This PDF file contains the front matter associated with SPIE Proceedings Volume 9837 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Angular orientation of both mobile and stationary objects continues to be an ongoing topic of interest for guidance and control as well as for non-GPS based solutions for geolocations of assets in any environment. Currently available sensors, which include inertia devices such as accelerometers and gyros; magnetometers; surface mounted antennas; radars; GPS; and optical line of sight devices, do not provide an acceptable solution for many applications, particularly for gun-fired munitions and for all-weather and all environment scenarios. A robust onboard full angular orientation sensor solution, based on a scanning polarized reference source and a polarized geometrical cavity orientation sensor, is presented. The full position of the object, in the reference source coordinate system, is determined by combining range data obtained using established time-of-flight techniques, with the angular orientation information.

We describe a problem of optimal planning for unmanned vehicles and illustrate two distinct procedures for its solution. The problem under consideration, which we refer to as the search tour problem, involves the determination of multi-stage plans for unmanned vehicles conducting search operations. These types of problems are important in situations where the searcher has varying performance in different regions throughout the domain due to environmental complexity. The ability to provide robust planning for unmanned systems under difficult environmental conditions is critical for their use in search operations. The problem we consider consists of searches with variable times for each of the stages, as well as an additional degree of freedom for each stage to select from one of a finite set of operational configurations. As each combination of configuration and stage time leads to a different performance level, there is a need to determine the optimal configuration of these stages. When the complexity of constraints on total time, as well as resources expended at each stage for a given configuration, are added, the problem becomes one of non-trivial search effort allocation and numerical methods of optimization are required. We show two solution approaches for this numerical optimization problem. The first solution technique is to use a mixed-integer linear programming formulation, for which commercially available solvers can find optimal solutions in a reasonable amount of time. We use this solution as a baseline and compare against a new inner/outer optimization formulation. This inner/outer optimization compares favorably to the baseline solution, but is also amenable to adaptation as the search operation progresses. Numerical examples illustrate the utility of the approach for unmanned vehicle search planning.

In many multi-agent teams, entities fully trust their teammates and the information that they provide. But we know that this can be a false assumption in many cases, which can lead to sub-optimal performance of the team. In this paper, we build off of prior work in developing a simple model of estimating and responding to different levels of trust between team members. We have chosen to use a vehicle convoy application to generate data and test the operation of the trust estimation algorithm and its evolution. We build on prior work, where a cruise control algorithm to maintain following distance was implemented, as were algorithms to adjust follow distance based on trust in the leader and the capability for a lead vehicle to “look back” and adjust its speed based on the follow distance of the vehicle behind. In this paper we introduce a mechanism, based on trust, which negotiates between two follow behaviors, either follow the vehicle ahead or drive towards a set of fixed waypoints. We also add a nonlinear relationship between trust and follow distance to provide a knob to adjust convoy performance and the paper shows that it does adjust performance, somewhat as expected.

Mobility and terrain are two sides of the same coin. You cannot describe mobility unless you describe the terrain. For example, if my world is trench warfare, the tank may be the ideal vehicle. If my world is urban warfare, clearing buildings and such, the tank may not be an ideal vehicle, perhaps an anthropomorphic robot would be better. We seek a general framework for mobility that captures the relative value of different mobility strategies. Game theory is positively the right way to analyze the interactions of rational players who behave strategically. In this paper, we will describe the interactions between a mobility player, who is trying to make it from point A to point B with one chance to refuel, and a terrain player who is trying to minimize that probability by placing an obstacle somewhere along the path from A to B. In previous work [1], we used Monte Carlo methods to analyze this mobility game, and found optimal strategies for a discretized version of the game. Here we show the relationship of this game to a classic game of timing [2], and use solution methods from that literature to solve for optimal strategies in a continuous version of this mobility game.

