Search results

This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain.

This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC).

Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.

Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless.

In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

This study aims to investigate the rule‐based decentralised control framework for a swarm of UAVs carrying out a cooperative ground target engagement mission scenario.

This study is to investigate the rule‐based decentralised control framework for missions which require high‐level cooperation between team members. The design of the authors’ control strategy is based on agent‐level interactions. Different to a centralized task assignment algorithm, the cooperation of the agents is entirely implicit. The behaviour of the UAVs is governed by rule sets which ultimately lead to cooperation at a system level. The information theoretic measures are adopted to estimate the value of possible future actions. The prediction model is further considered to enhance the team performance in the scenario where there are tight coupled task constraints.

The simulation study evaluates the performance of the decentralised controller and compares it with a centralised controller quantitatively. The results show that the proposed approach leads to a highly cooperative performance of the group without the need for a centralised control authority. The performance of the decentralised control depends on the complexity of the coupled task constraints. It can be improved by using a prediction model to provide information such as the intentions of the neighbours that is not available locally.

The achievable performance of the decentralised control was considered to be low due to the absence of communication and little global coordinating information. This study demonstrated that the decentralised control can achieve highly cooperative performance. The achievable performance is related to the complexity of the coupled constraints and the accuracy of the prediction model.

The purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.

Control of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.

Robustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.

To the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.

The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance. The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. This arrangement significantly narrows the gap between the estimated and the true path analyzed using the mean square error (MSE) for different offsets on Next Generation Simulation Interstate 80 (NGSIM I-80) data set. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation, thereby, making it amicable for real-road scenarios.

The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase.

This arrangement significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation. Thereby, making it amicable for real-road scenarios.

This paper builds a mathematical model that considers the offset and host (mid) vehicles for appropriate path fitting.

The purpose of this paper is to achieve accurate integrated navigation results for the unmanned aerial vehicle (UAV) systems even in the presence of possible navigation faults in the subsystems of the federated Kalman filter.

The federated Kalman filter is modified from two aspects to get accurate navigation results under abnormity. First, time-variant vector distribution coefficients trading off the navigation accuracy and the observability degree of each state component are computed to replace the traditional scalar coefficients. Second, a fault-tolerant filter is proposed as the local navigation filter.

Simulations for the navigation of a UAV system show that the proposed method can be applied for accurate navigation purpose even in the presence of subsystem navigation faults.

New fault-tolerant federated Kalman filters for integrated navigation are presented to achieve accurate navigation solutions.

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

The case compares two U.S. Department of Defense (DoD) programs from the 1970s and 1980s: (1) “stealth” combat aircraft, capable of evading detection or engagement by anti-aircraft systems, and (2) precision attack of hardened ground vehicles from “standoff” distances, i.e., far behind the battle lines. Conceived at roughly the same time, motivated by the same strategic challenge, and initially driven by the same DoD organization, stealth combat aircraft progressed from idea to deployment in less than eight years---an astounding pace for a complex military system---while a demonstrated system for standoff precision strike against mobile ground targets was not fully implemented. The case highlights the critical role of the Defense Advanced Research Projects Agency (DARPA), part of the DoD, regarded as one of the most innovative entities in the U.S. federal government.

The case highlights factors that facilitate rapid, successful implementation of radically innovative or disruptive concepts. Students are introduced to the organizational realities facing such projects, including issues of strategic clarity, interdepartmental competition and cooperation, executive leadership, and timing. Comparing the differences in implementation of the two programs in the case reveals issues relevant to any large organization seeking to bring innovative concepts to fruition.

This paper aims to investigate the effect of four important process parameters (i.e. laser focal distance, travel speed, feeding gas flow rate and standoff distance) on the size of single clad geometry created by coaxial nozzle-based powder deposition by high power laser.

Design of experiments (DOE) and statistical analysis methods were both used to find optimum parameter combinations to get minimum sized clad, i.e. clad width and clad height. Factorial experiment arrays were used to design parameter combinations for creating experimental runs. Taguchi optimization methodology was used to find out optimum parameter levels to get minimum sized clad geometry. Response surface method was used to investigate the nonlinearity among parameters and variance analysis was used to assess the effectiveness level of each problem parameters.

The overall results show that wisely selected four problem parameters have the most prominent effects on the final clad geometry. Generally, minimum clad size was achieved at higher levels of gas flow rate, travel speed and standoff distance and at minimum spot size level of the laser focal distance.

This study presents considerable contributions in assessing the importance level of problems parameters on the optimum single clad geometry created laser-assisted direct metal part fabrication method. This procedure is somewhat complicated in understanding the effects of the selected problem parameters on the outcome. Therefore, DOE methodologies are utilized so that this operation can be better modeled/understood and automated for real life applications. The study also gives future direction for research based on the presented results.

This paper aims to provide technical details of the techniques used for the remote detection of chemical compounds in a number of applications and also to highlight key research themes.

Following a short introduction, this first considers remote gas detection using the DIAL technique. Remote gas cloud imaging is then discussed, and this is followed by a review of chemical warfare agent detection technologies. A selection of research activities and product developments aimed at remotely detecting explosives are considered and, finally, brief concluding comments are drawn.

Remote gas sensing is now a practical reality, and products are available which can remotely detect, identify, quantify and in some cases visualise a wide range of toxic and environmentally threatening gases. These satisfy numerous industrial, environmental and military applications. Remotely detecting explosives poses a significant technological challenge, and despite some commercialisation, it remains the topic of an extensive research effort, much involving LIBS and Raman techniques. Importantly, much of this work also has potential in non-military applications, with several developments being shown to detect various industrially important compounds.

This provides a technical insight into the techniques and products used in a range of remote chemical sensing applications.

The Paris/Le Bourget International Air and Space Show is the world's oldest international show and is of considerable importance. It will have 548 exhibitors from 23 countries and there will be 165,000 square metres of display area — with 10,000 square metres for the out‐door static exhibits. The numbers of chalets is to be increased by 55.