1 Introduction

The aircraft industry is fully motivated by a well-established trade and passenger’s transport services which lead to well-tailored crafts with good cost–benefit and a huge infrastructure like airports, trade terminal, maintenance and repair facilities, and sets of rules and legislations [1,2,3].

There is a set of aircraft tasks that is used for long-range tasks that can be performed over in sensitive geographic places that demand specialized equipment and well-trained crew. The examples of these tasks are: surveillance, patrol, data collection, search and rescue. These tasks are being progressively substituted by drones or robotic aircrafts, which eliminated crew fatigue and improved performance. Although there have been many improvements in electronics, robotics and computing, the costs of using the so-called drones, especially in defense and surveillance, have increased a lot as stated in Table 1 [4, 5].

Table 1 Operational costs of surveillance crafts
Full size table

The cost of using drones is high because, although there is no use of an on-board crew, it is necessary to have a staff working in the remote-operated station.

Motivated by a scenario of new applications for autonomous aerial unnamed vehicles (UAV), especially air ships, this work proposes a new UAV mission control framework. This framework is composed of a low cost embedded computer cluster, a new autonomous collision evasion system, a new on fly parallel distributed mission route generating system, and a set of instruments for UAV orientation (GPS, electronic compass, accelerometers, among others). The proposed framework is tailored for air ship dynamic and will guide it safely and in a reliable way during its typical missions.

2 Related work

Vehicles path planning is one of the most studied subjects. It is considered a computational complex problem or with exponential time for their resolution. In this section, the most relevant and related works to this subject will be briefly described.

The collision evasion in dynamic environments (the ones with mobile obstacles) is discussed in [6]. The work compares the needs and difficulties of two kinds of platforms: blimps and helicopters. The author proposes an approach based on insect vision model (specifically a grasshopper) that is modeled by neural network to avoid collisions of a robotic helicopter. The direction of the object to be avoided is supplied by Reichardt correlation model.

The control attitude of a robotic blimp that keeps a specific position is proposed in [7]. The work uses the sliding control technic which is implemented by a fuzzy logic system that uses a Lyapunov filter and the stabilization theorem.

Reference [8] proposes an infinity or achievable horizon optimization method to unknown environments. The work is based in a finite optimization time and is dependent on computational performance of the used equipment. It uses a finite state model of the movement of a mini robotic helicopter vehicle. The proposed work uses cost function called tail discharge or path horizon.

The task of controlling a robotic blimp in a strong wings environment is studied in [9]. The work proposes a stability control of the aircraft using following path technic and flying against the wind. The stability control uses a Lyapunov function that follows pre-establish rules. An adapted remote control blimp of 12 m of length was used in satisfactory tests.

The floatability and attitude control of a blimp using just density variation of a pair of balloons inside the craft are the subject of [10]. The work uses the same floatability systems used by aquatic gliding animals, to make the blimp move in the air. It takes the difference of behavior of aquatic systems when they are used in the atmosphere where the air behaves as compressing fluid into consideration. The work uses a feedback control constructed over the motion equation of studied airship. The control system uses a linear quadratic regulator to generate feedback gains.

A path optimization that uses finite retreaded horizon is proposed in [11]. The work uses a Lyapunov as a cost-to-go function to construct a viability path graph to be optimized. After that, the optimization problem is solved as sequential finite state control and sequential decision problem and is used as global planner fed by a finite retreaded horizon method. The work compares the performance and computation load of other works with the proposed model. The tests were conducted in real environment using a Blade CX robotic helicopter.

A robust and simple navigation system using fuzzy logic is the subject of [12] that uses a Plantaraco robotic blimp. The work uses ultrasonic sensors to get data about the surround environment. These data feed a collision evasion fuzzy system and that generates quick turns of 180°. The tests results show a well-balanced behavior of the fuzzy controller that can be even trained.

The control and surveillance of natural disaster areas by a robotic blimp is proposed in [13]. The blimp uses a path control system with speed field method and an optimal inverted path controller as control strategy. The used strategy was efficient in strong wind environment to avoid complex time corrections in the path. The optimal inverted path controller uses y and x coordinates, a non-linear looping based in a Hamilton–Jacobi–Bellman equation, and a Lyapunov filter to horizontal vehicle control. The practical tests used a 12.2 m length blimp, payload of 15 kg, with wind sensors and stereo cameras.

