Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles

Transcription

1 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 1 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles Authors: Gabriele Bastianelli Diego Salamon Alessandro Schisano Alessandra Iacobacci Abstract Collaboration indicates the action of help or support someone in the performance of any activity, contributing to the achievement of a goal. In particular, when teams working collaboratively can obtain greater resources as, recognition and reward when facing competition for finite resources. The ability to establish a collaborative mechanism between unmanned sensors platforms can bring benefits through improved situational awareness (SA). In the design of future unmanned vehicle systems, coordination of heterogeneous teams is one of the key issues that should be solved. Index Terms- unmanned, satellite, collaboration, agent, sensor network, uav. I. INTRODUCTION Collaborative USV means: Unmanned Satellite Vehicles that can analyze the land by transmitted data between them and received / transmitted from the base, acting as a consequence to the actions of others, without a remote command. The technology used allows to exchange data for position and other information through two satellites network (like GALILEO to establish positions). One for geo-positioning and other to communicate information and raw-data to others devices to a Central Unit. The Collaborative USV will increase the autonomy of unmanned vehicles and provide solutions with an adaptive and dynamic execution of autonomous target engagement. With increased autonomy, individual unmanned vehicles can communicate, coordinate and influence each other, allowing them to interact in collaborative teams of unmanned assets. Finally, we will show you an agent-based simulation of a typical situation considering aerials USV, in heterogeneous areas, to check the territory in search of resources, considering and analyzing critical related issues. For the simulation, we will create a small, different world separated into four parts, each one of with specific characteristics. For example, can be classified as a difficult terrain, a canyon, a desert land, a liquid surface and the wood. To each zone, a USV characteristic has been assigned, which has the task of scan it. Because depending on soil type, the USV will have different ways and different times of interaction, we are faced with decisions, such as helping another USV which is in difficulties, focus a particular found another USV, or having to return to the Central Operation Unit to recharge. To take a decision, we have to simulate the interaction trough a complete and real time knowledge of mission in progress. Moreover, the USV will be identified as agents that move on a two-dimensional plane. This oversimplifies the real problem, but lets just focus on decision-making of the USV. In the last years, there was a progressive growth of production of unmanned vehicle; generally, literatures refers to Unmanned Mobile Systems (UMS) to show a set of mobile objects that moves in an environment; UMSs can be equipped with artificial intelligence, or can be guided remotely by a human operator. In the set of UMS, there is a big subset of it, named Unmanned Aircraft Systems (UAS) that identify their operating environment with the Air. The terminology unmanned aircraft system refers not only to the aircraft, but also to allow the supporting equipment used in the system, including sensors, microcontrollers, software, ground station computers, user interfaces, and communications hardware. Unmanned aircraft systems are playing increasingly prominent roles in defense programs and defense strategy around the world. Technology advancements have enabled the development of both large Unmanned aircraft and smaller. As recent conflicts have demonstrated, there are numerous military applications for unmanned aircraft, including reconnaissance, surveillance, battle Damage assessment, and communications relays. Civil and commercial applications are not as well developed, although potential applications are extremely broad in scope, including environmental monitoring (e.g., pollution, weather and scientific applications), forest fire monitoring, homeland security, border control, drug interdiction, aerial surveillance, mapping, and monitoring traffic, precision agriculture, disaster relief, ad hoc communications networks, and rural search and rescue. For many of these applications to develop to maturity, the reliability of UAS needs to increase, their capabilities need to be extended further, their ease of use needs to be improved, and their cost must decrease. In addition to these technical and economic challenges, the regulatory challenge of integrating unmanned aircraft into national and international airspace needs to be overcome. Another element of investigation will be artificial intelligence, or, more exactly, collaboration intelligence; the future belongs to the robots exhibiting social intelligence. Social and interactive skills are necessary in many applications where robots interact and collaborate with other robots or humans. Sensor motor skills including locomotion, object manipulation, carrying the loads, etc. are fundamental for robot companions or robot workers. Such tasks require complete reliability in operation, safety, and full autonomy. Those features are expected from military robots, which are strongly stimulating the progress in service robotics.