Coordination among multiple robots has been extensively studied, since a number of practical real problem s can be performed using an effective approach. In this paper is investigated a collective task that requires a multi-robot system to search for randomly distributed mines in an unknown environment and disarm them cooperatively. The communication among the swarm of robots influences the overall performance in terms of time to execute the task or consumed energy. To address this problem, a new distributed recruiting protocol to coordinate a swarm of robots in demining mission, is described. This problem is a multi-objective problem and two bio inspired strategies are used. The novelty of this approach lies in the combination of direct and indirect communication: on one hand an indirect communication among robots is used for the exploration of the environment, on the other hand a novel protocol is used to accomplish the recruiting and coordination of the robots for demining task. This protocol attempts to tackle the question of how autonomous robot can coordinate themselves into an unknown environment relying on simple low-level capabilities. The strategy is able to adapt the current system dynamics if the number of robots or the environment structure or both change. The proposed approach has been implemented and has been evaluated in several simulated environments. We analyzed the impact of our approach in the overall performance of a robot team. Experimental results indicated the effectiveness and efficiency of the proposed protocol to spread the robots in the environment.

In the last years the physical security in transportation systems is becoming a critical issue due to the high number of accidents and emergency situations. With the increasing availability of technological applications in vehicular environments researchers aimed at minimizing the probability of road accidents. In this paper, we propose a new platform able to discover dangerous driving behaviors. We based our application on the on-board diagnosis standard, able to provide all the needed information directly from the electronic vehicle control unit . We integrated the received data with a fuzzy logic approach, obtaining a description of the driver behavior. The overall system can take several initiatives (alarms, rpm corrections, etc.), in order to notify the driver bad behavior. The performance of the proposed scheme has been validated through a deep campaign of driving simulations.

MANETs allow mobile nodes communicating to each other using the wireless medium. A key aspect of these kind of networks is the security, because their setup is done without an infrastructure, so external nodes could interfere in the communication. Mobile nodes could be compromised, misbehaving during the multi-hop transmission of data, or they could have a selfish behavior to save energy, which is another important constraint in MANETs. The detection of these behaviors need a framework that takes into account the latest interactions among nodes, so malicious or selfish nodes could be detected also if their behavior is changed over time. The monitoring activity increases the energy consumption, so our proposal takes into account this issue reducing the energy required by the monitoring system, keeping the effectiveness of the intrusion detection system. The results show an improvement in the saved energy, improving the detection performance too.

In order for humans to safely work alongside robots in the field, the human-robot (HR) interface, which enables bi-directional communication between human and robot, should be able to quickly and concisely express the robot's intentions and needs. While the robot operates mostly in autonomous mode, the human should be able to intervene to effectively guide the robot in complex, risky and/or highly uncertain scenarios. Using smart glasses such as Google Glass∗, we seek to develop an HR interface that aids in reducing interaction time and distractions during interaction with the robot.

Energy-efficient bipedal walking is essential in realizing practical bipedal systems. However, current energy-efficient bipedal robots (e.g., passive-dynamics-inspired robots) are limited to walking at a single speed and step length. The objective of this work is to address this gap by developing a method of synthesizing energy-efficient bipedal locomotion on patterned terrain consisting of stepping stones using energy-efficient primitives. A model of Cornell Ranger (a passive-dynamics inspired robot) is utilized to illustrate our technique. First, an energy-optimal trajectory control problem for a single step is formulated and solved. The solution minimizes the Total Cost Of Transport (TCOT is defined as the energy used per unit weight per unit distance travelled) subject to various constraints such as actuator limits, foot scuffing, joint kinematic limits, ground reaction forces. The outcome of the optimization scheme is a table of TCOT values as a function of step length and step velocity. Next, we parameterize the terrain to identify the location of the stepping stones. Finally, the TCOT table is used in conjunction with the parameterized terrain to plan an energy-efficient stepping strategy.