Reference [14] proposes a path control using a reinforced learning method. The method keeps the blimp high using previous environment conditions and agents (autonomous computational programs). The agent programs get bonus when they execute right actions that maximize a Monte Carlo search method used to search solutions. This method leads a direct learn, with no need of previous data storage by agent programs. As search solution space can grow up fast, the Gaussian function will be used to reduce the size of this search space. Ultrasonic sensors capture the state components of these tuples during blimp navigation and a Kalman filter removes any noise. The tests used a 18 m length blimp and were conducted indoors in a 5 m high old factory shed.

The overall robotic blimp dynamic is described in a mathematical model by Gammon et al. [15]. This model is used to develop a predictive and adaptive robust non-linear control that is used to navigate and guide a robotic blimp over environment disturbs. The blimp data (longitudinal flight, climb rate, turns, etc.) were collected and the blimp dynamic mathematic model was generated by MATHLAB suite.

Reference [16] AURORA project proposes an image capture system that models a set of elementary signals. This set establishes a relationship between the blimp speed and the land targets. It uses a similar model of the pendulum problem but in association with the movement to a zero distance with the blimp. The work takes into account the wind effects in three possible situations: without wind and no environmental disturbance; with wind and weak environmental disturbance; and with wind and strong environmental disturbance.

Although there are many significative proposed path planning and collision avoidance methods (as SLAM variations described in [8, 11, 14]) there is none like this work. As stated in this session, the only work that can be considered similar to this is the one described in [14], but it does not propose a coordination of path control and obstacle avoidance, neither a low cost embedded scalable computing environment, nor a fly path optimization procedure as this article proposes, and in [14], obstacle avoidance is not robust and live solution as the collision avoidance proposed in this work.

3 Materials and methods

3.1 The proposed UAV

This work proposes a 36-m-long and 9-m-wide elliptical shape blimp as shown in Fig. 1. It will have two thrusters alongside its length (for climb maneuvers) and a tail thruster (for direction maneuvers), and a cargo bay.

Fig. 1
figure 1

An example of blimp

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3.2 UAV kinematics

The degree of freedom (DOF) is set of independent movements or rotations that can define position or orientation of a mechanical system. Any body surrounded by a fluid has its position and orientation defined by Euler angles and position coordinates (x, y and z). The Euler angles and position coordinates can give a total of six DOFs to a rigid body as stated in Table 2.

Table 2 Euler angles
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The coordinate vector \(\eta\) totally describes the blimp orientation. It is composed by Euler angles vector \(\theta\), plus the three-position coordinated vector p. shown by Eqs. (1), (2) and (3). A blimp is a sub-actuated vehicle and it means that it demands less control data than degrees of freedom DOFs [19] and it stands for all environmental situations. The minimal representation to describe the control data to aerial vehicle is the roll (\(\varPhi\)), pitch (θ) and yaw (Ψ) plus the position vector coordinates p, will describe the blimp behavior at any time. The following equations, from (1) to (9) represent the position, attitude, forces and moments actuating in the blimp.

$$\eta = [p, \theta ]^{\text{T}} ,$$
(1)
$$p = [x, y, z]^{\text{T}} ,$$
(2)
$$\theta = [\varphi , \theta , \varPsi ]^{\text{T}} ,$$
(3)
$$v = [V, \varOmega ]^{\text{T}} ,$$
(4)
$$V = [u, v, w]^{\text{T}} ,$$
(5)
$$\varOmega = [p, q, r]^{\text{T}} ,$$
(6)
$$\tau = [f, m]^{\text{T}} ,$$
(7)
$$f = [X,Y,Z]^{\text{T}} ,$$
(8)
$$m = [K,M,N]^{\text{T}} .$$
(9)

The blimp position is described in relation to inertial coordinates and its linear and angular speeds by a body fixed referential, as stated in Fig. 2, so the following vectors describe a blimp behavior [17, 19].

