外文文献—群机器人嗅觉定位

外文文献—群机器人嗅觉定位 附录I 英文 英文原文: Swarm Robotic Odor Localization Adam T. Hayes, Alcherio Martinoli, Rodney M. Goodman Microsystems Lab 136-93, California Institute of Technology, Pasadena CA 91125athayes,alcherio,rogo@micro.caltech.edu Abstract This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of Swarm Intelligence. We describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. We then demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume. Finally, we show that an embodied simulator can faithfully reproduce the real robots experiments and thus can be a useful tool for off-line study and optimization of odor localization in the real world. 1 Introduction This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of Swarm Intelligence (SI), a computational and behavioral metaphor for solving distributed problems that takes its inspiration from biological examples provided by social insects. In most biological cases studied so far, robust and capable group behavior has been found to be mediated by nothing more than a small set of simple interactions among individuals and between individuals and the environment[1]. The application of SI principles to autonomous collective robotics aims to develop robust task solving by minimizing the complexity of the individual units and emphasizing parallelism, exploitation of direct or indirect interactions, and distributed control. The main advantages of this approach are three: first, scalability from a few to thousands of units, second, flexibility, as units can be dynamically added or removed without explicit reorganization, and third, increased system robustness, not only through unit redundancy but also through the design of minimalist units. Several examples of collective robotics tasks solved with SI principles can be found in the literature: aggregation [2] and segregation [3], exploration [4], stick pulling [5], and collaborative transportation [6]. Recently, advances have been made in understanding biological and artificial odor classification and odor localization as developed in moths [7,8] and rats [9] in air, and lobsters [10] and stomatopods [11] in water. Biology utilizes olfaction for a wide variety of tasks including finding others of the same species, communication, behavior modification, avoiding predators, and searching for food. Odors, unlike visual and auditory perceptions, are non-spatial: they possess neither spatial metric nor direction. In contrast, odorant stimuli possess both spatial and temporal character, snaking out complex plumes thatcan wander over a wide area. This implies that a level ofsophistication beyond gradient following is necessary forlocalization of an odor source.Animals use a combination of hardware (e.g. receptoradaptation), software (temporal integration and/or spatial integration), and search strategies (both intrinsic and landmark-based) to locate odor sources. Odor localization is in essence a behavioral problem that varies from animal to animal. Some exploit fluid information at different layers (lobster), or sense several residues on the ground (ants). Others can track odors in 3-D environments (moths) or use a combination of information to locate their targets (dogs). From an engineering standpoint some tasks are facilitated by combining odor sensing with mobile robots, such as the localization of chemical leaks and the chemical mapping of hazardous waste sites. We are interested in developing small mobile robots that use odor tracking algorithms and multi sensor (e.g. odometry, anemometry,olfaction) fusion to search out and identify sources of odor. The aim of the case study described in this paper is three-fold. Firstly, we describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. Secondly, we demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume. Thirdly, we show that an embodied simulator can faithfully reproduce the real robots experiments and thus can be a useful tool for off-line study and optimization of odor localization in the real world. 