Design and experimental data for a high power laser diode based free space point-to-point optical power/data link is presented. In time critical power up applications, such as providing power and guidance information to a munition shell just prior to deployment, energy of the order of 100 J needs to be transferred in under 10 s. Current inductive technology is slow and broadcasts a radio-frequency signal which is undesirable for stealth operation. Rapid energy transfer times require high irradiance levels at the surface of the photovoltaic cells, typically, exceeding 1000X suns. Through efficient thermal design of heat sinks, high optical to electrical power conversion efficiencies of 50%, which are usually attainable at low power levels of 1 W, are achievable at higher power levels.

Resupplying forward-deployed units in rugged terrain in the presence of hostile forces creates a high threat to manned air and ground vehicles. An autonomous unmanned ground vehicle (UGV) capable of navigating stealthily at night in off-road and on-road terrain could significantly increase the safety and success rate of such resupply missions for warfighters. Passive night-time perception of terrain and obstacle features is a vital requirement for such missions. As part of the ONR 30 Autonomy Team, the Jet Propulsion Laboratory developed a passive, low-cost night-time perception system under the ONR Expeditionary Maneuver Warfare and Combating Terrorism Applied Research program. Using a stereo pair of forward looking LWIR uncooled microbolometer cameras, the perception system generates disparity maps using a local window-based stereo correlator to achieve real-time performance while maintaining low power consumption. To overcome the lower signal-to-noise ratio and spatial resolution of LWIR thermal imaging technologies, a series of pre-filters were applied to the input images to increase the image contrast and stereo correlator enhancements were applied to increase the disparity density. To overcome false positives generated by mixed pixels, noisy disparities from repeated textures, and uncertainty in far range measurements, a series of consistency, multi-resolution, and temporal based post-filters were employed to improve the fidelity of the output range measurements. The stereo processing leverages multi-core processors and runs under the Robot Operating System (ROS). The night-time passive perception system was tested and evaluated on fully autonomous testbed ground vehicles at SPAWAR Systems Center Pacific (SSC Pacific) and Marine Corps Base Camp Pendleton, California. This paper describes the challenges, techniques, and experimental results of developing a passive, low-cost perception system for night-time autonomous navigation.

A central challenge to autonomous off-road navigation is discriminating between obstacles that are safe to drive over and those that pose a hazard to navigation and so must be circumnavigated. Foliage, which can often be safely driven over, presents two important perception problems. First, foliage can appear as a large impenetrable obstacle, and so must be discriminated from other objects. Second, real obstacles are much harder to detect when adjacent to or occluded by foliage and many detection methods fail to detect them due to additional clutter and partial occlusions from foliage. This paper addresses both the discrimination of foliage, and the detection of obstacles in and near foliage using Lidar. Our approach uses neighboring pixels in a depth image to construct features at each pixel that provide local surface properites. A generative model for obstacles is used to accumulate probabilistic evidence for obstacles and foliage in the vicinity of a moving platform. Detection of obstacles is then based on evidence within overlapping cells of a map without the need to segment segment obstacles and foliage. High accuracy obstacle and foliage discrimination is obtained and compared with the use of a point scatter measure.

Control of current tactical unmanned ground vehicles (UGVs) is typically accomplished through two alternative modes of operation, namely, low-level manual control using joysticks and high-level planning-based autonomous control. Each mode has its own merits as well as inherent mission-critical disadvantages. Low-level joystick control is vulnerable to communication delay and degradation, and high-level navigation often depends on uninterrupted GPS signals and/or energy-emissive (non-stealth) range sensors such as LIDAR for localization and mapping. To address these problems, we have developed a mid-level control technique where the operator semi-autonomously drives the robot relative to visible landmarks that are commonly recognizable by both humans and machines such as closed contours and structured lines. Our novel solution relies solely on optical and non-optical passive sensors and can be operated under GPS-denied, communication-degraded environments. To control the robot using these landmarks, we developed an interactive graphical user interface (GUI) that allows the operator to select landmarks in the robot’s view and direct the robot relative to one or more of the landmarks. The integrated UGV control system was evaluated based on its ability to robustly navigate through indoor environments. The system was successfully field tested with QinetiQ North America’s TALON UGV and Tactical Robot Controller (TRC), a ruggedized operator control unit (OCU). We found that the proposed system is indeed robust against communication delay and degradation, and provides the operator with steady and reliable control of the UGV in realistic tactical scenarios.