Fig. 2
figure 2

Inertial and fixed referential

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The η vector (1) has the referential coordinates in a specific referential and v and τ vectors, (4) and (7), the speed and the applied forces related to inertial referential. So, the relation among variables of each referential where one referential is passed to each other [17, 19] can be described by:

$$\dot{p} = R(\theta )V,$$
(10)
$$R(\theta ) = \left[ {\begin{array}{*{20}l} {{\text{c}}(\varPsi ) \cdot {\text{c}}(\theta )} \hfill & { - {\text{s}}(\varPsi ) \cdot {\text{c}}(\varphi ) + {\text{c}}(\varPsi ) \cdot {\text{s}}(\theta ) \cdot {\text{s}}(\varphi )} \hfill & { {\text{s}}(\varPsi ) \cdot {\text{s}}(\varphi ) + {\text{c}}(\varPsi ) \cdot {\text{c}}(\varphi ) \cdot {\text{s}}(\theta )} \hfill \\ {{\text{s}}(\varPsi ) \cdot {\text{c}}(\theta )} \hfill & {{\text{c}}(\varPsi ) \cdot {\text{c}}(\varphi ) + {\text{s}}(\varPhi ) \cdot {\text{s}}(\varphi ) \cdot {\text{s}}(\varPsi )} \hfill & { - {\text{c}}(\varPsi ) \cdot {\text{s}}(\varphi ) + {\text{s}}(\theta ) \cdot {\text{s}}\varPhi \cdot {\text{c}}(\varphi )} \hfill \\ { - {\text{s}}(\theta )} \hfill & {{\text{c}}(\theta ) \cdot {\text{s(}}\varphi ) } \hfill & {{\text{c}}(\theta ) \cdot {\text{c}}(\varphi )} \hfill \\ \end{array} } \right],$$
(11)
$$\dot{\theta } = T(\theta )\varOmega ,$$
(12)

where c and s are cosine and sine, respectively, and T is given by:

$$T^{ - 1} (\theta ) = \left[ {\begin{array}{*{20}l} 1 \hfill & 0 \hfill & { - {\text{s(}}\theta )} \hfill \\ 0 \hfill & {{\text{c}}(\varphi )} \hfill & {{\text{c}}(\theta ) \cdot {\text{s}}(\varphi )} \hfill \\ 0 \hfill & { - {\text{s}}(\varphi )} \hfill & {{\text{c(}}\theta )\cdot {\text{c}}(\varphi )} \hfill \\ \end{array} } \right],$$
(13)
$$T(\theta ) = \left[ {\begin{array}{*{20}l} 1 \hfill & {{\text{s}}(\varphi ) \cdot {\text{t}}(\theta )} \hfill & {{\text{c}}(\varphi ) \cdot {\text{t}}(\theta )} \hfill \\ 0 \hfill & {{\text{c}}(\varphi )} \hfill & { - {\text{s}}(\varphi )} \hfill \\ 0 \hfill & {{\text{s}}(\varphi )/{\text{c}}(\theta )} \hfill & {{\text{c}}(\varphi )/{\text{c}}(\theta )} \hfill \\ \end{array} } \right],$$
(14)
$${\dot{\eta }} = J(\eta )v\quad \quad (15) \leftrightarrow \left[ {\begin{array}{*{20}c} {\dot{p}} \\ {\dot{\theta }} \\ \end{array} } \right] = \left[ {\begin{array}{*{20}l} {R(\theta )} \hfill & {0_{3 \times 3} } \hfill \\ {0_{3 \times 3} } \hfill & {T(\theta )} \hfill \\ \end{array} } \right] \left[ {\begin{array}{*{20}c} V \\ \varOmega \\ \end{array} } \right],$$
(16)

An inertial system is set in blimp body mass reference to start blimp path definition. It starts its path in a geographic coordinate (Rio de Janeiro) and it must go to another coordinate (São Paulo). The starting orientation of the blimp is pointing towards north. The path control must convert those geographic coordinates into the Cartesian system: x, y, z [20,21,22] and then use Eq. (12) to calculate the azimuth angle and the distance. For example, the azimuth angle will be −133°, 9478° and the distance will be 352 km. Thus, as the blimp reaches the target (São Paulo city), it repeats all the procedure to reach the next target and repeats this procedure every time it reaches a target.

