Multi-Robot Coordination. Chapter 11

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1 Multi-Robot Coordination Chapter 11

2 Objectives To understand some of the problems being studied with multiple robots To understand the challenges involved with coordinating robots To investigate a simple behaviour-based selforganization strategy for a common application To investigate a simple communication strategy 11-2

3 What s in Here? Multi-Robot Coordination: Purpose and Issues Advantages and Disadvantages of Multiple Robots Types of Research and Disciplines Role of Learning The Foraging Problem What is it? Explicit Distribution Implicit Distribution Improvement in Distribution Hierarchical Communication What is it? Various Schemes - Random - Sequential -Vecr - Focused Averaging 11-3

4 Multi-Robot Coordination: Purpose and Issues 11-4

5 Multiple Robots There are advantages when using multiple robots: + larger range of task domains + greater efficiency + improved system performance + fault lerance + lower economic cost + ease of development??? + distributed sensing and action 11-5

6 Multiple Robots There are also disadvantages / challenges: - performance depends on issues involving interaction between robots - interactions complicate development - difficult model group behaviors from p down (i.e., centralized control) when environment is unknown and/or dynamic - sensor and/or physical interference - need lots of batteries! 11-6

7 Research 5 major themes of robot group research: Group control architecture - decentralization and differentiation Resource conflict resolution - e.g., space sharing Origin of cooperation - i.e, genetically-determined social behavior or interaction-based cooperative behavior Learning - e.g., control parameter tuning for desired cooperation Geometric problem solving - e.g., geometric pattern formation A A typical typical research research paper paper will will focus focus on on only only one one theme theme (or (or aspect) aspect) of of group group robotics. robotics. 11-7

8 Research What kinds of problems have been studied: Multi-robot path planning Traffic control Formation generation, keeping and control Target tracking Multi-robot docking Box-pushing Foraging Multi-robot soccer Exploration and localization Transport 11-8

9 Disciplines There are three disciplines that are most critical the development of robotic agents: Distributed Artificial Intelligence - distributed Problem Solving or Multi-Agent Systems - considers how tasks can be divided among robots which share knowledge about problem and evolving solutions. Distributed Systems - focus on distributed control addressing deadlock, messagepassing, resource allocation etc Biology - botm-up approach where robots follow simple reactive rules - Interaction between robots results in complex emergent behavior 11-9

10 Learning and Adapting Robots perform for certain period of time without human supervision in order solve problem must be able deal with dynamic changes in environment and their own performance capabilities Learning, evolution and adaptation allow robot improve its likelihood of survival and its task performance in environment: adaptation how a robot learns by making adjustments learning helps one robot adapt environment evolution helps many robots adapt environment 11-10

11 Evolution vs. Learning Evolution: process of selective reproduction and substitution based on the existence of a distributed population of vehicles does not perform well when certain environmental changes occur that are different from evolved solutions Learning: a set of modifications taking place within each individual during its own lifetime often takes place during an initial phase when task performance is considered less important control policy used that gives reasonable performance robot team gradually improves over time

12 Overview Summary There are many aspects of multi-robot coordination Robots that perform well gether in one kind of environment will perform poorly in others. To be useful, multi-robot strategies must: be designed and fine-tuned for particular applications explicitly / implicitly distribute the work among the robots consider both sensory and environmental interference from other robots be able operate under unexpected situations be cost-effective 11-12

13 This Course Multi-Robot coordination strategies is a huge pic o much cover in this course We will consider: self-organization for simple foraging applications hierarchical communication focus coverage We will look a simulated results: robots will be reactive and use instinctive behaviors analyze the performance over time combine different types of robots 11-13

14 The Foraging Problem 11-14

15 Foraging Consider a common problem studied in robotic colonies, foraging: gathering/collecting items - possibly bringing them some specific location(s) (e.g., particular room) or general locations(s) (e.g., outer walls). there are many variations of this problem We will consider a specific instance: robots can detect when it finds an item and can push it some location (or pick it up and drop it off). robots will be encoded with a fixed, instinctive behavior and thus will not learn how forage

16 Foraging Consider allowing robots move randomly in an environment with no cooperation. Robots must find forage items (e.g., when passing over them) and bring them the boundaries. Robots may collide, which may interrupt the forage procedure of a robot. Eventually, over time, each forage item will be found by some robot: 11-16

17 Foraging As more robots are used, the speed of forage completion increases. The performance decreases when the forage items are not evenly distributed. % Completion Foraging Performance Over Time - Random Movement with Evenly Spread Forage Items Increasing Time ====> 5 robots 10 robots 25 robots 50 robots 100 robots 200 robots 1 robot Foraging Performance Over Time - Random Movement with Clustered Forage Items 90 this is because robots are robots 10 robots 25 robots not directed wards forage % Completion robots 100 robots 200 robots 1 robot items, only finding them by chance Increasing Time ====> 11-17

18 Foraging Intuitively, performance can be improved by: reducing collisions (or interference) between robots preventing robots from traveling over the same areas directing robots wards clusters of forage items The obvious way of reducing collisions and preventing duplicate travel is distribute robots by explicitly assigning each one a particular area in the environment in which forage. environment broken down in equal-sized areas which are assigned individual robots 11-18

19 Foraging Explicit Distribution This strategy has advantages: + ensure even distribution of robots - good when items be foraged are evenly distributed randomly + minimizes sensor interference and physical collisions between robots and disadvantages: - requires robots know and maintain specific positions - requires knowledge of environment - expensive sensors?? (e.g., GPS) - expensive computation (e.g., position estimation) - can be inefficient if forage items are clustered 11-19