This paper explores the applicability of a Linear Quadratic Regulator (LQR) controller design to the problem of bipedal stance on the Minitaur [1] quadrupedal robot. Restricted to the sagittal plane, this behavior exposes a three degree of freedom (DOF) double inverted pendulum with extensible length that can be projected onto the familiar underactuated revolute-revolute “Acrobot” model by assuming a locked prismatic DOF, and a pinned toe. While previous work has documented the successful use of local LQR control to stabilize a physical Acrobot, simulations reveal that a design very similar to those discussed in the past literature cannot achieve an empirically viable controller for our physical plant. Experiments with a series of increasingly close physical facsimiles leading to the actual Minitaur platform itself corroborate and underscore the physical Minitaur platform corroborate and underscore the implications of the simulation study. We conclude that local LQR-based linearized controller designs are too fragile to stabilize the physical Minitaur platform around its vertically erect equilibrium and end with a brief assessment of a variety of more sophisticated nonlinear control approaches whose pursuit is now in progress.

This paper describes the development of a dynamic, quadrupedal robot designed for rapid traversal and interaction in human environments. We explore improvements to both physical and control methods to a legged robot (Minitaur) in order to improve the speed and stability of its gaits and increase the range of obstacles that it can overcome, with an eye toward negotiating man-made terrains such as stairs. These modifications include an analysis of physical compliance, an investigation of foot and leg design, and the implementation of ground and obstacle contact sensing for inclusion in the control schemes. Structural and mechanical improvements were made to reduce undesired compliance for more consistent agreement with dynamic models, which necessitated refinement of foot design for greater durability. Contact sensing was implemented into the control scheme for identifying obstacles and deviations in surface level for negotiation of varying terrain. Overall the incorporation of these features greatly enhances the mobility of the dynamic quadrupedal robot and helps to establish a basis for overcoming obstacles.

The Robotics Collaborative Technology Alliance (RCTA) program focuses on four overlapping technology areas: Perception, Intelligence, Human-Robot Interaction (HRI), and Dexterous Manipulation and Unique Mobility (DMUM). In addition, the RCTA program has a requirement to assess progress of this research in standalone as well as integrated form. Since the research is evolving and the robotic platforms with unique mobility and dexterous manipulation are in the early development stage and very expensive, an alternate approach is needed for efficient assessment. Simulation of robotic systems, platforms, sensors, and algorithms, is an attractive alternative to expensive field-based testing. Simulation can provide insight during development and debugging unavailable by many other means. This paper explores the maturity of robotic simulation systems for applications to real-world problems in robotic systems research. Open source (such as Gazebo and Moby), commercial (Simulink, Actin, LMS), government (ANVEL/VANE), and the RCTA-developed RIVET simulation environments are examined with respect to their application in the robotic research domains of Perception, Intelligence, HRI, and DMUM. Tradeoffs for applications to representative problems from each domain are presented, along with known deficiencies and disadvantages. In particular, no single robotic simulation environment adequately covers the needs of the robotic researcher in all of the domains. Simulation for DMUM poses unique constraints on the development of physics-based computational models of the robot, the environment and objects within the environment, and the interactions between them. Most current robot simulations focus on quasi-static systems, but dynamic robotic motion places an increased emphasis on the accuracy of the computational models. In order to understand the interaction of dynamic multi-body systems, such as limbed robots, with the environment, it may be necessary to build component-level computational models to provide the necessary simulation fidelity for accuracy. However, the Perception domain remains the most problematic for adequate simulation performance due to the often cartoon nature of computer rendering and the inability to model realistic electromagnetic radiation effects, such as multiple reflections, in real-time.

n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist’s palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist’s shade of color. We call the GUI an “Adverb Palette” where the word “Adverb” represents a specific type of objective for the path, such as the adverbs “quickly” and “safely” in the commands: “travel the path quickly”, “make the journey safely”. The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of flexibility using the adverb palette.