3.3 UAV dynamic

The blimp dynamics, described in [17,18,19], takes into account many data about the effects of aerodynamics, structural issues, actuators, and propulsions. Newton and Lagrange Laws of energy conservation were used to study the blimp dynamics:

$$M\dot{v} + C(v)v + D(v) + g(\eta ) = \tau ,$$
(17)
$$\left\{ {\begin{array}{*{20}l} {\eta = \left[ {x,y,z,\varphi , \theta ,\varPsi } \right]^{\text{T}} } \hfill \\ {v = \left[ {u,v,w,p,q,r} \right]^{\text{T}} } \hfill \\ {\tau = \left[ {X,Y,Z,K,M,N} \right]^{\text{T}} } \hfill \\ \end{array} ,} \right.$$
(18)

where

  • M = M RB + MA: rigid body system inertia matrix (including the added masses).

  • C(v) = C RB + C A(v): coriolis forces matrix and centripetal forces matrix (including the added masses).

  • D(v): aerodynamic dumping matrix.

  • g(η): gravitational forces and momentum and static sustentation vector.

  • τ: control data vector.

In Eq. (17) the parameters associated with aerodynamic forces are the added masses—relative to the forces of fluid linear movement around the blimp flight envelope; coriolis and centripetal forces—relative to circular movements of the blimp; and aerodynamic dumping—relative to the resistance of a body that flows into a fluid (wind plus the blimp speed).

As a blimp is sub-actuated vehicle, the roll and pitch angles are minimal and can be set to zero [19]:

$$\varphi = q = 0,$$
(19)

The simplification represented by (19) stands for all environmental situations (wind, different service ceiling, etc.) meaning that the blimp will be using the same orientation parameters. The difference will be the control vector values (forces and moments) to keep the blimp into the planned path. So Eq. (13) can be simplified to Eq. (17):

(20)

And (17) can be rewritten in a linear shape as:

$$\dot{\eta } = v,$$
(21)
$$M\dot{v} + Dv + G\eta_{\text{p}} = \tau ,$$
(22)

The state variables are defined as:

$$x = \left[ {\begin{array}{*{20}c} {x_{1} } \\ {x_{2} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {\eta_{\text{p}} } \\ v \\ \end{array} } \right] \Rightarrow \dot{x} = \left[ {\begin{array}{*{20}c} {\dot{x}_{1} } \\ {\dot{x}_{2} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {\dot{\eta }_{\text{p}} } \\ {\dot{v}} \\ \end{array} } \right],$$
(23)

and

$$Bu = \tau ,$$
(24)

where u is the control variable vector, so the solution for the dynamic of the blimp is:

$$\dot{x}_{1} = x_{2} ,$$
(25)
$$\dot{x}_{2} = M^{ - 1} Dx_{2} - M^{ - 1} Gx_{1} + M^{ - 1} Bu.$$
(26)

The results (25) and (24) do not represent the temporal dependency of variables, so the following model (24) expresses these dependencies:

$$\left[ {\begin{array}{*{20}l} {\dot{x}_{1} } \hfill \\ {\dot{x}_{2} } \hfill \\ \end{array} } \right] = \left[ {\begin{array}{*{20}l} {0_{6 \times 6} } \hfill & {I_{6 \times 6} } \hfill \\ { - M^{ - 1} G} \hfill & { - M^{ - 1} D} \hfill \\ \end{array} } \right]\left[ {\begin{array}{*{20}l} {x_{1} } \hfill \\ {x_{2} } \hfill \\ \end{array} } \right] + \left[ {\begin{array}{*{20}l} {0_{6 \times 3} } \hfill \\ {M^{ - 1} B} \hfill \\ \end{array} } \right]u \Leftrightarrow \dot{x} Ax + bu ,$$
(27)

where D is the linear dumping matrix, G is the restoring matrix forces, and M is the system inertia matrix that is given by:

$$M = {\text{diag\{}}m - X_{{\dot{u}}} ,m - Y_{{\dot{v}}} ,m - Z_{{\dot{w}}} ,I_{xx} - K_{{\dot{p}}} , I_{yy} - M_{{\dot{q}}} , I_{zz} - N_{{\dot{r}}} {\text{\}}} .$$
(28)