2 The Odor Localization Problem The general odor localization problem addressed in this paper is as follows: find a single odor source in an enclosed 2D area as efficiently as possible. This can be broken down into three subtasks: plume finding - coming into contact with the odor, plume traversal - following the odor plume to its source, and source declaration -determining from odor acquisition characteristics that the source is in the immediate vicinity. Plume finding amounts to a basic search task, with the added complication, due to the stochastic nature of the plume, that a sequential search is not guaranteed to succeed. Plume traversing requires more specialized behavior, both to progress in the direction of the source and to maintain consistent contact with the plume. Source declaration does not necessarily have to be done using odor information, as typically odor sources can be sensed via other modalities from short range, but here we propose a solution using no extra sensory apparatus. 2.1 Biological Inspiration As an odor source dissolves into a fluid medium, an odor plume is formed. The turbulent nature of fluid flow typically breaks the plume into isolated packets, areas of relative high concentration surrounded by fluid that contains no odor. The task of odor localization thus becomes one of plume traversal, or following the trail of odor packets upstream to the source. Although the approach of moving slowly and continually sampling odor and flow data to reduce environmental noise is used in nature (starfish) and has been applied to robotic systems [12,13], environmental and behavioral constraints (e.g. significant plume sparseness or meander, time critical performance) can render these systems ineffective. In that case, upon sensing an odor signal, a good policy is to move directly upwind, because a good immediate local indication of source direction under such circumstances is the instantaneous direction of flow [14]. When the odor is no longer present, a good strategy is to perform a local search until it is reacquired, as the location of the previous packet encounter provides the best immediate estimate of where the next will occur. This type of behavior has been observed in moths [15], and its performance has been studied in simulation [8]. The previous work on this algorithm was aimed at studying biology, which limited the sensory and behavioral time scales investigated. When applying these ideas to robots, however, the separation between algorithm and underlying hardware is much more clear, and it no longer makes sense to constrain behavior strictly by sensory response characteristics. Therefore, in this work, key aspects of the search behavior, such as surge duration and casting locality, are treated as algorithm parameters. 2.2 The Spiral Surge Algorithm The basic odor localization algorithm used in this study, Spiral Surge (SS), is shown in Figure 1. It consists of different behaviors related to the three different subtasks. Plume finding is performed by an initial outward spiral search pattern (SpiralGap1). This allows for thorough coverage of the local space if the total search area is large and initial information can be provided by the deployment point (an external 'best guess' as to source location). Alternatively, if no a priori knowledge is available, a spiral with a gap much greater than the arena size (producing essentially straight line search paths) provides an effective, although not optimal [16], search procedure. Future workwill address search efficiency in greater detail. Plume traversal is performed using a type of surge algorithm. When an odor is encountered during spiraling, the robot samples the wind direction and moves upwind for a set distance (StepSize). If during the surge another odor packet is encountered, the robot resets the surge distance but does not resample the wind direction. After the surge distance has been reached, the robot begins a spiral casting behavior, looking for another plume hit. The casting spiral can be tighter than the plume finding spiral (SpiralGap2), as post surge the robot has information about packet density and a thorough local search is a good strategy. If the robot subsequently re-encounters the plume, it will repeat the surging behavior, but if there is no additional plume information for a set amount of time (CastTime), the robot will declare the plume lost and return to the plume