Human-robot teaming largely relies on the ability of machines to respond and relate to human social signals. Prior work in Social Signal Processing has drawn a distinction between social cues (discrete, observable features) and social signals (underlying meaning). For machines to attribute meaning to behavior, they must first understand some probabilistic relationship between the cues presented and the signal conveyed. Using data derived from a study in which participants identified a set of salient social signals in a simulated scenario and indicated the cues related to the perceived signals, we detail a learning algorithm, which clusters social cue observations and defines an "N-Most Likely States" set for each cluster. Since multiple signals may be co-present in a given simulation and a set of social cues often maps to multiple social signals, the "N-Most Likely States" approach provides a dramatic improvement over typical linear classifiers. We find that the target social signal appears in a "3 most-likely signals" set with up to 85% probability. This results in increased speed and accuracy on large amounts of data, which is critical for modeling social cognition mechanisms in robots to facilitate more natural human-robot interaction. These results also demonstrate the utility of such an approach in deployed scenarios where robots need to communicate with human teammates quickly and efficiently. In this paper, we detail our algorithm, comparative results, and offer potential applications for robot social signal detection and machine-aided human social signal detection.

Recent research and advances in robotics have led to the development of novel platforms leveraging new sensing capabilities for semantic navigation. As these systems becoming increasingly more robust, they support highly complex commands beyond direct teleoperation and waypoint finding facilitating a transition away from robots as tools to robots as teammates. Supporting future Soldier-Robot teaming requires communication capabilities on par with human-human teams for successful integration of robots. Therefore, as robots increase in functionality, it is equally important that the interface between the Soldier and robot advances as well. Multimodal communication (MMC) enables human-robot teaming through redundancy and levels of communications more robust than single mode interaction. Commercial-off-the-shelf (COTS) technologies released in recent years for smart-phones and gaming provide tools for the creation of portable interfaces incorporating MMC through the use of speech, gestures, and visual displays. However, for multimodal interfaces to be successfully used in the military domain, they must be able to classify speech, gestures, and process natural language in real-time with high accuracy. For the present study, a prototype multimodal interface supporting real-time interactions with an autonomous robot was developed. This device integrated COTS Automated Speech Recognition (ASR), a custom gesture recognition glove, and natural language understanding on a tablet. This paper presents performance results (e.g. response times, accuracy) of the integrated device when commanding an autonomous robot to perform reconnaissance and surveillance activities in an unknown outdoor environment.

With increasing necessity for intuitive Soldier-robot communication in military operations and advancements in interactive technologies, autonomous robots have transitioned from assistance tools to functional and operational teammates able to service an array of military operations. Despite improvements in gesture and speech recognition technologies, their effectiveness in supporting Soldier-robot communication is still uncertain. The purpose of the present study was to evaluate the performance of gesture and speech interface technologies to facilitate Soldier-robot communication during a spatial-navigation task with an autonomous robot. Gesture and speech semantically based spatial-navigation commands leveraged existing lexicons for visual and verbal communication from the U.S Army field manual for visual signaling and a previously established Squad Level Vocabulary (SLV). Speech commands were recorded by a Lapel microphone and Microsoft Kinect, and classified by commercial off-the-shelf automatic speech recognition (ASR) software. Visual signals were captured and classified using a custom wireless gesture glove and software. Participants in the experiment commanded a robot to complete a simulated ISR mission in a scaled down urban scenario by delivering a sequence of gesture and speech commands, both individually and simultaneously, to the robot. Performance and reliability of gesture and speech hardware interfaces and recognition tools were analyzed and reported. Analysis of experimental results demonstrated the employed gesture technology has significant potential for enabling bidirectional Soldier-robot team dialogue based on the high classification accuracy and minimal training required to perform gesture commands.