The matrix B comes from a vector decomposition of control vector:

$$B = \left[ {\begin{array}{*{20}l} 1 \hfill & 0 \hfill & 0 \hfill \\ 0 \hfill & 0 \hfill & 0 \hfill \\ 0 \hfill & 0 \hfill & 1 \hfill \\ 0 \hfill & { - Z_{MT} } \hfill & 0 \hfill \\ {Z_{MD} } \hfill & 0 \hfill & { - X_{MD} } \hfill \\ 0 \hfill & {X_{MT} } \hfill & 0 \hfill \\ \end{array} } \right],$$
(29)
$$\eta = P(\varPsi ).$$
(30)

Using a 1350 kg payload blimp, one propulsion propeller and one direction motor 150 kg each, and a 0.3750 m3 compressed air tank (for stability and buoyance proposes), an elliptical air envelop with b = 18 m, and a = 4.5 m, the blimp must have a density of 0.1670 kg/m3 [17, 19]. Using (14), (24) and (25) and the blimp layout from Fig. 1, the virtual inertia matrix is calculated as shown in [17,18,19].

The aerodynamic dumping matrix elements from [17,18,19] and [23] are used to calculate the force needed to the blimp reach a specific speed. For example, if a speed of 18 m/s with a 0.4 m/s2 acceleration towards a 3 m/s wind is required, just use [24] to get 1219.2 N to value the propulsion needed, and then motors can be chosen. The forces needed to change the blimp direction can be calculated by [18, 19, 24]. For example, if the blimp makes a turn using an angular acceleration of 0.02 rad/s2 and an angular speed of (π/12) rad/s, the needed force will be 190.31 N by the third motor at the stern of the blimp and orthogonal to xz plane or in y axis direction.

4 The new framework

The framework proposed in this article is composed of an embedded parallel distributed computer, a new collision evasion system, a parallel distributed mission route generator program, and a set of electronic sensors.

4.1 The embedded computer cluster

The Beowulf-based computer cluster [25, 26] is a reliable and easy way to construct a high-performance computer facility. This kind of cluster can provide a lot of benefits as for example free parallel distributed programming environment and high performance programming tools. Thus, this work proposes a framework compounded of an embedded computer cluster made of four nodes connected among them by a switch. The cluster main node (the one who coordinate parallel/distributed execution in the cluster) will be linked with an Arduino [27] or similar hardware to interface control airship movements. A radio link will provide a link to cluster (and the whole proposed framework) to mission control facilities as shown in Fig. 3.

Fig. 3
figure 3

The embedded computer cluster

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The speed of route calculations on the fly is crucial to make the framework reliable and effective. A brand-new platform is used as cluster nodes: four ODROID-x2 open development platform, based on Exynos 4412 Prime 1.7 GHz ARM Cortex-A9 Quad Core with 2 GB memory and two and 2 e ODROID-C1 [28,29,30,31] as shown in Fig. 4. The cluster will supply the airship with enough computer power to generate its routes on fly as it needs and it will run a new, vivid and specific obstacle collision detection and avoidance system. The main path generator task must have been performed by a graph path discover program. The Traveler Salesman (TS) algorithm was chosen to perform it. A version of TS program was modified to be executed in a parallel/distributed programing environment; using MPI (message passage interface) library, it can be executed in the proposed embedded computer cluster. The parallel/distributed version of Traveler Salesman (TS) program will be executed any time it is need because of any change in previously best path discovered by parallel/distributed TS. This will happen when an obstacle avoidance procedure is performed or any time other significant disturbance in airship flight path happens.

