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E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS RoboCup: An Application Domain for Distributed Planning and Sensoring in Multi-robot Systems.

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Presentation on theme: "E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS RoboCup: An Application Domain for Distributed Planning and Sensoring in Multi-robot Systems."— Presentation transcript:

1 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS RoboCup: An Application Domain for Distributed Planning and Sensoring in Multi-robot Systems Enrico Pagello President of the International IAS-Society IAS-Lab Intelligent Autonomous Systems The University of Padua

2 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Presentation Outline What a Cooperative Multi-Robot System should be T. Arai, E. Pagello, L. Parker. Editorial: Advances in Multi-Robot Systems. IEEE/Trans. On R&A, Vol. 18, No. 5, pp , October 2002 Scientific perspective in RoboCup with respect to Cooperation Research on RoboCup at IAS-Lab, The University of Padua Distributed Sensoring: An Omnidirectional distributed vision sensor E. Menegatti, A. Scarpa, D. Massarin, E. Ros, E. Pagello: Omnidirectional Distributed Vision System for a Team of Heterogenueous Robots. Proc. of IEEE Workshop on Omnidirectional Vision (Omnivis’03), Praga June 2003 E. Menegatti, A. Pretto, and E. Pagello Testing Omnidirectional Vision-based Monte- Carlo Localization under Occlusion. Proc. Of IROS-2004, Sendai (Japan), Sept 29 - Oct 2, 2004 Cooperative Robotics: An Hybrid Architecture a MSL Team A. D’Angelo, E. Menegatti, and E. Pagello: How a cooperative behavior can emerge from a robot team. Proc. of DARS’04, Toulouse June 2004

3 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Why Multi-Robot Systems (MRS) have been so successful ? In challenging application domains, MRS can often deal with tasks that are difficult, if not impossible, to be accomplished by an individual robot. A team of robots may provide redundancy and contribute cooperatively to solve the assigned task, or they may perform the assigned task in a more reliable, faster, or cheaper way beyond what is possible with single robots.

4 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS What a Cooperative Multi-Robot System is ? Cooperative Robotics research field is so new that no topic can be considered mature Early research goes to Cellular Robotics by [Fukuda, IECON 1987] and Cyclic Swarm by [Beni, Intelligent Control 1988] Multi-Robot Motion Planning by [Arai, IROS 1989] ACTRESS Architecture by [Asama, IROS 1989] [Dudek, Autonomous Robots 1996] and [Cao, Autonomous Robots 1997] gave a taxonomy In [Arai, Pagello, & Parker, IEEE/Trans. 2002] we identify several primary research areas

5 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Research roots for Cooperative Multi-Robot Systems Cooperative mobile robotics research began after the new behavior-based control paradigm Brooks 1986, Arkin 1990 Since behavior-based paradigm is rooted in biological inspirations, many researchers found it instructive to examine the social characteristics of insects and animals The most common application is using simple local control rules of various biological societies, like ants, bees, and birds, for similar behaviors in MRS MRS can flock, disperse, aggregate, forage, and follow trails, etc.

6 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS New and interesting research issues The dynamics of ecosystems has been applied to MRS to demonstrate Emergent Cooperation Cooperation in higher animals, such as wolf packs, has generated significant study in Predator-Prey Systems Pursuit policies relay expected capture times to the speed and intelligence of the evaders and the sensing capabilties of the pursuers Competition in MRS, such as in higher animals including humans, is being studied in domains such as multi-robot soccer.

7 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Inherently cooperative tasks A particular challenging domain for MRS is the one whose tasks are inherently cooperative, that is, tasks in which the utility of the action of one robot is dependent upon teammates’ current actions Inherently cooperative tasks cannot decomposed into independent sub-tasks to be solved by a DARS Team success throughout task execution is measured by the by the combined actions of the robot team, rather than by individual actions More recently identified biological topics of relevance are: Imitation in higher animals to learn new behaviors Physical Interconnectivity by insects such as ants, to enable collective navigation over challenging terrains How to maintain Communication in a distributed animal society