Current computer vision systems rely primarily on fixed models learned in a supervised fashion, i.e., with extensive manually labelled data. This is appropriate in scenarios in which the information about all the possible visual queries can be anticipated in advance, but it does not scale to scenarios in which new objects need to be added during the operation of the system, as in dynamic interaction with UGVs. For example, the user might have found a new type of object of interest, e.g., a particular vehicle, which needs to be added to the system right away. The supervised approach is not practical to acquire extensive data and to annotate it. In this paper, we describe techniques for rapidly updating or creating models using sparsely labelled data. The techniques address scenarios in which only a few annotated training samples are available and need to be used to generate models suitable for recognition. These approaches are crucial for on-the-fly insertion of models by users and on-line learning.

Surfaces with specular, non-Lambertian reflectance are common in urban areas. Robot perception systems for applications in urban environments need to function effectively in the presence of such materials; however, both passive and active 3-D perception systems have difficulties with them. In this paper, we develop an approach using a stereo pair of polarization cameras to improve passive 3-D perception of specular surfaces. We use a commercial stereo camera pair with rotatable polarization filters in front of each lens to capture images with multiple orientations of the polarization filter. From these images, we estimate the degree of linear polarization (DOLP) and the angle of polarization (AOP) at each pixel in at least one camera. The AOP constrains the corresponding surface normal in the scene to lie in the plane of the observed angle of polarization. We embody this constraint an energy functional for a regularization-based stereo vision algorithm. This paper describes the theory of polarization needed for this approach, describes the new stereo vision algorithm, and presents results on synthetic and real images to evaluate performance.

Perception for ground robot mobility requires automatic generation of descriptions of the robot’s surroundings from sensor input (cameras, LADARs, etc.). Effective techniques for scene understanding have been developed, but they are generally purely bottom-up in that they rely entirely on classifying features from the input data based on learned models. In fact, perception systems for ground robots have a lot of information at their disposal from knowledge about the domain and the task. For example, a robot in urban environments might have access to approximate maps that can guide the scene interpretation process. In this paper, we explore practical ways to combine such prior information with state of the art scene understanding approaches.

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

In this paper some of the motivations for energy-efficient communications in wireless systems are described by highlighting emerging trends and identifying some challenges that need to be addressed to enable novel, scalable and energy-efficient communications. So an architecture for Internet of Things systems is presented, the purpose of which is to minimize energy consumption by communication devices, protocols, networks, end-user systems and data centers. Some electrical devices have been designed with multiple communication interfaces, such as RF or WiFi, using open source technology; they have been analyzed under different working conditions. Some devices are programmed to communicate directly with a web server, others to communicate only with a special device that acts as a bridge between some devices and the web server. Communication parameters and device status have been changed dynamically according to different scenarios in order to have the most benefits in terms of energy cost and battery lifetime. So the way devices communicate with the web server or between each other and the way they try to obtain the information they need to be always up to date change dynamically in order to guarantee always the lowest energy consumption, a long lasting battery lifetime, the fastest responses and feedbacks and the best quality of service and communication for end users and inner devices of the system.

In the modern Vehicular Ad-hoc Networks (VANET) based systems even more applications require lot of data to be exchanged among vehicles and infrastructure entities. Due to mobility issues and unplanned events that may occurs it is important that contents should be transferred as fast as possible by taking into account consistence of the exchanged data and reliability of the connections. In order to face with these issues, in this work we propose a new transfer data protocol called Fast and Scalable Content Transfer Protocol (FSCTP). This protocol allows a data transfer by using a bidirectional channel among content suppliers and receivers exploiting several cooperative sessions. Each session will be based on User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) to start and manage data transfer. Often in urban area the VANET scenario is composed of several vehicle and infrastructures points. The main idea is to exploit ad-hoc connections between vehicles to reach content suppliers. Moreover, in order to obtain a faster data transfer, more than one session is exploited to achieve a higher transfer rate. Of course it is important to manage data transfer between suppliers to avoid redundancy and resource wastages. The main goal is to instantiate a cooperative multi-session layer efficiently managed in a VANET environment exploiting the wide coverage area and avoiding common issues known in this kind of scenario. High mobility and unstable connections between nodes are some of the most common issues to address, thus a cooperative work between network, transport and application layers needs to be designed.