Fig. 4
figure 4

ODROID-x2 and ODROID-C1 board data

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4.2 The new path control system

The path mission control system (PMCS) uses a set of pre-selected targets to be reached by the proposed autonomous managed blimp. The set of targets is used by the parallel distributed TS module of the PMCS to establish an optimal route to complete the mission. During the execution of planned route, the laser detection and ranging (LIDAR) [32, 33] and normal radar are used to scan possible unpredicted and unexpected obstacles. The LIDAR and radar perform their scans, collecting data in volume represented by a cone as stated in Fig. 5, so it can detect objects in paths above and below in front of the blimp path. As soon as an obstacle is detected, it is analyzed and the proper avoidance procedures are taken. Fig. 6 shows a flowchart of the PMCS and its main modules. The avoidance procedures are described in next session.

Fig. 5
figure 5

LIDAR range and operation mode

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Fig. 6
figure 6

Path mission control system

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4.3 New obstacle avoidance systems

As a part of PMCS, a new collision avoidance system is proposed. As described in sub-session B, the airship is equipped with a LIDAR and radar to detect obstacles. The radar will make a long-range search and detection of obstacles, while the LIDAR will make short-range detection and collect precise data of obstacle like distance, speed and attitude. The system identifies two classes of obstacles: fixed and mobile ones. The PCMS has two different procedures: one for fixed obstacles and another for mobile obstacles as shown in Fig. 7.

Fig. 7
figure 7

Obstacle evasion

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Fixed obstacle detection procedure uses fallow the wall philosophy and it takes LIDAR data to decide which side to turn so as to contour the fixed obstacle as a wall, as shown in Fig. 8. Figure 9 shows the flowchart for fixed obstacle avoidance procedure of PMCS.

Fig. 8
figure 8

Fixed obstacle avoidance example

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Fig. 9
figure 9

Fixed obstacle procedure

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A new and innovative procedure to detect mobile obstacles is proposed in this article. This new procedure uses the data from LIDAR and COLREGS72 (convention on the international regulations for preventing collisions at sea) [34] navigation rules for cross routes to avoid possible collision situations. The reason to use the navigation rules is that the airship has similar behavior (dynamic forces) of a ship, and as COLREGS72 rules are widely used and tested in control ship and their movements and routes, they must work well with the path control of blimps. Figure 10 shows a flowchart of the avoidance mobile obstacle procedure module from PMCS and Fig. 11 shows an example of COLREGS72 rules.

Fig. 10
figure 10

Mobile obstacle avoidance system

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Fig. 11
figure 11

Sequence (1), (2), (3) and (4) shows a lateral avoidance collision with a mobile obstacle

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To prove the correctness of the proposed obstacles avoidance algorithms of PMCS, a blimp orientation simulator was constructed where all path situations with fixed and moving obstacles to be avoided can be simulated. This simulator is a simple graphic program developed in Delphi and it uses the proposed rules to avoid fixed and mobile obstacles. All collision scenarios of fixed and moving obstacles were extensively tested and Fig. 12 shows the main screen of this simulator.

Fig. 12
figure 12

Obstacle simulation environment program

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4.4 UAV sensors and navigation framework layout

A basic set of sensors is necessary to support airship autonomous operation. The basic sensors are: electronic compass, speed sensor, wind speed sensor, accelerometer, altimeter, frontal radar with 30 km of range and 90° of aperture, LIDAR with 10 km of range and 90° of aperture, GPS, electronic gyroscopic, six ultrasonic close-range sensors (about 50 m range) and four digital cameras. These should be basic and the sensors that supply data for airship navigation and attitude procedures should be always present. Depending on the kind of mission, some extra and specific equipment will be necessary such as: night vision high resolution cameras, electronic surveillance devices for law and enforcement and patrolling tasks, as shown in Fig. 13.

Fig. 13
figure 13

Airship equipment basic layout

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5 Tests and development

A set of tests was carried out with all possible frontal collision scenarios. They were performed in attitude and collision avoidance occurred without any problems in all tested scenarios confirming the accuracy of the proposed methodology. Parallel distributed version of TS program is developed and under tests in the embedded cluster. The execution times of parallel distributed TS program will be compared with the already collected times of the sequential version of TS program to measure the real speed up of new parallel distributed version. In the parallel-distributed TS version sets of 8, 16, 32, 64 and 128 nodes in possible target graphs are being executed in the prototype cluster to collect execution times to show the speed of parallel executions.