8 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Communication versus Cooperation Communication issue in MRS started since the inception of Distributed Autonomous Robots Systems (DARS) research. Distinctions between Implicit and Explicit Communication are usually made: Implicit communication occurs as a side-effect of other actions, or “through the world” Explicit communication is a specific act designed solely to convey information to other robots on the team. Communication affects the performance of MRS in a variety of tasks even a small amount of information can lead to great benefit The challenge is to maintain a reliable communication even when connections between robots may change dynamically and unexpectedly setting up and maintaining distributed network

9 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Architecture and Task Planning Research in DARS has focused on the development of architectures, task planning capabilities, and control addressing the issues of: action selection heterogeneity versus homogeneity of robots achieving coherence amidst team actions resolving conflicts, etc. Each architecture focuses on providing a specific type of DARS capability: fault tolerance swarm control role assignment, etc.

10 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Architecture and Task Planning, Localization and Mapping Research in DARS has focused on the development of architectures and task planning capabilities, where each architecture focuses on providing a specific type of distributed capability Initially, most of the research took an existing algorithm developed for single robot mapping, localization, or exploration, and extended it to MRS [Fox et al., Autonomous Robots 2000] took advantage of a MRS to improve positioning accuracy beyond single robot to develop a colaborative multi-robot exploration Only more recently, researchers have developed new algorithms that are fundamentally distributed, to take advantage from MRS

11 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Middle-size League Building, maintaining, and programming a team of fully autonomous robots High speed moving (>2m/s) Large field (12m X 8 m) Sensing the environment Cooperation abilities RoboCup Soccer : The oldest RoboCup standard problem

12 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Middle-size League: progresses from 1997 to 2003 USC (USA) - Osaka Univ. (Japan) Nagoya 1997 Isfahan Univ (Iran) - AIS (Germany) Padua 2003 RoboCup Soccer : From simple moves towards complex actions

13 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Middle-size League: RoboCup2003 : Vision and Localization Vision is still a key research issue in MSL All teams used color information Half of teams use shape detection Even less can make edge detection Auto-color calibration is a hot topic, especially to relax lightning condition Robot Self-Localization is mainly based on Visual Landmarks Most teams detect corner posts Half of teams detects also field lines Several teams use statistical methods

14 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Middle-size League: RoboCup2003 : Control Architectures One half of teams use reactive control architectures (behavior based robotics) One third of teams use their own architectures like: Dual Dynamics, two-level FSMs, Fuzzy Approaches, etc. Several teams develops advanced robot skills using learning Only a few teams extends reactive motion control with path planners based mainly on potential field methods or similar

15 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Research on RoboCup IAS-Lab, Dept. of Information Engineering, The University of Padua Soccer-robot design ODVS (Omnidirectional Distributed Vision System) MonteCarlo Localization using omni-vision Coordinated behaviors

16 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Evolving the Artisti Veneti Team First platform for MSL was designed on 1998 over a Pioneeer1 base Second and third platforms evolved from a Pioneer1 to a Pioneer2 base Third platform is a Golem robot We shifted from 2-wheeled robot, with a directional camera, towards omnidrive and omnivision platforms Fourth platform ehnance the circular movement of original goalkeeper

17 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Omnidirectional Sensor Convex Mirror Perspective camera Perspex Cylinder (support) Camera Mirror

18 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS How to design a mirror Mirror profile construction Pin Hole Vertex Y X Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 x y P Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Pin Hole Vertex D Min D Max X Y d Max d1d1 D1D1 Made by F. Nori at IAS-Lab

19 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Our robot mirrors Mirror’s three parts: Measurement Mirror Marker Mirror Proximity Mirror Mirror Profile The task determines the mirror profile

20 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS A mirror designed for AIS – Fraunhofer Institut (Germany) n Three-parts mirror n Tailored on their mobile robot n Satisfing customer requirements

21 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Requirements and profile In the case of Soccer Robots Requirements and profile For Goalie: Locate the ball Identify the markers See the defended goal For Attacker: Locate the ball Identify the markers See both goals Lighter mirror

22 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Heterogeneous Robots Characteristics: Chassis shaped for omnidirectional vision Mirror profile designed for the robot’s task Mirror Camera

23 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Heterogeneous Vision Systems Peripheral vision Foveal vision

24 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Heterogeneous Vision Systems OVA’s view PVA’s view

