featured with high mobility and handling hazardous situations in complex environments. Among the competences of UGVs, autonomous navigation is one of the most challenging problems. This is because that the success in achieving autonomous navigation depends on four factors: Perception, localization, cognition, and proper motion controller. In this thesis, we introduce the realization of autonomous navigation for a two-wheeled differential ground robot under the robot operating system (ROS) environment from both the simulation and experimental perspectives. In Chapter 2, the simulation work is discussed. Firstly, the robot model is described in the unified robot description format (URDF)-based form and the working environment for the robot is simulated. Then we use the \textit{gmapping} package which is one of the packages integrating simultaneous localization and mapping (SLAM) algorithm to build the map of the working environment. In addition, ROS packages including \textit{tf}, \textit{move\_base}, \textit{amcl}, etc., are used to realize the autonomous navigation. Finally, simulation results show the feasibility and effectiveness of the autonomous navigation system for the two-wheeled UGV with the ability to avoid collisions with obstacles. In Chapter 3, we introduce the experimental studies of implementing autonomous navigation for a two-wheeled UGV. The necessary hardware peripherals on the UGV to achieve autonomous navigation are given. The process of implementation in the experiment is similar to that in simulation, however, calibration of several devices is necessary to adapt the scenario in a practical environment. Additionally, a proportional-integral-derivative (PID) controller for the robot base is used to handle the external noise during the experiment. The experimental results demonstrate the success in the implementation of autonomous navigation for the UGV in practice. / Graduate

This work presents a framework for the exploration and path planning for a collaborative UAV and UGV system. The system is composed of a UAV with a stereo system for obstacle detection and a UGV with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the UAV. Both identify frontiers for exploration with the Dijkstra Frontier method using Dijkstra's Algorithm to identify a frontier with unknown space, and the other uses a bi-directional RRT to identify multiple frontiers for selection. The final algorithm developed was for to give the UGV partial plans when an entire plan is not yet found. This improves the overall mission tempo. The algorithm is designed to keep the UGV a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in Gazebo using the ROS framework. The Dijkstra Frontier method was also tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The UGV partial plan method showed a decreased traveled distance for the UGV but increases in UGV mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions. / Master of Science / This work presents a framework for the exploration and path planning for a collaborative aerial and ground vehicle robotic system. The system is composed of an aircraft with a camera system for obstacle detection and a ground vehicle with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the aircraft. Both identify frontiers for exploration with the Dijkstra Frontier method using path planning algorithms to identify a frontier with unknown space (Dijkstra Frontier), and the other uses a sampling based path planning method (RRT Explore) to identify multiple frontiers for selection. The final algorithm developed was for to give the ground vehicle intermediate plans when an entire plan is not yet found. The algorithm is designed to keep the ground vehicle a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in a simulation framework using Robot Operating System and one exploration method was tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The ground vehicle intermediate plan method showed a decreased traveled distance for the ground vehicle but increases in ground vehicle mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions.

One of the problems affecting robot operators' spatial awareness involves their ability to infer a robot's location based on the views from on-board cameras and other electro-optic systems. To understand the vehicle's location, operators typically need to translate images from a vehicle's camera into some other coordinates, such as a location on a map. This translation requires operators to relate the view by mentally rotating it along a number of axes, a task that is both attention-demanding and workload-intensive, and one that is likely affected by individual differences in operator spatial abilities. Because building and maintaining spatial awareness is attention-demanding and workload-intensive, any variable that changes operator workload and attention should be investigated for its effects on operator spatial awareness. One of these variables is the use of automation (i.e., assigning functions to the robot). According to Malleable Attentional Resource Theory (MART), variation in workload across levels of automation affects an operator's attentional capacity to process critical cues like those that enable an operator to understand the robot's past, current, and future location. The study reported here focused on performance aspects of human-robot interaction involving ground robots (i.e., unmanned ground vehicles, or UGVs) during reconnaissance tasks. In particular, this study examined how differences in operator spatial ability and in operator workload and attention interacted to affect spatial awareness during human-robot interaction (HRI). Operator spatial abilities were systematically manipulated through the use of mental transformation training. Additionally, operator workload and attention were manipulated via the use of three different levels of automation (i.e., manual control, decision support, and full automation). Operator spatial awareness was measured by the size of errors made by the operators, when they were tasked to infer the robot's location from on-board camera views at three different points in a sequence of robot movements through a simulated military operation in urban terrain (MOUT) environment. The results showed that mental transformation training increased two areas of spatial ability, namely mental rotation and spatial visualization. Further, spatial ability in these two areas predicted performance in vehicle localization during the reconnaissance task. Finally, assistive automation showed a benefit with respect to operator workload, situation awareness, and subsequently performance. Together, the results of the study have implications with respect to the design of robots, function allocation between robots and operators, and training for spatial ability. Future research should investigate the interactive effects on operator spatial awareness of spatial ability, spatial ability training, and other variables affecting operator workload and attention.

Historiskt har soldater till fots burit sin personliga utrustning till fots. Den övriga utrustning som soldaten behövde transporterades ofta i vagnar dragna av olika dragdjur. Då stridstempot och framförallt framryckningshastigheten har ökat, har behovet av att bära med sig all nödvändig utrustning ökat. I takt med att nya system tillförts, har därmed också den burna vikten för den enskilde soldaten ökat. Undersökningen har genomförts som en komparativ studie av olika typer av obemannade markfarkoster, så kallade UGV-system, genom att deras möjligheter och begränsningar har analyserats och jämförts utifrån de krav som ställs av scenariot, och av den militära användaren, vid lösandet av en specifik taktisk uppgift. Kriterierna för jämförelse har med hjälp av konceptet militär nytta, framtaget av Andersson et al (2015), tagits fram ur scenariot. Studien kan konstatera att den militära nyttan med dessa UGV-system är att soldaternas egen rörlighet och uthållighet ökar, samtidigt som den skaderisk som tunga bördor innebär minskar. En soldat som inte är utmattad efter att ha burit tung utrustning har en högre stridsberedskap och agerar med större skärpa. Förbandens operativa rörlighet och uthållighet ökar också och beroende på vilket UGV-system som används återfinns olika grader av militär nytta. / Historically, soldiers on foot have carried their personal equipment. Other equipment that the soldier needed was often transported in wagons drawn by different beasts of burden. As the high operational tempo, and above all, forward speed has increased, the need to carry all necessary equipment also has grown. And, as new systems have been added, the load to carry for the individual soldier has thus increased. The survey has been conducted as a comparative study of different types of unmanned ground vehicles, so-called UGV systems, by analyzing their possibilities and limitations based on the requirements of the scenario and also the requirements by the military user in solving a specific tactical task. The criteria for comparison have been developed from the scenario using the concept of military utility, developed by Andersson et al. (2015). The study concludes that the military benefits connected to the UGV systems are that the individual mobility and endurance of the soldiers increases, and that the risk of injuries from carrying heavy loads decreases. A soldier not exhausted from carrying heavy loads has a higher combat preparedness and acts with greater focus. The operational mobility and endurance of the unit also increases and, depending on which UGV systems are used, different degrees of military benefit are to be found.

No description available.

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems.

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems.

<p>The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently.</p><p>The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing.</p> / <p>Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt.</p><p>Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.</p>

The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently. The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing. / Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt. Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.