finding behavior (with a wider, less local, spiral gap parameter). Source declaration can be accomplished using the fact that a robot performing the plume traversal behavior at the head of a plume will tend to surge into an area where there is no plume information, and then spiral back to the origin of the surge before receiving another odor hit. If the robot keeps track internally of the post spiral inter-hit distances (using odometry, for example, which is sufficient because information must be accurate only locally), a series of small differences can indicate that the robot has ceased progress up the plume, and must therefore be at the source. However, because small inter-hit distances can occur in all parts of the plume, this method is not foolproof, and tuning the significance threshold (SrcDecThresh), as well as the number of observed occurrences before source declaration (SrcDecCount), is required to obtain a particular performance within a given plume. See Table SS uses only binary odor information generated from a single plume sensor. This is motivated partially because this is the most simple and reliable type of information that can be obtained from real hardware. However, due to the highly stochastic nature of turbulent fluid flow and the odor-packet nature of the plume, it is unclear that more complex sensing -- via graded intensity information or larger sensor arrays -- would benefit an agent when flow information is available through other means. 2.3 Collaborative Spiral Surge One way to increase the performance of a robot swarm is collaboration. In particular, if collaboration is obtained with simple explicit communication schemes such as binary signaling, the team performance can be enhanced without losing autonomy or significantly increasing complexity at the individual level. Several simple types of communication can be integrated into the basic SS. Though this issue is not explored in this paper, the effects of communication strategies can change depending on the environment, so communication type should be a tunable system parameter. 2.4 Plume Traversal This paper will focus on the plume traversal subtask because it contains most of the plume related complexity present in the full odor localization task, and due to experimental limitations it is not feasible to study all phases with real robots at this time. To study plume traversal, we place groups of agents within a starting area at the distal end of an odor plume in an enclosed arena. Over repeated trials we measure the time and distance traveled by the whole group until the first agent comes within a given radius of the plume source (Tsf, Dsf). To justify the high density of agents in the plume (which would be unlikely given that in the general problem the plume area is a small percentage of the total search area), we allow communication between the agents that causes all downwind agents (locally determined from previous individual measurement and odometry) to surge toward an agent that has received an odor hit and is initiating its own surge behavior. This provides an attractive force that holds the group together as it traverses the plume. Efficiency for the plume traversal task cannot be defined in the general case. Instead, there are two basic measures of task performance: time and group energy (which can be considered proportional to the sum of the individual distances traveled). Since these measures are physically independent, a composite metric incorporating a particular weighting of these two basic factors can be considered. This metric is an arbitrary weighting of time and distance, which are normalized by the optimum values for the given task (Tmin, Dmin). The form ensures that for any exponent _ and _ greater than 0, the optimal system will achieve a performance of 1, and any that require more time or distance will have a performance less than 1. By choosing specific values for _ and _, the appropriate relationship can be generated for evaluating any particular application. 