25 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Features and Events for Omnivision Events: A new edge A disapearing edge 180° apart Two edges 180° apart Two pairs od edges 180° apart Two pairs od edges 180° apart Features: Vertical edges

26 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Omnidirectional Vision and Mapping P2 P4P3 P5P1 It simplifies data interpretation : – Discriminate b/t “turns” and “travels” – Simplify “Exploring around the block”

27 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Experimental Results Correct tracking of edges Recognition of actions Calculation of the turn angle The path segmentation

28 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Single Robot Mapping Strategy Use an omnidirectional vision sensor Detect topologically meaningful features in the environment Use Spatial Semantic Hierarchy of Kuipers (SSH) Build a topological map Use the map to explore the environment E. Menegatti, E. Pagello, M. Write Using Omnidirectional Vision within the Spatial Semantic Hierarchy IEEE/ICRA2002, Washington, May 2002

29 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Multi-robot mapping strategy Every robot builds its own local map When two robots can see each other, they share their local maps by matching their current views: Identifying the objects seen by both robots Estimating their relative distance and orientation If the match is successful, they transmit their own local map to the teammate Each robot connects this new local map to its local map

30 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Some hints Every robot carries on an independent exploration by using use a misanthropy robot strategy i.e. Follow a direction of exploration that increases the distance from the visible teammates Use redundacy of the observers and observation to improve the map Exploit the heterogeneity of the robots more deeply in tasks too expensive (or not achievable) for homogeneous robots Use maps of non previoulsy met robots to navigate. The bridge is the common starting location.

31 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS ODVS for Navigation We realised a network of smart uncalibrated sensors able to learn how to navigate a blind service robot in an office like environment The sensors learn by observing the robot motion. The first stage is supervised, then the knowledge is propagated autonomously exploiting the overlapping field of view of the sensors VA1 VA2 E. Menegatti, E. Pagello, T. Minato, T. Nakamura, H. Ishiguro “Toward knowledge propagation in an omnidirectional distributed vision system” Proc. of 1st Int. Workshop on Advances in Service Robotics (ASER'03), Bardolino (Italy), March 2003

32 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Implicit Communication VA1 learns its own mapping VA1 moves the robot in the field of view of VA2 VA2 observes the robot VA2 receives from VA1 the motor commands sent to the robot VA2 trains its own neural nets to build its own mapping VA1 VA2

33 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Monte Carlo Localisation (MCL) as a very successful approach Applying MCL to omnidirectional vision used as a range finder An experimentally generated sensor model The fusion of sensor data for pose likelyhood calculation A global localization experiment in a RoboCup Environment Robustness to occlusion An application to a non-roboCup Environment E. Menegatti, A. Pretto, E. Pagello A New Omnidirectional Vision Sensor for Monte-Carlo Localization Proc. of 8th RoboCup Int. Symposium, Lisbon (Portugal), July 2004 Why Monte-Carlo Localization

34 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS MCL (Monte Carlo Localisation) in one page MCL is a probabilistic technique to estimate the robot’s positions from the odometric and sensor data We calculate the probability density of robot positions (the belief) by a set of weighted samples The samples are localisation hypothesis When the robot moves, everytime a new image is processed, the samples are moved in accordance with the motion model To every sample is associated a weight proportional to the probablity that the robot is occuying that position When the robot grasps new data, the sample weights are updated according to the sensor model At every step a resampling eliminates the less probable positions

35 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Our approach to MCL Starting from the work of [Kröse, IVC 2001] and [Burgard, ICRA 2002], we realised an omnidirectional image-based Monte Carlo Localisation system for a large office environment [Menegatti, RAS 2004] We decided to port a similar approach in RoboCup, but image-based localisation is not suited due to: (i) many occlusions (ii) an high dynamical environment (iii) high computational costs for processing the whole image Previous works in RoboCup implemented MCL using complex method for landmark or feature detection, and need to cope with dynamic occlusions [Utz, RoboCup-IV 2001], [Enderle, IAS2000] We fell back on range-scanner, like [Fox, JAIR 1999][Thrun, AI 2000]