2 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 2 II. UAV-USV TECHNOLOGIES We analyze the positive aspects to use a swarm of collaborative USV (in a defined area) dedicated to analysis and observation. For example, we will consider a situational awareness like a resource searching mission, or (at the same way) as a fire detection mission, or as a critical infrastructure surveillance and detection of warning. At first, we will briefly analyze the possibilities and the theoretical concept that a swarm of USV operating in a limited territory may be able to perform their tasks autonomously (almost) without human intervention. The only features to help them is the Real Time (RT) cloud analysis of scanned Raw data from UVSs. For example, each USV knows position of itself and positions of each other USVs, each USV can take a decision and communicate it to the others USVs. For this goal they can use a map provided by GNSS, updating in Real Time (RT) with USV scanned data. This features allow them to take decisions in order to help another USV during the mission, or at the same way to return into a Central Operative Station to recharge. Then, we will briefly proceed to the analysis of the benefits that a solution of this type can lead, and making some examples of situations. Different situation have different benefits. We will proceed to describe benefits and weakness for context like a fire alarm detection in a heterogeneous area. At the same way, we well describe it to situational awareness like these we will write above. it goes to zero as the number of nodes N in a wireless ad hoc network increases. This result holds regardless of optimality in routing, power control, or transmission. Intuitively, this states that, as the number of nodes increases, every node spends almost all of its time forwarding packets of other nodes. From the energy point of view, transmitting raw data to distant nodes is wasteful of scarce resources. The diminishing wireless capacity result can be somewhat mitigated by introducing mobility to nodes, if an application is delay tolerant. Traditional centralized sensing and a signal processing system, where the data has been processed on the edges of a network, is not the better strategy. Each UAV collect and process data independently, only a selection of data is relayed to other UAV. In this case processing resource are limited if compared with previous case, but: Swarm global intelligence is bigger than a single UAV; Delay necessary time to relayed and received processed data is strongly depleted. Nodes Location. The World is the environment overlapped to a grid of nodes; a node is an object, active or passive, fixed or mobile that adds to grid some information; UAV represents an active mobile Node. This model is useful for next studies and simulation, where fixed Virtual sensor in atomizing in the World of simulation. III. ENVIRONMENT This text focuses on simulation of collaborative UAV equipped within Satellite Positioning system receiver. Based on Galileo Technologies; in this case we talk about Unmanned Satellite Vehicle (USV). The aims of the work is define the better strategy to deploy a UAV satellite base collaborative environment. Two are the building block of our environment: Intelligence Location (principally about the sensor network); Nodes Location (UAV is only one of possible nodes of the environment); It isn t a military or civil major objective, all the assumption and all the results are generally applicable both to civil activities (e.g. search and rescue) and military one (e.g.. target localization). Each UAV is equipped within a Virtual Sensor. UAV s Fleet are positioning in a virtual World of the simulation platform. Intelligence Location. As the above applications have illustrated, many sensing tasks require a sensor network system to process data cooperatively and to combine information from multiple sources. In traditional, centralized sensing and signal processing systems, raw data collected by sensors are relayed to the edges of a network where the data is processed. From the scalability point of view, the nonlocal processing at the edges depletes precious bandwidth. If every sensor has some needed data to be sent to another node in a network, then, a well-known wireless capacity result by Gupta and Kumar states that the per node throughput scales as IV. SIMULATION PLATFORM Agent Base Modeling (ABM) is a class of computational models for simulating the simultaneous operations and interactions of multiple autonomous agents aiming to recreate and predict the occurrence of complex phenomena. ABM tools allow the modeling of a system or process by using a Multi Agent Simulation (MAS) system, and posterior simulation in presence of complex phenomena. However, in this work the intention is also to consider to use the ABM tools to simulate agent-based control systems. These platforms are being used to simulate agent-based models for different application domains, such as economics, chemical, social behavior and logistics. A special remark to the use of ticks (universal time) in simulation environments instead of the real time clock, otherwise it is impossible to compare different simulation results (which are dependent of some parameters such as the processing power of the PC processor). NetLogo Modeling Platform. Since the NetLogo tool has been chosen to be used in this work, due to its good relation between programming effort and simulation speed, this section describes his tool briefly. The NetLogo application runs on a Java Virtual Machine, therefore, it is able to run on major available platforms, like Windows, Linux, Mac and Solaris. However, its programming language is based on the Logo programming language and not in Java, making it very easy to be used even by persons with low skills in programming. Basically, NetLogo world is composed by two types of agents, the

