Top PDF A hardware-in-the-loop simulation and test for unmanned ground vehicle on indoor environment

A hardware-in-the-loop simulation and test for unmanned ground vehicle on indoor environment

A hardware-in-the-loop simulation and test for unmanned ground vehicle on indoor environment

In this paper, we present an alternative approach for analyzing performance and monitoring of unmanned ground vehicle (UGV) through a Hardware-in-the-loop (HIL) simulation. This approach is started by defined a mathematical model of kinematic-dynamic forces apply and sensor-actuator model that integrated in UGV for simulation. A novel hardware control architecture was built to meet the HIL simulation method. Both simulation model and hardware configurations are provided by MATLAB/simulink toolboxes. To verify the HIL system, 2 small fields was built for the real test. Moreover 3D virtual model of UGV and the environment test field was developed to ease the system monitoring. Finally from the tracking system, results show that the HIL simulation method combined with the real environment produce more information of parameter that influenced during the tests given.
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Unmanned Ground Vehicle for Military Assistance

Unmanned Ground Vehicle for Military Assistance

[1] This survey summarizes current research on unmanned vehicles which are used in terrain detection. Various methods are used for indoor and outdoor terrain detection. Most of the research done in this area is novel and this field is still developing as a program of research. However, the research in this field is necessary even though many existing approaches are effective. The findings consists of various methods and algorithms used; the type of hardware and algorithm used to perform the terrain detection; the benefits of each approach span in the use of various sensors and the way the terrain detection is carried out in each of its applications. For autonomous navigation, the vehicle should be equipped with reliable sensors for sensing the environment, building environment models and following the correct defined path.
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Simulation of muscle-powered jumping with hardware-in-the-loop ground interaction

Simulation of muscle-powered jumping with hardware-in-the-loop ground interaction

Abstract— We developed a novel reverse haptic interface to augment forward dynamic simulations with real-world contact forces. In contrast with traditional haptics, in which a real- world user drives an interaction with a simulated environment, reverse haptics allows a simulated mechanism to probe the real- world environment through a force-sensing robotic manipulator. This method can implicitly extend computer models of biome- chanics and robotic control with complex ground interactions. A 3-DoF manipulator and a biologically inspired musculoskeletal model were developed to test jumping performance on a diverse range of real-world substrates. Jumps were of similar height despite differences in material properties and no active muscle control. Muscle power was lower at the hip, yet total muscle work was higher, against compliant surfaces compared to stiff surfaces. Through reverse haptics, the forces of actuation, inertia and contacts could be measured simultaneously to reveal how intrinsic muscle properties may compensate for substrate dynamics.
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An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the- Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will propagate to real/live flight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, provides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation.
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Unmanned Ground Vehicle Software Development Environment

Unmanned Ground Vehicle Software Development Environment

The Unmanned Ground Vehicle Electronic Hardware Architecture[1] memorandum describes an electronic hardware architecture that has the flexibility and extensibility to support a wide range of UGV platforms. It achieves this flexibility by ascribing to a distributed paradigm which enables the use of multiple scales of processors. The Electronic Hardware Architecture is applicable to small indoor platforms with limited payloads and it easily scales to support large platforms that do not have payload limitations. The hardware architecture does not exist in isolation since it provides services and functionality to software algorithms. It is the software that creates autonomous capabilities to the UGV. These autonomous capabilities are implemented by software algorithms such as: the representation of the world; path planning; obstacle avoidance; and others. Additional software will be involved in commanding the various motors that allow the UGV to traverse the selected path.
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Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle

Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle

A series of experimental and simulation studies were performed in order to demonstrate and evaluate the performance of the GNC algorithms applied to the unmanned ground vehicle. An initial open-loop experiment was conducted in which the UGV was manually controlled to drive a straight path on level terrain with two cones on either side of the path serving as obstacles. The purpose of this experiment was to confirm the functionality of the terrain mapping algorithms for a relatively simple test case. Then, a second open-loop experiment was conducted in which the robot slowly traveled on a curved path with multiple obstacles in the area in order to test the terrain mapping algorithms on more complex terrain. Finally, a set of closed-loop simulations was performed in which the UGV planned a path and autonomously drove through the planned waypoints to arrive at a target location. In these simulations, the terrain data derived from the second set open-loop experiment was used to provide the obstacle avoidance constraints for the path planning algorithm.
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Optimal Path Planning for a Remote Sensing Unmanned Ground Vehicle in a Hazardous Indoor Environment