3 Materials and Methods 3.1 Real Robots We use Moorebots, as shown in Figure 2. The plume traversal arena is 6.7 by 6.7 m, and the robots are 24 cm in diameter. In addition to the basic setup, as described in[17], each robot is equipped with four infra-red range sensors for collision avoidance, a single odor sensor tuned to sense water vapor, and a hot wire anemometer. The odor sensor detects the presence of an airborne substance through a change in the electrical resistance of a chemically sensitive carbon-doped polymer resistor [18].We generate a water plume using a pan of hot water and an array of fans. Mapping the plume using a random walk behavior (see Figure 3a) indicates that the plume is stable. The anemometer is enclosed in a tube which gives it unidirectional sensitivity, which, combined with a scanning behavior, allows the robot to measure wind direction. A wind map of 2102 individual samples averaged spatially is shown in Figure 4a. An overhead camera tracking system, combined with a radio LAN among the robots and an external workstation, is used to log position data during the trials,reposition the robots between trials, and emulate the binary communication signals. Trials of different group size are interleaved and inactive robots are automatically positioned at recharging stations. 3.2 Inherent Odor Localization Task Complexity When studying the performance of distributed robotic systems, it can be useful to model the system using different levels of abstraction. Probabilistic analytic models are ideal, but it can be difficult to formalize all relevant local interactions at the macroscopic level. Less abstract model types include probabilistic numerical models (microscopic-level), non-embodied point simulations, and finally embodied simulations. Successful modeling provides a way of understanding the essential aspects of the system, as well as a significantly decreased evaluation time, which allows a more complete investigation of the system parameter space. In order to demonstrate SS as an odor localizing strategy, we attempted to apply the numerical probabilistic modeling methodology described in [4]. However, we were unsuccessful because that framework is not able to capture the influence of agent trajectory across different functional states. In the previously studied exploration task, agent trajectories were randomized via wall avoidance between state transitions, so the assumptions of the model (that position and heading within each state are random) were approximately correct. In the odor localization task, transitions between areas where plume information is available to areas where there is none do not require an intermediate avoidance procedure. Thus the random position and heading assumptions of the modeling methodology do not hold, and it cannot be successfully applied. Note that it may yet be possible to develop a more sophisticated model that properly incorporates all aspects of the algorithm and dynamics of the environment. The next lower level of investigation is non-embodied point simulation. Again, we attempted to evaluate SS at this level, but we found that the source declaration aspect of the algorithm, a sub-task in which agent density can be elevated around the source, is very sensitive to the interagent repulsion parameters. Since these are intended only to approximate the behavior of the real robots, we could not hope to obtain accurate performance information using non-embodied simulation. 3.3 Embodied Simulation In absence of a functional higher level alternative, we used Webots [19], a 3D sensor-based, kinematic simulator, originally developed for Khepera robots [20], to systematically investigate the performance of SS in simulation. This embodied simulator has previously been shown to generate data that closely matches real Khepera [5,2] and Moorebot [4] experiments, so we were confident that real robot behavior was accurately captured. The physical arena was captured in Webots, as shown in Figure 4b. To properly capture the plume stimulus, we incorporated a series of leaky source 2D plume images generated in a water flume by Philip Roberts and Donald Webster at Georgia Tech. Such 'plume movies', even though they do not capture the influence of the agents on plume dynamics, offer a good approximation to the discretized (packet-like) nature of odor stimulus received in real environments. We scaled the recorded plume data to imitate the average speed and envelope of the real plume data (see Figure 3a and Figure 3b), and tuned the odor sensitivity threshold (higher threshold leads to less odor information) based on performance observed in our real arena. Odor hit frequency differences between the real and simulated maps are due to different polling rates of the respective measurement systems and differences in response bandwidth of the real and simulated sensors. Flow information was taken directly from the real robot data (as shown in Figure 4a) and introduced into the embodied simulations. 