36 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Our omnidirectional enhanced range finder We detect colour transitions of interest: G- W, G -Y, G - Blue We detect occlusion: G - Black

37 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Probability distribution of the robot’s pose The scan of every colour transition of interest (here Green-White) gives a probability distribution in the whole field. Black dots = high probability, White dots = low probability Note the symmetry in the environment

38 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Sensor Model.1 - Calculating p(o|l) p(o|l) is the probability to have a the scan o at the location l o i is the measurement along the single ray i of the scan Omni-Scan: One scan per colour transition of interest Every scan has 60 rays (one every 6°) Every ray has one receptor every 4 cm from 10 cm to 4 meters When a transition is found the ray is not searched anymore {i = 1:60}

39 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Expected and Real Scans

40 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Sensor Model.2 – estimating p(o i |l) Taking 2000 images in different positions in the field For every ray of the 2000 scans Computing the actual distance of the colour transition (here Green-White) Estimating the distance of the colour transition with the vision software Running the Expectation Maximisation (EM) to fit the experimental data separately for every colour transition Expected Distance

41 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Sensor Model.3 –Results The resulting probability density calculated for every colour transition is the sum of three components: 1. Erlang distribution (accounting for image noise and imperfect colour quantization) 2. Gaussian distribution centered around the expected distance 3. Discrete density (accounting for missing the transition) Erlang Gaussian Discrete

42 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Combining the three probability distributions Probability distribution for the green-white ToI Probability distribution for the green-blueToI Probability distribution for the green-yellow ToI Resulting Probability distribution for the robot’s pose

43 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Global Localisation Step 0 Step 4 Step 6 Step 18

44 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Sensor Occlusion.1

45 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Sensor Occlusion.2 Our system is able to recognise occlusion by other robots as a Green-Black ToI along a ray These rays are labeled as FAKE_RAY (   and discarded from the calculation of p(o|l) We called this process ray discrimination Our system scans with less rays (so less information), but keeps the usable information and avoids using expensive algorithm as distance filters.

46 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS We need uniformly colored surfaces, clear color gaps, and uniform light Red floor, white walls, and gray furnitures New color transitions: Red - White, Red - Gray The omnidirectional image is scanned with 60 rays, one every 6 degrees Sperimentation at University Building

47 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS The ideal scan was different from the real one: Robot shadow Mirror deformation Error in color detection near the door In the probability map of the environment, there are dark zones everywhere the probability to have the observation is higher: All cornered zones are darker The samples closer to the real pose have a higher weight Ideal scans and probabilities in real environments

48 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Extending the limits of the sensorial horizon of the single agent The first step: using omnidirectional vision (RoboCup is an example of this) But, RoboCup proved omnidirectional vision is not enough for highly dynamic environments: cannot see occluded objects cannot see very distant objects To realise a Distributed Vision System we need to share information between the agents of a team

49 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Omnidirectional Distributed Vision System (ODVS) Requirements: Robots’ only sensor: omnidirectional vision Robots’ only sensor: omnidirectional vision No use of external computer No use of external computer Every robot shares its measures Every robot shares its measures Every robot fuses all measures received Every robot fuses all measures received by teammates by teammates Measures can refer to different instants Measures can refer to different instants in time in time Tracking multiple moving objects in highly dynamic environments by sharing the information gathered by every single robot by sharing the information gathered by every single robot

50 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Enhancing the ODVS by fusing multiple observations E. Menegatti, A. Scarpa, D. Massarin, E. Ros, E. Pagello Omnidirectional Distributed Vision System for a Team of Heterogenueous Robots Proc. of IEEE Workshop on Omnidirectional Vision (Omnivis’03), Praga June 2003 Fusing Multiple Observations from Single Measurements

51 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Inspiring Works Stroupe et al. [ICRA 2001] Perspective cameras They fused measurements made at the same instant in time No tracking, just recognition Gutmann et al. [IROS 2001] Laser Range Finders + Perspective cameras External Global Sensor Integrator Robot uses external information only for unseen objects

52 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Problems for ODVS Our Robots are heterogeneous: Omni-sensors are different On-board processing power is different Robots’ platforms are different The robot need to share: the same spatial frame of reference the same temporal frame of reference The system must be robust to failure of single robots