Optimal Path Planning for a Remote Sensing Unmanned Ground Vehicle in a Hazardous Indoor Environment

This paper presents an approach for optimal path planning for a remote sens- ing autonomous robot in a cluttered and hazardous indoor environment. The operating scenario of this robot is applicable during the search and rescue mis- sions, where unmanned ground vehicles (UGVs) are favored, to survey and sense the environment for various detectable phenomena such as gases, fire or smoke detection and etcetera. The proposed algorithm can be generalized to any given map and as an example simulation scenario; we present an application of path planning of a mobile robot in an urban search and rescue mission to navi- gate through an indoor hazardous building for remote-sensing and assessment of the hazardous situation. The sensing of the environment would enable the first responders’ team to determine the severity of the emergency and would help them to decide on rescuing the victims with the least risk towards the team. With the proposed system, the robot will navigate autonomously by utilizing probabilistic roadmaps (PRM) to find out all the possible navigation paths for autonomous navigation of the robot, given the building map. With the various solutions of the probabilistic roadmaps, an optimal path that would be selected, based on particle swarm optimization algorithm, that covers most of the indoor area to provide the best possible assessment of the hazard situation.
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Hardware-in-the-Loop Machine Simulation for Modular Machine Tools

Hardware-in-the-Loop Machine Simulation for Modular Machine Tools

This kind of machine simulation can be used during first planning phases to improve the commissioning on personalized machine tools. Another great advantage of this kind of simulation will be the possibility to show even the effect of a refitting in more detail and the improvement for known processes can be quantified already before changing the real machine. One barely mentioned application for such kind of simulations supports the marketing process. During the first discussions with possible customers this kind of machine simulation can be used not only to quantify the different variants of machine tools but also to visualize the effect of these changes. Current procedure for selling a machine tool uses known similar processes or experience based approximations to estimate the properties of a machine for a targeted process. These values might differ a lot compared to the real process on the real machine. With the presented simulation it is possible to test a customer provided process on different machine variants. Additionally, this allows an accurate forecast of important manufacturing values like production time, process stability and process efficiency.
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Model Oriented Scheduling Algorithm for The Hardware-In-The-Loop Simulation

Model Oriented Scheduling Algorithm for The Hardware-In-The-Loop Simulation

Fig. 1. Scheme of model blocks on time axis с To organize the calculationson the basis of EDF, we propose to allocate all threads in the intervals (windows) to be performed in one RT cycle. This is due to the fact that, when processing thread of high frequency with slow threads,a situation will necessarily arise with the execution of the slow thread violating the deadline for performing fast. In this case, a slow thread can be decomposed into blocks. Each block is processed in the next provided window с . Thus each of the thread model is represented by a set of composite blocks on the preparation phase. Each block contains an iteration part of the simulation task. The slice of a part depends on two values: RT cycle timings and task deadline . Each block begins and ends with the context switch interrupt of anotherblock from another thread.
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A Probabilistic Framework for Real Time Mapping on an Unmanned Ground Vehicle

A Probabilistic Framework for Real Time Mapping on an Unmanned Ground Vehicle

Before we conclude, there is one final issue which we feel needs to be addressed given the overall framework within which the research presented in this thesis fits. In particular, this thesis has been the culmination of several years of work on the 2004 and 2005 DARPA Grand Challenges. Recently, however, DARPA has announced a third Grand Challenge, to be held in November 2007, which will focus primarily on autonomous driving in urban, dynamic environments. It is clear that this urban Grand Challenge (DGC3) will present difficulties that are somewhat different from those of the previous two Grand Challenges (DGC1-2). The most notable difference, of course, is that in DGC1-2, the assumption that the environment would be static was a valid one, whereas in DGC3, the environment will be dynamic, i.e. , it will contain moving obstacles such as other vehicles. Because this static assumption was critical for the mapping framework developed for DGC1-2 and presented here, it is clear that additional work will be required for the Team Caltech vehicle to be able to compete in DGC3. In particular, we believe that navigation in an urban environment will require the vehicle to have a great deal more understanding and awareness of its environment. It will no longer be adequate for the vehicle to treat all terrain features the same based solely on their rough geometry, e.g., treating vehicles and large boulders the same, since they have roughly the same overall size and shape in an elevation map. Instead, the vehicle will have to actively recognize objects like vehicles, and attempt to model their dynamics to predict how they will behave. The rasterized mapping framework used for DGC1-2 may not be sufficient for this aspect of DGC3; a framework closer to the vectorized one described in the previous paragraph may be needed.
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Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