4 Results and Discussion 4.1 Real Robots We tested real robot plume traversal performance using two sets of SS parameters and two control experiments. We varied only SpiralGap2 and StepSize because we considered only the plume traversal aspect of the task. Parameter set SS1 represents a non-local search in that its search paths are straight and its surges extend to the boundaries of the arena. SS2 uses a smaller spiral gap and surge length to perform a more local exploration of the arena. Random Odor uses SS2 parameters, and receives odor hits that are generated from the time sequence of SS2 odor hits but are not correlated with robot position in the arena. This control experiment investigates whether an algorithm incorporating precise odor packet location information is more efficient than a blind upwind surging behavior. Random Walk takes straight line paths and random avoidance turns at boundaries (using no odor or flow information) to provide a traversal performance baseline. Specific parameters relating to the real robot tests are listed in Table II. 15 trials of each group size were run for SS1, SS2 and Random Odor, and 30 trials were run for Random Walk due to the high performance variance. Figures 5 and 6 show that for all conditions studied, traversal time decreases with group size while group distance traveled increases. Time and distance are normalized to the minimum values possible for this task.Figure 7 shows that while single robots are generally most efficient in this arena, SS1 gives the best results for each group size, demonstrating successful plume tracing. Random Odor performs worse than SS2 for all group sizes,indicating that location of odor information is an important aspect of the search algorithm. Also, SS2 performs worse than SS1, suggesting that local search is not a good strategy in this small arena where the goal-to-search perimeter ratio is high (i.e., it is likely to find the goal by chance). Note that as _ and _ change, giving more weight to time or energy in the performance function, the values in Figure 7 will tend toward the inverse of the data shown in Figure 5 or Figure 6. In other words, as time becomes more important than energy consumed, larger group sizes will become more efficient, and vice versa. All error bars in the plots represent standard error. 4.2 Webots We successfully reproduced the real robot performance data in Webots, as shown in Figure 7. Data represents 1000 trials per group size. All parameters in Table II apply to the Webots data as well.Because our Webots data closely matches our available real robot data, it is reasonable that further simulated experiments will accurately reflect real world behavior. The main limitations to our real robot experiments thus far are arena size and restriction to the plume traversal subtask, thus in simulation we ran a set of trials involving both the plume finding and plume traversal subtasks in a 25x (area) larger arena. The simulated plume remained the same, the start area remained the same size but was moved out of the plume to a corner of the arena, and both SS algorithms used a SpiralGap1 of 1785 km (producing straight line plume finding search paths) and a CastTime of 96 s. Figure 8 shows that in the larger arena the local search of SS2 is the best strategy. Single robots are no longer the most efficient because the penalty for losing contact with the plume is high. While larger group sizes ensure that the plume is never lost, they also bring higher interference and search overlap as well. Optimal balance for this environment and parameter set is at a group size of 4 for SS2. SS1 performs worse because its non-local search has a higher likelihood of losing the plume across all group sizes. Random Walk performance decreases most drastically, as the probability of encountering the goal by chance is highly dependent on the goal-to-search perimeter ratio. Note that the SS1 and the Random Walk performance curves have optimal values like SS2, but they occur at group sizes above 10. 