53 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Single Sensor Architecture of the Perception Module

54 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Single Measures A 2D Gaussian is associated to every measure The Gaussian represents the probability that The Gaussian represents the probability that the object is actually located at that point the object is actually located at that point Gaussian widths are determined Gaussian widths are determined experimentally for every single robot experimentally for every single robot To share the measurements with other robots: Measure is transformed in the absolute reference frame of play field Measure is transformed in the absolute reference frame of play field A time stamp is associated to every measure A time stamp is associated to every measure

55 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Fusing Multiple Observations (1) Measures come from: the vision system of the single robot the vision system of the teammates Measures can refer to: Different objects The same object (this is the most frequent case because of omnidirectional vision) They are processed in the same way: They are fused using a Kalman filter They are stored in ‘tracks’ Multiple tracks allowed for single object (Multi-modal distribution)

56 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Mean Object Position Associated Variance Fusing Multiple Observations (2) Two measurements (i.e. two Gaussians) are fused with a Kalman filter:

57 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Tracks management Every new measure is compared with existing tracks: If compatible the measure is added to the track If NOT compatible a new track is created When a track is not updated the associated variance increase Over certain threshold the track is deleted We allows more tracks for the same object Real position assumed to be the one of smallest variance

58 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Measures from Different Robots Problems: 1. sharing the same spatial frame of reference 2. sharing the same temporal frame of reference 3. Trusting teammates 4. Managing ‘old’’ measurements Adopted solutions: 1. Robust self-localisation thanks to omni-vision 2. Internal clock synchronised via Network Time Protocol (NTP) 3. Variance of measures from teammates are doubled 4. The state of the object is recalculated

59 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Old Measurements Old measurements cannot be thrown away For example: Very slow vision system reporting very accurate measurements Image processing time

60 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Experiment (1) Ball moving between steady robots

61 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Experiment (2) Moving ball and ‘blind robot’

62 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Experiment (3) Kidnapped ball

63 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Discussion We implemented an Omnidirectional Distributed Vision System The system is robust to failure of the single robots The system exploits: the heterogenity of the sensors The redundancy of the observations We presented experiments in real game scenarios The system requires fine tuning of the parameters: Variance associate to every measure Rate of growth of variance when track not updated Variance of teammates’ observations

64 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS A Hybrid Architecture for MRS We suggest to use an Hybrid architecture where the Deliberative part and the Reactive part can take mutual advantages. We introduced Robot Schemas at the low level, as building blocks to grow-up complex behaviors from simple ones, according to Arbib and Arkin : Behaviors are chunks of basic knowledge of how to act and perceive. Each behavior is implemented with a schema composed by a motor schema, representing the physical activities a perceptual schema which includes the sensing

65 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS The Perceptual/Motor Schema At each level, the primitive control component is a behavior built by perceptual and motor schemas only. The lower reactive level uses only information coming from sensors, and feeds the motors with appropriate commands. It can elaborate on some perceptual patterns generated by other individual robots, both opponents and temmates.

66 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS An Abstract Architecture Compound behaviors appear only at higher level, when they may receive more structured information about the environment. Only the higher deliberative levels refer to cooperative capabilities that any robot could exhibit as a teammate, while a cooperative behavior is going to emerge.

67 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS The Layered Levels of Control By releasing a behavior, we fire an activation-inhibition mechanism, built on some given evaluation condition rule, at some level of abstraction. Simple Behaviors like defendArea, or carryBall, are implemented as motor schemas accessing directly the robot effectors. Basic Behaviors, like playDefensive, and chaseBall, are obtained by simply appending two perceptual schemas seeBall and haveBall. playDefensive : seeBall --> defendArea chaseBall : haveBall --> carryBall Since a primitive behavior results in appending just one perceptual schema to one motor schema, at the reactive level we obtain sensori-motor coordinations

68 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Software architecture: ArtiFACT (Artisti Fuzzy Agents Control Toolkit) We designed a new hybrid deliberative reactive architecture. The classic deliberative paradigm (Sense-Reason-Act) has been evolved reinforcing reactive behaviors. A direct link between sense and act has been introduced to speed-up the reactive response of the robot Thus, deliberative conditions can be bypassed for certain inputs which need more reactive behaviors