3 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 3 stationary agents (or patches) and the mobile agents (or turtles). The patches are arranged in a grid way, so they can form the world in over that the turtles move around. There is a third type of agent that is the link agent, which connects turtles so they can form networks, graphs and aggregates. NetLogo is fully customizable, for example, the user can set the size of the patches and/or the world, and set the size, shape or color of the turtles. The NetLogo GUI is structured in a tab way and is composed by 3 tabs: Interface, Information and Procedures. The Interface tab is the graphical part of NetLogo, i.e. allow the user to insert buttons, create graphics and see the world behavior. The Information tab can be used to retrieve and/or change some information about the objective, functioning or bugs that the model may have. This is useful for the users (that are not the designers/developers) as a starting point to know the expected behavior of the model. The Procedures tab is the place where the code is built, i.e. creating the model with the desired characteristics and expected behavior. V. THEORETICAL MODEL In this section, we will describe the "role models" to simulate the actions of the UAVs to scan the area using satellite data to localize a single UAV and to localize a scanned/unknown patch. A patch is defined as a minimum portion of the territory that each UAV travels in a single unit of time. This description is in keeping with agent-based programming language used here, which divides the simulation environment in a "world" divided into "patches", and some "turtles" who are the agents that live and populate the "world". A. Creating the world Each "patch" of the "world" corresponds to coordinates (x, y), and each "patch" (or to each pair of coordinates (x, y)) correspond to two topological properties described with numerical values: A variable p(x,y), as the weight the single "patch", defined as the difficulty in performing the scanning process due to the topography of the area in question. Specifically, on the simulation carried out, p can represent three types of ground (earth, water, forests), to which are associated three default values of p. A binary variable s(x,y) (available values only 0-1), which says if you ran the scanning process (1) or not (0). At the beginning all patches have s(x,y) = 0 Finally we have created the world through a probabilistic dispersion relation, with coefficients given: 50% land, 25% water, 25% forests. B. Creating the agents In this simulation UAVs are described by entities called "agents" or "turtles. The "agents" are entities that describe trajectories on the "world" interacting with it through mathematical relationships, depending on the type of interaction behavior in simulated. Their goal is to analyze all of the territory within a defined period of time. During the simulation, we assumed a swarm of UAVs consists of 9 units, one at a time starting from a base in the origin of the Cartesian axes. C. The behavior In particular, have been defined, and it has been dealt with the experimental analysis, three types of behavior, which we call: non-cooperative mode, autonomous mode, cooperative mode. The intended objective for UAVs is the full scan of the territory assigned, and the variables considered are the speed of scanning tasks and the average time for scanning tasks of the entire area. 1) Non-cooperative mode. In this mode, each UAV is assigned a portion of land to be analyzed (a box large L), proceeding with a scanner "patch by patch". The mechanisms of interaction of the UAV with the world are described by the following relations. There is no interaction between the UAVs ). ( ( ) ) ) The interactions described above are accompanied with appropriate boundary conditions as: ( ( ( ) )) ) ( ) Where: ρ = 1 if s(x(t)+1,y(t)) = 0, ρ = 0 if s(x(t)+1,y(t)) = 1, called scanning condition, tell me if the next patch is to be scanned or not. p(x,y) is the weight of patch here. = 0 if e if called the scan time, which indicates how long the UAV is scanning the patch. When the value of time variable is equivalent to the weight of the patch, it is revealed to the world below (the scan is completed and it set s = 1 on patch (x(t),y(t)). is the size of box, and it used to define the boundary condition. is a Dirac s Delta. In this way, it describes the motion of a UAV (in a square box with side L), which interacts with the ground, analyzing its

4 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 4 morphology. When a UAV finishes scanning his box, it stops and waits for the others. 2) Autonomous mode. In this mode, the UAVs depart independently, one at a time from the base, and independently scan the area (the entire "world") by relying on the motion to probabilistic rules, similar to lattice random-walk in 2-dimensions. In summary, each "agent" can move randomly in a patch to the nearest neighbors if it is not fully analyzed, with a probability proportional to the number of nearest unknown neighbors around the current position (x (t), y (t)). If all the nearest neighbors were analyzed, the UAV requests data from the frontier of scanning tasks, referred to as the set of patches analyzed further away from the base, to go to the "patch" to be analyzed closer to him on the frontier. Under these conditions the motion of the UAV ("agents") in the "world" can be described by the 2 equations written below: ( ) ( ( ))( ) ( ( ) ) ( ( ) ) ) Where: ( ) is the probability distribution that the agent moves from the position ( ) to the position ( ). is the number of nearest neighbors ( ) of not yet scanned. ( ) are the coordinates of a patch on the frontier satisfying the following relationships: governs the motion of the UAVs, I get the following equations: ( ( ) ) ( ) ) ( ( ) ) ( ) ) ) ) With boundary conditions: ( ( ) ) ( ) ) ) ( ) ) The symbols used correspond to the descriptions made earlier. VI. EXPERIMENTAL ANALYSIS AND RESULTS Each type of behavioral measurements was performed on 50 runs. The unit of time used is called "ticks" and corresponds to required time for an "agent" to move in one of closest "patches each run is ended or with the complete scan of the world, or after 4000 ticks. We have analyzed, as parameters, the time average for a total scan of a "world" large 60x60 "patches" and the evolution of the relationship during the time between the number of patches not scanned, and the total number of patches in the "world". Each patch of which had a weight corresponding to the type of land allocated as follows: percentage of land: 50% percentage of forests: 25% percentage of water: 25% weight s (x, y) for the land patches : 4 weight s (x, y) for forests patches : 10 weight s (x, y) for the water patches : 2 Later, the screenshots are shown for each mode of behavior during the simulation experiment: ) ( ) ). The symbols used correspond to the descriptions made earlier. 3) Cooperative mode In this model, however, has attempted to merge the best properties of the previous ways. We have divided the "world" in the box (with side large L), as in a noncooperative, but we added the ability for UAVs "to be able to help," the other UAVs that have not finished scanning the box assigned to them. Writing by mathematical relationships, the behavioral rules that A. Total Time for Exploration. Experimentally, we obtain the following results for the time necessary to a total exploration of the world : MODE TIME (ticks) Non-Cooperative Autonomous 3574 Cooperative 2968 Data may be analysed by different way: energy consumtion, time to rescue and time to cover, etc..