5 Conclusion In this paper we have described a distributed algorithm for solving the full odor localization task, and shown that group performance can exceed that of a single robot. We have demonstrated that one subtask, plume traversal, can be successfully accomplished by real robots. Furthermore,we have established that an embodied simulator can accurately replicate the real robots results, and shown that it can be a useful tool for exploring system performance.Achievement of near optimal performance on the full odor localization task in the real world will require efficient search of a large parameter space, which will call for the combination of accurate simulation and machinelearning techniques. Acknowledgements We would like to thank O. Holland, S. Kazadi, J. Pugh, R. Enright, L. Van Tol, and A. Lundsten for contributions on various hardware and software aspects of the project. We would also like to thank our collaborators in the DARPAONR Chemical Plume Tracing Program for their valuable input. This work is supported in part by the Center for Neuromorphic Systems Engineering under grant EEC-9402726, by DARPAunder grant DAAK60-97-K-9503,by the ONR under grant N00014-98-1-0821, and by the ARO under MURI grant DAAG55-98-1-0266. A. Hayes is supported by a NSF Graduate Research Fellowship. References [1] E. Bonabeau, M. Dorigo, and G. Therulaz, Swarm Intelligence:From Natural to Artificial Systems, Oxford University Press, New York, US, 1999. [2] A. Martinoli, A. J. Ijspeert, and F. Mondada, “Understandingcollective aggregation mechanisms: From probabilistic modelling to experiments with real robots,” Robotic and Autonomous Systems, vol. 29, pp. 51-63, 1999. [3] O. E. Holland and C. Melhuish, “Stigmergy, self-organization, andsorting in collective robotics,” Artif. Life, vol. 5, pp. 173-202, 1999. [4] A. T. Hayes, A. Martinoli, and R. M. Goodman, “Comparing distributed exploration strategies with simulated and real autonomous robots,” in Proc. of the fifth Int. Symp. on Distributed Autonomous Robotic Systems DARS-2000, L. E. Parker, G. Bekey,and J. Barhen, Eds., Knoxville, Tennessee, October 2000, pp. 261-270, Springer Verlag. [5] A. J. Ijspeert, A. Martinoli, A. Billard, and L. M. Gambardella,“Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment,”Autonomous Robots, vol. 11, no. 3, pp. 149-171, 2001. [6] C. R. Kube and E. Bonabeau, “Cooperative transport by ants androbots,” Robotics and Autonomous Systems, vol. 30, pp. 85-101,2000. [7] R. T. Carde and A. Mafra-Neto, “Effect of pheromone plume structure on moth orientation to pheromone,” in Perspectives on Insect Pheromones. New Frontiers, R. T. Carde and A. K. Minks, Eds., pp. 275-290. Chapman and Hall, N.Y., 1996. [8] J. H. Belanger and M. A. Willis, “Adaptive control of odor guided locomotion: Behavioral flexibility as an antidote to environmental unpredictability,” Adaptive Behavior, vol. 4, pp. 217-253, 1996. [9] U. Bhalla and J. M. Bower, “Multi-day recording from olfactory bulb neurons in awake freely moving rats: Spatial and temporally organized variability in odorant response properties,” J. of Computational Neuroscience, vol. 4, pp. 221-256, 1997. [10] J. Atema, “Eddy chemotaxis and odor landscapes: Exploration of nature with animal sensors,” Bio. Bull., vol.191, pp. 129-138, 1996. [11] M. J. Weissburg, “From odor trails to vortex streets: Chemo and mechanosensory orientation in turbulent and laminar flows,” in Orientation and Communication in Arthropods, M. Lehrer, Ed.Birkhauser, Basel, 1997. [12] T. Nakamoto, H. Ishida, and T. Moriizumi, “A sensing system for odor plumes,” Analytical Chemistry, vol. 71, no. 15, pp. 531A-537A, 1999. [13] S. Kazadi, R. Goodman, D. Tsikata, and H. Lin, “An autonomous water vapor plume tracking robot using passive resistive polymer sensors,” Autonomous Robots, vol. 9, no. 2, pp. 175-188, 