measurements: 1) precision Q=T P/(T P+F P), which is the percentage of correctly classified road pixels over the total detected road pixels; and 2) error rate ER= (F P+F N )/(T P + F N ), which is the percentage of wrong classified image pixels over the ground- truth road area. T P , F P , and F N , respectively, are true positive, false positive, and false negative. is combined with the online GraphCut detection in ROI as the tracking result and then the tracking result is pixel wisely com-pared with the ground truth. From the result in Table II, we can see that our homography-alignment-based technique is much faster and more accurate. The reason is the introduction of the KLT tracker and context-aware RANSAC. The further experiment on the context-aware homography estimation scheme is given in Figs. 10 and 11. We can see that the alignment of road regions based on our context-aware homography-alignment-based tracking is more accurate than the one obtained by the traditional homography estimation. The reason can be due to the fact that some objects such as bushes, tress, or highland (hills) play a nontrivial role in homography estimation through the common RANSAC. However, these high objects cannot be treated as lying in the same plane with road areas, and are not desirable for the estimation of an optimal homography to accurately align road regions only. In addition, our context-aware homography estimation scheme is much faster than the common RANSAC one, and the computational time is less than 10%. We also experiment different features for homography estimation, including SIFT, Harris, and SURF. Amongst them, SURF has been widely used for an accurate homography estimation. It takes moreover 0.142 s per frame for the detection of SURF features. FAST has the best overall performance (as studied in [14]), where the FAST feature detection only takes 0.008 s per frame.
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Hardware-in-the-loop simulation of massive-payload manipulation on orbit

Hardware-in-the-loop simulation of massive-payload manipulation on orbit

The HIL simulator [14] is composed of a 6 axis force sen- sor for acquiring the external force, an X-Y motion table for reproducing the translational large movement of the payload, and a HEXA-type [15] motion table for repro- ducing 6-DOF guide movement. The force sensor is mounted at the boundary of the hardware simulation and software simulation. In the HIL simulation, a force and torque acting on the payload are measured by the force/ torque (F/T) sensor. The relative position and orienta- tion with respect to the payload of interest are calculated by solving a dynamic equation with the measured force/ torque data. Finally, the calculated relative position and orientation are realized on the hardware side by a servo- mechanism in real time (see Fig.  2). The response delay time compensation method [13] is applied to the hybrid simulator.
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Stability synthesis of power hardware-in-the-loop (PHIL) simulation

Stability synthesis of power hardware-in-the-loop (PHIL) simulation

N POWER hardware-in-the-loop (PHIL) simulation, a real- time parallel processing computer system, that can simulate a large electric network, is interfaced with a physical system through D/A and A/D converters and a power amplifier. This has the advantage that it can provide an opportunity to investi- gate the hardware under test (HuT) repeatedly in real test con- ditions. Wide variety of tests and experiments on power sys- tems, which are costly, difficult and risky to be practically examined, can be economically and safely implemented through a PHIL simulation. Moreover, this method has the potential to reveal the full extent of system interactions to be expected in the final design stage [1-3].
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Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

behind the UGV starting position. The acceleration would under perfect con- ditions be constant until the desired speed is reached. This approximation was shown to be quite accurate in most cases. Many of the flight tests had acceler- ation phases such as the one shown in Figure 8.1, with a constant acceleration that ends close to the same time as the vehicle positions overlap, as was intended. Not all landing attempts followed this pattern for the acceleration phase. This occurred for example as a result of poor initial conditions. In the example shown in Figure 8.2) there is a large initial error in ∆y, making the UAV fly with a considerable part of its velocity in the y direction. This makes the UGV overestimate the time it will take for the vehicles to align, leading to an overshoot in ∆x.
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The Star Scene Modeling and Generation for Hardware in the loop Simulation

The Star Scene Modeling and Generation for Hardware in the loop Simulation

Abstract. The star sensor simulation system is used to test the star sensor performance on the ground, which is designed for star identification and spacecraft attitude determination of the spacecraft. The computer star scene based on the astronomical star chat is generated for hardware-in-the-loop simulation of the star sensor simulation system using OpenGL. The results of star scene generation are shown in this paper and applied in the real time star sensor simulation.