5 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 5 B.1 non-cooperative mode: screenshots. A.3 Cooperative mode: screenshots In this case, the results are far better that the world is the collaborative mode, as expected. It is interesting to compare the result relating to artificial intelligence of single UAV; in collaborative mode. In order to finalize the mission with the best time, it is just possible using small processor with low consumption. This result is only one of positive consequence of agents/usvs collaboration. B. Unknown Space Evolution during the time. Looking at the trend over time of "discovery ratio" of "world", we note that the rapid achievement of a decisive varies depending on the mode of behavior that can be used. This is a significant achievement, especially because different types of real-world use of UAVs require different timings (have different objectives). For example, if one has as a constraint the quality of the scanning, or if one has a time interval as the constraint of short duration. From the graph extracted during the analysis, it is clear in fact, that the autonomous mode has a fast response and for a time less than the cross-point is able to scan the territory faster than the other modes. On the other hand, the cooperative mode is faster to perform a complete scanning. A.1 Autonomous mode: screenshots Figure 4 shows the 3 patterns, corresponding to the 3 modes of behavior of UAVs. The trend in red corresponds to the autonomous mode, while the trend in green corresponds to the non-cooperative and the cooperative blue.

6 Agent-Based Simulation of Collaborative Unmanned Satellite Vehicles 6 VI.3 Unknown patches ratio vs time (ticks). At the beginning of simulation, the delay of non-cooperative (green) and cooperative (blue) developments is due to the time of unfolding, calculated as the time allows each UAV (one at a time) to displace in the assigned position to begin scanning. In the middle, it is possible to observe a cross point between cooperative and autonomous trend. Specifically, the crosspoint is at time 2245 ticks. Here the curve of "discovery of the territory" in cooperative mode acquires slope due to the fact that cooperation starts between the various agents, overtaking autonomous mode. On the other hand, the difference compared to non-cooperative mode is evident, especially at the end, where the trend has lost the slope, it will be able to reach a quasi-asymptotic curve. VII. BIBLIOGRAPHY [1] K. Fregene, R. Madhavan, and L.E. Parker. Incremental Multiagent Robotic Mapping of Outdoor Terrains. In Proceedings of the IEEE International Conference on Robotics and Automation, pages , May [2] Y. Guo and L. E. Parker. A distributed and optimal motion planning approach for multiple mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation, pages , May [3] R. Madhavan, K. Fregene, and L.E. Parker. Distributed Heterogeneous Outdoor Multi-robot Localization. In Proceedings of the IEEE International Conference on Robotics and Automation, pages , May [4] V. Marik and D. McFarlane, Industrial Adoption of Agent-Based Technologies, IEEE Intelligent Systems, 20 (1), 2005, pp [5] A. Arsie, K. Savla, and E. Frazzoli. Efficient routing algorithms for multiple vehicles with no explicit communications. IEEE Trans. On Automatic Control, 54(10): , [6] B. Moore and K. Passino, Distributed balancing of AAVs for uniformsurveillance coverage, in IEEE Conference on Decision and Control, , [7] M. Pavone, E. Frazzoli, and F. Bullo. Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment. IEEE Trans. on Automatic Control, [8] C.J.E. Castle and A. T. Crooks, Principles and Concepts of Agent- Based Modeling for Developing Geospatial Simulations, Technical Report 110, Centre for Advanced Spatial Analysis, University College. London, UK, September [9] H. V. D. Parunak, Foundations of Distributed Artificial Intelligence, Applications of Distributed Artificial Intelligence in Industry, John Wiley & Sons, pp , [10] M.J. Berryman, Review of Software Platforms for Agent based Models, Technical report, Defence Science and Technology Organisation, Edinburgh, Australia, April [11] S.F. Railsback, S.L. Lytinen, and S.K. Jackson, Agentbased Simulation Platforms: Review and Development Recommendations, Simulation, vol. 82, n. 9, pp , [12] C. Castellano, S. Fortunato, V. Loreto, Statistical physics of social dynamics, Rev. Mod. Phys. 81, 591 (2009). [13] Di Feng Zhao,Leonidas J. Guibas, Wireless Sensor Networks: An Information Processing Approach, Elsevier, (2004).