The four ODRIOD-x2 boards and two ODROID-C1 boards as shown in Fig. 14 were tested individually with the properly certified version of Linux (UBUNTO flavor). Both of the ODROID boards are based on ARM microprocessor architecture. The tests were satisfactory and Linux installations were tuned to run only the essential services to make them light-weight operational systems. After that, an embedded computer cluster was constructed as shown in Fig. 15. The four ODRIOD-x2 boards were connected to eight ports switch and basic cluster tests were carried on, and the preliminary tests and performance of the cluster were satisfactory. After the basic tests with the parallel/distributed version of TS, the speed of path discover under a parallel/distributed environment was proved.

Fig. 14
figure 14

The four ODROID-x2 in the test bed and an example of ODROID-C1 board

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Fig. 15
figure 15

The embedded computer cluster prototype

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Finally, PMCS will integrate the avoid collision procedures and the parallel/distributed TS program and together they will control the hole airship path during a blimp mission.

A control system using Computed Torque control mode was developed for the controllability of a proposed episode shape blimp of 28 × 7 m with 1000 kg of work load and weighing 2250 kg. The Matlab/Simulink software was used to create a simulation test where environment issues, desired path and forces and moments were taken into account. The following data were used in the tests:

  • Air temperature 20 °C;

  • Air density 1205 kg/m3;

  • Helium density 0.1664 kg/m3;

  • 30 km/h wind (8333 m/s in 45° in xy plane towards blimp path;

  • Blimp cruise speed 60  km/h (16.67 m/s);

  • Service ceiling 1000 m;

  • Simulation time 600 s.

The tests proved the controllability of the proposed blimp using the simulation program as stated in Fig. 16, where the blimp forms a static position at sea level reached the service celling, the cruise speed and path as stated in Fig. 17.

Fig. 16
figure 16

Blimp behavior simulation

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Fig. 17
figure 17

Simulation parameters graphics

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6 Results and discussion

The results of tests using blimp orientation simulator are shown in Table 3. The results show that all collision situations with fixed and mobile obstacles were avoided by proposed avoidance procedures.

Table 3 Results of blimp orientation simulator tests
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In the execution performance tests, a set of target cities in Rio de Janeiro were used. In all performed tests, the blimps start the mission in target 0 (Rio de Janeiro city) and reach the remaining cities in the test set. Two versions of path mission control system (PMCS) programs were developed: one sequential and the other parallel/distributed. Both PMCS were developed in C program language and the parallel/distributed program uses the message-passaging interface (MPI) to provide communication among embedded cluster processors.

The sequential PMCS programs were executed in a single ARM processor of the cluster and the parallel/distributed ones in the prototype of an embedded cluster with four nodes. The sequential and parallel/distributed execution times for 8, 16, 32, 64 and 128 best path targets are plotted into Table 4 and the results show a speed of execution time of the parallel/distributed PCMS version compared with the sequential one and Fig. 18 compares sequential and distributed average execution times.

Table 4 Speed up test results
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Fig. 18
figure 18

Comparison of sequential and distributed average execution times

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7 Conclusion

This work proposes a parallel-embedded mission control framework. It uses an embedded computer cluster of new and powerful hardware to run a new and well-fitted collision avoidance system and a parallel-distributed route system generator. The preliminary tests of collision avoidance, in a computer graphic environment, developed in Delphi, proved that the proposed methodology works properly for both fixed and movable obstacles. A prototype of an embedded cluster was constructed and the preliminary tests showed a better execution time of parallel/distributed version of our path mission control system (PMCS). This framework will lead to more autonomy for unnamed airship making them perform completely autonomous path control of their missions, introducing a new and very cost-effective work platform for both military and civilian applications.

There are many possible uses and applications of an autonomous operated airship, and many things to improve its control framework to make this kind of vehicle even more efficient, reliable and safer. One of the possible future works to this proposed framework is to incorporate a 3D collision avoidance system to work together with the COLREGS72 rules and make the mission control framework even more suitable to an airship operation mode. Another issue is to extend the proposed framework range to a 360° view and so, to avoid lateral possible collision form any direction from any mobile objects.