2000. [14] C. T. David, J. S. Kennedy, J. S. Ludlow, and J. N. Perry, “A reappraisal of insect flight towards a point source of wind-borne odor,” J. of Chemical Ecology, vol. 8, pp. 1207-1215, 1982. [15] N. J. Vickers and T. C. Baker, “Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths,” Proc. of the Nat. Acad. of Sciences USA, vol.91, pp. 5756-5760, 1994. [16] D.W. Gage, “Randomized search strategies with imperfect sensors,”in Proc. of SPIE Mobile Robots VIII, Boston, September 1993, vol. 2058, pp. 270-279. [17] A. F. T. Winfield and O. E. Holland, “The application of wireless local area network technology to the control of mobile robots,” Microprocessors and Microsystems, vol. 23, pp. 597-607, 2000. [18] M. S. Freund and N. S. Lewis, “A chemically diverse conducting polymer-based electronic nose,” Proc. of the Nat. Acad. of Sciences USA, vol. 92, pp. 2652, 1995. [19] O. Michel, “Webots: Symbiosis between virtual and real mobile robots,” in Proc. of the First Int. Conf. on Virtual Worlds, VW’98,Paris, France, July 1998, pp. 254-263, Springer Verlag. [20] F. Mondada, E. Franzi, and P. Ienne, “Mobile robot miniaturization:A tool for investigation in control algorithms,” in Proc. of the Third Int. Symp. on experimental Robotics ISER-93, T. Yoshikawa and F.Miyazaki, Eds., Kyoto, Japan, 1993, pp. 501-513, Springer Verlag. 中文译文 群机器人嗅觉定位 摘要 这篇论文描述了通过使用群智能的对一群可自动移动的机器人进行嗅觉定位的研究。我们提出了一种一组因子能够比单个因子更有效的解决全局嗅觉定位的分布式算法，随后我们证明了一组真实的机器人在全分布控制的状态下能成功的通过真实的嗅觉流，最后，我们给出了一个具体的模拟器能够够如实的重现现实机器人的实验，并且这个模拟器对于真实世界中嗅觉定位的优化和学习来说也是一个非常有用的工具。 1 引言 这篇论文描述了通过使用群智能的方法对一群可自动移动的机器人进行嗅觉定位的研究。是一种解决分布式问题的可计算模型和行为级的象征。它的灵感来自于社会虫类中的生物实例，目前为止所学习的大多数生物种类，()群智能方法的使用能够通过减少单个个体的复杂性和强调并行性、利用直接或间接的交流、分布式控制来自动的集聚机器人的目标来进行更具活力的任务，这种方法的 个体可以动态的增加好处有三个:第一，从一些到成千个体的可扩展性，第二, 和不需要直接重组织进行移动的灵活性，第三，增加了系统的健壮性，不仅通过了冗余的个体，并且还通过了所设计的最小个体。一些用群智能解决的集聚机器人的方法在文学中也能发现:聚集和分离、探索、附着牵引、协同运输。 最近，生物学和人工嗅觉分类学取得了一些进步，并且嗅觉定位已经和空中的飞蛾和老鼠、水中的龙虾和口脚类动物一样灵敏。生物使用嗅觉来完成很多的任务，包括寻找同物种的其它个体、交流、行为变异、避开肉食动物、寻找食物等。嗅觉，与视觉和听觉感知不同，它是非空间的，它既没有空间的度量也没有空间方向。相反，嗅觉刺激拥有空间和时间的特征，蛇状出发的复杂烟羽能够开辟出一个很大的空间，这暗示着对嗅觉源的定位，遍历超过曲线梯度一级的复杂是必要的。 动物通常是硬件(适应的接收器)、软件(瞬间整合或空间整合)、搜寻策略(本身固有的和基于里程的)的结合来定位嗅觉源。嗅觉定位在本质上是动物与动物间行为不同的行为学问题，一些动物在不同的层次利用不同液体信息(如龙虾)，或者感觉相应的残留物(如蚂蚁)，其它的动物能在3D的空间内追踪相应的气味(蛾)或者使用结合的信息来定位它们的目标(狗)，从一个工程师的观点来说，可移动的机器人使用结合的嗅觉感觉能够加速任务的完成，比如说化学物质遗漏的定位、有危险的荒芜地带的绘制，我们对小型可移动机器人使用嗅觉遍历算法和多感觉(测程法、风力定向、嗅觉)融合来搜索和鉴定嗅觉源的研究十分感兴趣。 这篇论文所描述的案例学习的主要目的有三个，第一，提出了一种一组因子能够比单个因子更有效的解决全局嗅觉定位的分布式算法。第二，我们证明了一组真实的机器人在全分布控制的状态下能成功的通过真实的嗅觉流。第三，给出了一个具体的模拟器能够够如实的重现现实机器人的实验，并且这个模拟器对于真实世界中嗅觉定位的优化和学习来说也是一个非常有用的工具。 2 嗅觉定位问题 在这篇论文中所强调的嗅觉定位的问题如下:尽可能有效的在一个封闭的2D空间内寻找到一个单个的嗅觉源，这种情形可以分解为三个子任务:烟羽发 开始与嗅觉源建立联系，烟羽遍历—遍历嗅觉烟羽到它的嗅觉源，源声明—现— 从嗅觉探测出的特点来决定源在附近的区域，烟羽发现声明了一个基本的搜索任务，随着额外的复杂性，由于烟羽的随机特点，因此一个顺序的搜索不能够保证成功，烟羽的遍历需要跟多具体的行为，即能够向源的方向前进，又能够保持和烟羽的一致联系，源的声明不需要使用烟羽信息，像典型的烟羽源在短范围内能够被其它方式感觉到。但在这儿我们提出了一种不需要使用额外感觉器械的方法。 2.1 生物感知 当一种气味源溶解在液体介质中时，一种气味烟羽就开始形成了，液体流动的湍流状态通常将烟羽分解成不同并隔离的信息包，被液体围绕的相对高集聚密度的区域没有气味，因此，嗅觉定位的任务变成了一个烟羽遍历的问题，或者是跟随着气味信息的踪迹追溯到其源所在。 尽管移动缓慢和不间断的抽取气味和流动数据的信息能够减少了环境噪音的方法在自然界中被使用(如海星)，并且在机器人系统中也已经开始使用，环境和行为的限制(如重要烟羽的缺少或者婉转，时间临界性能)能够弥补这种系统的无效，在那种情形中，根据感觉一种气味信息，一种好的方法能够直接进行逆风的移动，因为一种好的直接定位明了源的方向在这个区域内并且在流动的方向上，当气味不在出现，一种好的策略便是执行一次局部搜索直到重新获得气味，当以前信息包的位置重新发现，它对下一次将要在哪儿发生提供了最好的直接估计，这种类型的行为已经在飞蛾中发现，并且它的表现已经在模拟中进行学习。 前面关于算法的工作主要是针对于生物学习，并且限制了感觉和行为的时间范围调查，当把这种思想运用于机器人时，算法和最底层硬件的分离显现的更加明显，并且它不再需要通过感觉回应特征对相应行为进行严格限制，因此，在本文工作中，重要的方面是对行为方面进行研究，例如出现持久和丢弃定位时，都将其视为算法的参数。 2.2 螺旋涌现算法 本文研究中所使用的基本嗅觉定位算法是螺旋涌现(SS)，如图1所示，它由不同的行为和三个不同的子任务组成， 图1 螺旋涌现嗅觉定位行为 表1 螺旋涌现算法参数 烟羽发现由初始的外层螺旋搜索模式进行执行，如果总的搜索区域太大，这样允许进行局部区域的覆盖，并且初始信息能够被调度点提供(一种外在的“最佳猜测”作为源位置)，可选择的是，如果没有先行信息可用，比场地尺寸(本质上产生了直接的线性搜说路径)更大的螺旋间距将更加有效，尽管不是最佳的搜索程序，但在以后的工作中将更详细强调搜索效率。 烟羽遍历由一种叫涌现算法来执行，当在螺旋期间遇到一种气味信息，机器人对风的方向进行采样，并且逆风移动一段距离，如果在涌现期间，遇到另一个气味包，机器人重新设置出现的源距离，但不需要对风的方向进行重新采集信息，在达到出现的距离以后，机器人开始进行螺旋状的行为，寻找另外的烟羽迹，所投掷的螺旋状可以比发现烟羽的螺旋更紧密，由于前面出现的机器人已经有信息包浓度的信息并且通过局部搜索也是一种好的策略，如果机器人随后重新遇到烟羽，它将重复前面出现的行为，但是在一定的时间内，如果没有新的烟羽信息，机器人将声明烟羽丢失，并且返回到发现烟羽时的行为(通过更宽、更少局部性，螺旋间距参数)。 源的声明可以通过机器人在进行烟羽遍历前，有烟羽的区域部分将涌现到没有烟羽信息的区域，然后在嗅觉到另一个气味前回旋到最初出现的地方，如果机器人继续内在的遍历以前的螺旋距离(使用测程法，例如，只有在局部区域信息才能够充足)，一系列小的差别表明机器人已经停止向烟羽前进，并且肯定在源的地方，但是，因为小的内碰距离能够在有烟羽的任何地方发生，这种方法不是肯定能成功的，并且调整临界值的重要性、在源声明之前所观察到发生的数目，都需要获得一个特别的性能在一个给定的烟羽内，表I为单个螺旋涌现参数的总结。 