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Development and Real-time Dynamic Positioning of an Unmanned Ground Vehicle

Development and Real-time Dynamic Positioning of an Unmanned Ground Vehicle

An autonomous UGV is essentially an autonomous robot or we can say an intelligent robot that operates without any human controller or human intervention on the UGV. Data collected from its sensors by the vehicle is used to develop some restricted understanding of its surrounding area, which is further used by control algorithms to determine the next movement to take in the perspective of a human provided task or objective. This can eliminate the requirement of any human to watch over the tedious task that the UGV is completing.

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Hardware in the loop 
		simulation and digital control of double inverted pendulum

Hardware in the loop simulation and digital control of double inverted pendulum

The popularity that control systems have gained at industrial level, has triggered the use of new technologies to simulate industrial processes in laboratories, without having a station with the plant to control. This paper presents the modeling of an inverted double pendulum and, subsequent emulation and control using Hardware-In-The-Loop. To being able to accomplish the previous, first the mathematical model of the plant was obtained from the method of Euler-Lagrange differential equations. The model was then discretized with a sampling time of 0.2 s and programmed into an embedded device. Within a user interface developed in C#, a discretized LQR controller was programmed acting on the embedded system, through a serial communication protocol. Furthermore, this interface monitors the output signals. The obtained results demonstrate the advantages of using such tools, since a plant can be controlled in real time, without having it physically made.
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System Modeling and Simulation of an Unmanned Aerial Underwater Vehicle

System Modeling and Simulation of an Unmanned Aerial Underwater Vehicle

In addition, the system dynamics of UUVs has also been paid increasing attention by researchers. With the development of modern marine researches, many intelligent marine equipment, such as surface ships, semi-submarines, unmanned submarines and deep-sea robots, have been developed in all aspects. In particular, the underwater autonomous vehicles are facing unknown and hazardous environments, and their research and deployment have been regarded as one of significant goals and challenges by human beings. Therefore, an UUV with autonomous control should have capability to perceive its own position as well as its environment and react to unexpected or dynamic circumstances properly [14,15]. In addition, the system models of different shape configurations, such as open structure, torpedo-like and multi-thruster, are studied respectively [16,17]. Thruster fault detection and the isolation method and switching control of multi-thruster have been discussed [18,19].
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Benchmarking Model Variants in Development of a Hardware-in-the-Loop Simulation System

Benchmarking Model Variants in Development of a Hardware-in-the-Loop Simulation System

A Hardware-in-the-Loop (HIL) simulation system that offers the ability to test various engine control architectures and algorithms without the need for a physical engine prototype is under development at NASA Glenn Research Center. In order to demonstrate a path to a realistic test bed, a self contained closed-loop engine model was restructured to a processor-in-the-loop configuration. These models implemented in the HIL system were compared against the baseline engine and controller model from the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k). C-MAPSS40k was restructured to enable simula- tion on three separate computers, each containing one of three components (the Engine Plant Model, the Control System Platform (CSP), and the User Interface) communicating over a local area network. Addi- tional modifications were made to the CSP to replace the sensor and actuator models in C-MAPSS40k with Smart Transducer models developed around the IEEE 1451 specifications. These models were implemented in Simulink as part of the CSP, and also implemented on microcontroller boards integrated with the HIL system to simulate an HIL application. To demonstrate that these modifications did not significantly affect the simulation results, and to evaluate the ability to run simulations in real-time, a test flight profile was de- fined for benchmarking the HIL system. Four controller configurations were considered for comparison to the baseline C-MAPSS40k. Results from benchmarking these alternate configurations have demonstrated that the extended models differed little from the baseline case. Possible topics for future work include modifying the initial condition creation process for the processor-in-the-loop system to reduce initialization transients or the implementation of a 4Mbps control network and the benchmarking of real-time simulations.
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Hardware-in-the-Loop Simulation mit dem hydraulischen Versuchsstand MAVIS

Hardware-in-the-Loop Simulation mit dem hydraulischen Versuchsstand MAVIS

Im vorherigen Kapitel wurde gezeigt, dass durch die MAVIS eine Verzögerung eingebracht wird, die auch als Totzeit modelliert werden kann.. Solche Totzeiten können die Ursache für ein ins[r]

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