69 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS A Functional Architecture The architecture of each single robot shows An inner loop, for close feedback, An outer looop, for high level reasoning. To allow cooperation with teammates, two sensorial sources can input asynchronously both Environment constraints (the “Ruler”) Information about teammates (the “Teamplay”)

70 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS On Role Allocation in RoboCup Inspired by Stone and Veloso’s pioneering work, many teams employ role-based coordination, in which robots can take on different static roles within the team Although it would be possible to statically assign roles once forever, most teams switched to dynamic role allocation, by solving an iterated assignement problem, where the current allocation is re-evaluated periodically 10 times for each second Given n robots, n prioritized (weighted) single-robot roles, and some estimates of how well each robot can be expected to play each role, assign robots to roles so as to maximize the overall expected performance Gerkey and Mataric [Springer Book on RoboCup2004] showed that this technique is an instance of the canonical Greedy algorithm for Optimization theory

71 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS RoboCup Team solutions adopted RoboCup role allocation problem is similar to task allocation problem for MRS in order to cooperatively achieve the goal, where a time-extended role concept replace that of a transient task CS Friburg Team used a distributed role allocation mechanism in which two robots may exchange roles only if both want to do it, both moving to a higher-utility role for themselves. ART Team, as well as early, Artisti Veneti Team, ordered the roles in a descending priority, and then assigned each to the available robot with the highest utility.

72 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Utility Functions Multi-robot role allocation is a dynamic decision problem, that varies in time, according to the environmental changes, Utility concept rely on the fact that each individual robot can somehow internally estimate the value (i.e. the cost) of executing an action In RoboCup it is common to compute utility as the weighted sum of factors like distance to target, distance to ball, defence-offense coonfigurations, etc. The computation is affected by sensor noise, general uncertainties, and environmental changes Given the utility value Uij of each robot i for each role j, find the highest utility Uij, assign robot i to role j, and iterate

73 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Dynamic role assignment in practice We developed an enhanced reactive approach starting from behavior-based hand-coded software Dynamic role assignments among attacker, supporter and defender, were managed by considering collision avoidance issues and competitive behaviors

74 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Coordinating the Master/Supporter Roles Consider the coordination between two robots carrying the ball towards the opponent’s goal: We may indentify a Master Role and a Supporter Role Roles can be played at different responsibility levels: Can be >>> Assume >>> Acquire >>> Advocate Ball assignments depend on Ball Possesses HaveBall condition allows to discriminate which robot is really carrying the ball It is an Environment constraint acting as a kind of Macroparameter, evaluated by different teammates It allows to synchronize the activation of a new cooperation pattern

75 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Notifying Roles Roles can be switched provided a notification is exchanged between teammates A notification implies a communication between teammates based on a first-notified/first-advocated basis A notify(Role) rule is: Supporter (mate) -->> reply (role, mate) Master (mate) --> request (role, mate) Environment Rules require that a Master role must be advocated, whereas a Supporter role should be acquired. haveBall and notify (Role) are the two allowed asynchronous communication from outside for a single robot

76 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Constructing Clamping Behaviors A role is switched from acquire to advocate, or from assume to acquire, provided a notification is made to its teammate Two complex Clamping Behaviors for Master and Supporter can be constructed from notify (x) and haveBall (z) The Master robot shows a chase_ball behavior haveBall (me) & not haveBall (mate) -->> acquire (Master) Acquire (Master) & Notify (Master) -->> advocate (Master) The Supporter robot shows an approach_ball behavior Not acquire (Master) & canBe (Supporter) -->> assume (Supporter) Assume (Supporter) & Notify (Supporter) -->> acquire (Supporter) The robot chasing the ball suggests a teammate to become supporter by advocating a master role, and forcing the other robot to acquire a supporter role by approaching the ball.

77 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS Single Robot Architecture and effect on coordination Conditions are defined as fuzzy functions. A value is returned depending on how strongly the condition is met Team coordination is obtained by incorporating some conditions depending on messages coming from other robots, when the condition is evaluated

78 E. Pagello, RoboCup: Distributed Planning and Sensoring in MRS The Artisti Veneti Team


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