螺旋涌现仅使用了二进制气味信息来产生一个单个的烟羽感觉器，这样只能部分的被激发，因为这是可从硬件获得的最简单和可靠的信息，但是，由于湍急流动液体的高随机特点和烟羽的气味包性质，使得复杂的感觉变得更加不清楚，通过分级信息或使用更强的感觉阵列，当通过其它的方式使流动的信息变得可行时可能使其中的一个因子更加有效， 2.3 协作的螺旋出现 一种增加机器人群性能的方法是通过协作，特别地，如果协作能够通过二进制信号来进行简单直接的交流，在没有失去自由度和明显的增加单个级别的复杂性的情况下，整个机器人团队的性能能得到很到提高，几种简单地交流类型能够与螺旋涌现整合到一起，尽管这种方法在这一章中不进行更深研究，交流的效果可以通过环境进行改变，因此，交流的类型应该是系统中的一个可调参数。 2.4 烟羽遍历 这篇论文主要专注于烟羽遍历子任务，因为它包含在全局嗅觉定位任务中了大多数烟羽相关的复杂性，并且由于实验环境的限制，在这个时期类，用真实的机器人来学习所有的阶段是不现实的，为了学习烟羽的遍历，在一个封闭的区域内，我们在开始区域中一个气味烟羽的远端安排了一系列的因子，通过反复的实验，我们测量了整组所移动的时间和距离知道第一个因子达到我们所给定的烟羽源的范围内。 为了证明烟羽因子的高密度性(不考虑在通常问题下，烟羽区域只占整个搜索区域中很小的一部分的情况)，我们允许引起顺风(由以前个体单个个体测量和测程来决定)间因子的交流来向已经向得到嗅觉冲击的因子靠近并且初始化自己的涌现现行为，这让机器人在遍历烟羽时集聚在一起时提供了一种很有具吸引力的力量。 烟羽遍历任务的效率不能在通常情况下定义，相反，对任务的性能有两个基本的测量方法:时间和组能量(可认为是单个个体移动距离的和)，因此这种测量是独立于物理设备的，可认为是一种混合公式包含这两种基本因子的权重。 这公式是时间和距离的任意权重，是给定任务优化后的最右值，这种公式格式保证了阶(自己找)都大于,，优化的系统可以实现接近,的性能，如果其中一个需要更多的时间和距离，将使性能小于1，通过对选定具体的值，对评价任何特殊的应用时都可产生相应的关系。 3 实体和方法 3.1 真实机器人 我们使用了Moorebots，如图2所示，烟羽遍历区域是6.7乘6.7，并且机 器人的直接为24cm，加上基本的步骤，如17所描述，每个机器人都安装了4个红外探测器来避免碰撞，一个单一的气味传感器用于调整感觉到水蒸气和热风速。 图2.1Moorebots 在遍历烟羽的区域 嗅觉传感器通过化学敏感的碳掺杂聚合物电阻的变化来发现空气中物质的出现，我们使用扇状的阵列生成了一水状图，使用随机的行为映射来表明烟羽的稳定。 图 3 6(a)为6个真实的机器人执行随机的扩散行为在一个1个小时时间 内所获的烟羽状图，(b)为6个模拟机器人在1个小时内获的的烟羽状图 风速计被装在一个密闭的空间中，没有直接的感觉，因此，与一个扫描行为的结合，使机器人能够测出风的方向，2102个单个样本平均空间的风样图如图4a所示。 前端的摄像机跟踪系统，结合机器人之间的无线局域网和外部工作站，用于在试验位置数据，重新审查之间的机器人，模拟二进制通信信号。组大小不同的试验交错和活动的机器人自动在感知域的位置。 3.2嗅觉定位任务的复杂性 当学习的分布式机器人的性能系统，使用不同的抽象层次模型系统是有用的。概率分析模型很理想，但它是很困难的，使所有有关的局部相互作用在宏观层面的形式化。越少抽象模型类型包括概率数值模型(微观层次)，非体现点模拟，并最终体现的模拟。成功建模提供了一个了解基本方式该系统的各个方面的途径，以及搜索的时间显着下降，这使得一个更完整的参数空间的研究系统。 为了证明一个嗅觉定位的SS策略，我们试图运用数值概率模型方法中描述的建模方法。然而，我们是不成功的，因为该框架通过不同功能状态是无法采集不同的个体轨迹。在先前研究的探索任务，客体的轨迹是随机进入气味源的发散范围避免之间的状态转换，这样的假设模型(个体的位置和朝向是随机的)是比较接近现实的。在嗅觉定位中，局部之间的转换信息是可用的不需要一个中间的规避过程的区域。因此，随机建模的位置和朝向的假设方法不成立，它不能成功应用。请注意，它可能还可能制定一个更复杂的适当结合算法和动态的环境的各个方面的模型。 下一个较低水平的调查不能通过点模拟再现。再次，我们在这个水平试图评估SS，但我们通过这样一个子任务，在个体围绕气味源目标的密度升高，是因为非常敏感的interagent斥力参数发现，算法方面对目标搜索存在问题。由于这些仅近似真实的机器人的行为，我们使用不可再现的模拟没有希望得到准确的性能信息。 3.3 仿真再现 在功能的更高层次的选择的情况下，我们使用webots [19]，基于三维运动 ，系统地探讨了SS表现模拟。传感器的模拟器，最初开发Khepera机器人[20] 以前这体现模拟器显示生成数据，最接近真实Khepera[5,2]和Moorebot [4]的实验，所以我们有信心准确地捕捉真正的机器人行为。 Webots捕获物理上的搜索空间范围，如图4b所示。要正确地捕捉烟羽刺激，我们合并的一系列气味源的2维烟羽图像是由在Georgia 理工学院的Philip Roberts和Donald Webster在水槽中产生的烟羽。这种“烟羽搜索轨迹图像”，虽然他们没有捕获到个体对烟羽动态的影响，但是提供了在一个被真实环境所接纳的模拟器中，很自然地离散分布。我们测量烟羽数据记录以模仿真实的平均搜索速度和群烟羽的数据(见图3a和图3b)，并调整了气味的灵敏度阈值(门槛较高，导致至少根据我们观察到的性能气味信息)在真实搜索环境中。在真实的和模拟的地图，气味命中之间的频率差异是由于各自的测量系统不同的轮询频率的差异，真实和模拟传感器的响应带宽的差异。流信息直接取自真正的机器人数据(图4a所示)，并把该数据加入模拟器中。 4 结果和讨论 4.1 真实机器人 我们测试了真正的机器人烟羽遍历性能，使用两套SS的参数和两个对照实验。我们仅仅使SpiralGap2和StepSize变化，因为我们认为只有烟羽遍历方面的任务。参数集SS1代表非本地搜索，其搜索路径是直线，其激增地浪涌般地延伸到边界。SS2的使用一个较小的螺旋差距，浪涌长度进行更多本地的勘探。随机气味源使用SS2的参数，并接收从SS2的气味源命中 的时间序列所产生的气味，而不是在烟羽范围与机器人相关的点击。这种控制实验研究纳入精确的气味包的位置信息的算法是否是超过盲目螺旋行为更有效率。随机游动的直线路径在边界(使用无异味或流信息)提供了遍历的性能基线和随机避免曲折。真正的机器人测试有关的具体参数列于表二。每个组的15项试验运行SS1，SS2和随机的气味，和30个由于高性能方差的随机游动的运行试验。 图5和图6显示，所有研究的条件，穿越时间与群体规模减少，而组的搜索距离增加。对这任务来说可能正常时间和距离的最低值。 图7显示SS1给出了每个组的大小最好的结果，证明成功的烟羽跟踪而单一的机器人一般都是在这个感知范围内最有效的。随机气味源比SS2更糟对所有的群大小来说，表明气味信息的位置是搜索算法的一个重要方面。此外，SS2的 执行比SS1差，表明本地搜索不是一个良好的手段在一个小的感知范围内，在该范围内目标搜索周边信号是比较高的(即，它可能是偶然发现目标)。请注意，当的变化，在性能方程中的时间或能量给予更多的权重，如在图7中的值，与图5或图6所示的数据趋势相反。换句话说，随着时间的比消耗的能量变得更加重要，群体规模较大将变得更有效率，反之亦然。在绘图中所有的错误条表示错误。 4.2 Webots 如图7所示，我们成功地再现了在Webots机器人的性能数据。数据代表了每个种群大小1000个试验组的大小。表二中的所有参数同样是。 作为我们well.Because Webots数据充分符合我们提供真正的机器人数据，这是合理的，进一步的模拟实验将准确地反映现实世界的行为。对我们真正的机器人实验来说，主要限制是感知域大小和烟羽遍历子任务的限制，因此，我们在模拟中我们运行一套即在25倍于原来的搜索域更大的搜索空间对发现烟羽也包括烟羽遍历子任务有效的策略。模拟柱保持不变，初始区域的大小保持不变，但是移动出了烟羽的范围到了一个角落里，并且两个使用了1785公里(产生直线的搜索烟羽路径)的SpiralGap1的SS算法消耗时间96秒。图8显示了SS2的本地搜索，在更大的搜索空间上是最好的策略。单个机器人不再是最有效的，因为烟羽失去联系的代价更高。虽然较大的群体规模，确保烟羽永远不会丢失，也带来更高的干扰和搜索重叠。在群大小为4的SS2参数是这种环境和参数设置的最佳平衡。SS1执行差，因为它的非局部搜索有更高的可能性失去烟羽。随机游动性能的最大幅度下降，遇到偶然的目标的概率高度依赖的搜索目标信号的范围。请注意，SS1和随机游动的性能曲线有像SS2的最佳值，但他们在10组以上的大小的情况下发生。 5 总结 本文描述为解决嗅觉局部化任务的分布式算法，并显示了群机器人的性能可以超过单个机器人。我们已经证明，一个子任务，烟羽遍历，可以在真正的机器人成功完成。此外，我们已经建立了一个模拟器，它可以精确地再现真实机器人的结果，并表明，它是一个有用的工具，对于探索在现实世界中完全嗅觉局部化系统接近最佳性能。 在现实世界中的气味本地化的任务充分的接近最佳的性能将要求高效的搜索参数对于更大搜索空间，它将调用为准确的模拟和机器学习的组合技术。 致谢 A. 我们要感谢O. Holland, S. Kazadi, J. Pugh, R.Enright, L. Van Tol, 和Lundsten在各种硬件和软件方面的贡献。我们也想感谢我们的合作者，在DARPAONR Chemical Plume Tracing Program的宝贵意见。这项工作受到Center for Neuromorphic Systems Engineeringunder grant EEC-9402726、DARPA under grant DAAK60-97-K-9503、the ONR under grant N00014-98-1-0821、 the ARO under MURI grant DAAG55-98-1-0266..的部分支持。 A. Hayes是由美国国家科学基金会研究生研究奖学金支持。