This paper evaluates use of solar flux concentrator systems with photovoltaic cells, it provides analysis on overall economic feasibility based on cost/benefit considerations. Properties evaluated include launch volume/mass, efficiency once in a functioning configuration and service life. Production time will also be discussed considering research on existing technology to expedite integration. Solar energy is primarily harvested via solar panels. With the utilization of a large mirrored dish, solar energy can be concentrated to maximize the efficiency of photovoltaic systems form a cost/benefit standpoint. The design concepts for these systems include fully rigid, tensioned over frame, and inflatable approaches. The efficiency of such systems will be discussed. Pre-existing systems, such as the photovoltaic blanket arrays on the international space station, will be considered. Areas of consideration include cost/output ratio, the efficiency of the array, and the system’s service life. Prior work on ridged, tensioned, and inflatable mirrored systems will be presented.

Many mechanical and electrical systems have utilized the proportional-integral-derivative (PID) control strategy. The concept of PID control is a classical approach but it is easy to implement and yields a very good tracking performance. Unmanned aerial vehicles (UAVs) are currently experiencing a significant growth in popularity. Due to the advantages of PID controllers, UAVs are implementing PID controllers for improved stability and performance. An important consideration for the system is the selection of PID gain values in order to achieve a safe flight and successful mission. There are a number of different algorithms that can be used for real-time tuning of gains. This paper presents two algorithms for gain tuning, and are based on the method of steepest descent and Newton’s minimization of an objective function. This paper compares the results of applying these two gain tuning algorithms in conjunction with a PD controller on a quadrotor system.

Energy storage is one of the most important determinants of how long and far a small electric powered unmanned aerial system (UAS) can fly. For years, most hobby and experimentalists used heavy fuels to power small drone-like systems. Electric motors and battery storage prior to the turn of the century were either too heavy or too inefficient for flight times of any usable duration. However, with the availability of brushless electric motors and lithium-based batteries everything has changed. Systems like the Dragon Eye, Pointer, and Raven are in service performing reconnaissance, intelligence, surveillance, and target acquisition (RISTA) for more than an hour at a time. More recently, multi-rotor vehicles have expanded small UAS capabilities to include activities with hovering and persistent surveillance. Moreover, these systems coupled with the surge of small, low-cost electronics can perform autonomous and semi-autonomous missions not possible just ten years ago. This paper addresses flight time limitation issues by proposing an experimental method with procedures for system identification that may lead to modeling of energy storage in electric UAS’. Consequently, this will allow for energy storage to be used more effectively in planning autonomous missions. To achieve this, a set of baseline experiments were designed to measure the energy consumption of a mid-size UAS multi-rotor. Several different flight maneuvers were considered to include different lateral velocities, climbing, and hovering. Therefore, the goal of this paper is to create baseline flight data for each maneuver to be characterized with a certain rate of energy usage. Experimental results demonstrate the feasibility and robustness of the proposed approach. Future work will include the development of mission planning algorithms that provide realistic estimates of possible mission flight times and distances given specific mission parameters.

Electric motors are becoming increasingly popular for the propulsion and control of unmanned systems. In order to optimize power generation and energy use for unmanned systems, it is important to understand the dynamics of electric motors and the corresponding powertrain. This paper provides an early, preliminary study on an electric motor used for unmanned aerial systems (UAS’). An electric motor dynamometer is used for collecting data on the motor, and trends are discussed. Future work will look at implementing mathematical models in an unmanned ground system built for experimentation.