20 Foraging Explicit Distribution A simple way of determining the foraging areas for each robot is base the regions on the dual graph: Recursively divide dual graph in half until number of regions matches the number of robots: Each Each robot robot remains remains in in its its own own designated designated area. area

21 Foraging Explicit Distribution There are multiple ways split the dual graph by finding an edge that evenly splits: links # of dual graph links - simple and fast, assuming a nice triangulation area area covered by dual graph triangles - best if robots need perform coverage algorithms or searching with uniform distribution of foraging items. perimeter perimeters of dual graph triangles - good if robots are patrol outer boundaries of their environment 11-21

22 Foraging Explicit Distribution Performance (i.e., speed of forage completion) is highly dependant on shape of environment and location of forage items. With With forage forage items items evenly evenly distributed, distributed, robots robots work work effectively effectively in in near near optimal optimal configuration, configuration, provided provided that that robots robots do do not not have have leave leave their their environment environment complete complete the the task. task. With With clustered clustered forage forage items, items, most most robots robots become become useless useless if if forced forced remain remain in in a a particular particular area. area

23 Foraging Implicit Distribution Clearly, fixing the locations of each robot may not be the best choice if: the distribution of forage items is not known be random and evenly distributed the robots must travel outside their areas complete the forage task (i.e., deliver their payload). A compromise is hard-code specific behavioral rules in the robots that minimize their collisions and attempt keep them distributed

24 Foraging Implicit Distribution Consider robots with omni-directional beacons which are detectable from other nearby robots: robots avoid moving wards nearby beacons intuitively, robots should remain separated/distributed When other robot detected When other robot detected within sensor range, robot within sensor range, robot moves in opposite direction. moves in opposite direction. With multiple beacons, With multiple beacons, either move away in either move away in combined vecr combined vecr direction or away from direction or away from strongest signal. strongest signal. Although robots may still reencounter other robots during Although robots may still reencounter other robots during their movements, in general their movements, in general they remain distributed. they remain distributed

25 Foraging Comparison A comparison of these schemes shows that: for evenly spread forage items there is no significant advantage of either scheme in terms of forage completion time and the simple random movement seems do well. for clustered forage items the fixed area scheme performs poorly with few robots and the repel scheme performs better Scheme Comparison - 25/12/4 Robots Evenly Spread Forage Items Repel scheme favorable Repel scheme favorable since performs well AND since performs well AND minimizes robot contact. minimizes robot contact. Scheme Comparison - 25/12/4 Robots Clustered Forage Items % Completion Increasing Time ====> Repel (12 robots) Fixed (12 robots) Random (12 robots) Repel (4 robots) Fixed (4 robots) Random (4 robots) Repel (25 robots) Fixed (25 robots) Random (25 robots) % Completion Increasing Time ====> Repel (12 robots) Fixed (12 robots) Random (12 robots) Repel (4 robots) Fixed (4 robots) Random (4 robots) Repel (25 robots) Fixed (25 robots) Random (25 robots) 11-25

26 Foraging Improvement A more significant improvement can be made if something is known about the forage items (e.g., they are clustered). can signal other robots when item is encountered leave signal on until: - fixed amount of time elapses - other robots come nearby can either wait stationary or continue moving Robot turns on beacon Robot turns on beacon when item is found. when item is found. Robots within beacon s Robots within beacon s range will travel ward range will travel ward nearest beacon. nearest beacon. Robots outside of Robots outside of beacon s range will beacon s range will continue moving continue moving randomly. randomly

27 Foraging Improvement Consider five beacon attraction schemes: Always On - beacon is always on, robot keeps moving Timed Out Stationary - beacon on for fixed time, robot waits stationary until beacon timeout Timed Out Moving - beacon on for fixed time, robot keeps moving Until Near - beacon on until robot nearby, robot waits stationary until another robot comes nearby Until Near or Timed Out - beacon on for fixed time, robot waits stationary until beacon timeout or until another robot comes nearby Robots Robots may may get get in in a a deadlock deadlock situation. situation

28 Foraging Improvement Here is the basic idea behind the attraction code: REPEAT { int desireddirection = direction of closest/strongest beacon signal; IF (desireddirection!= null) { boolean collisiondetected = read front collision sensors; } IF (collsiondetected) { Turn away from obstacle; } Turn wards desireddirection } ELSE { wander (i.e., move forward or turn randomly) } IF (a forage item is found) { Turn on my beacon; Wait for XXX seconds; Turn off my beacon; } Add this code for the Add this code for the TimedOutStationary scheme TimedOutStationary scheme IF (a forage item is found) { Turn on beacon; counter = 5000; //msec } IF (--counter == 0) { Turn off beacon; } Depends Depends on on sensor. sensor. The The desired desired direction direction may may be be that that of of the the strongest strongest signal signal (if (if many many beacons beacons sensors sensors are are mounted mounted in in a circular circular fashion), fashion), or or may may be be a a direction direction representing representing a a combination combination of of multiple multiple signals. signals. Usually, Usually, the the direction direction will will be be one one of of fixed fixed directions directions around around the the robot. robot. Add Add this this code code instead instead for for the the TimedOutMoving TimedOutMoving scheme scheme 11-28

29 Foraging Improvement What about performance? Movement Movement while while beacon beacon is is on, on, is is best best strategy. strategy. Up Up 3x 3x faster faster than than random random movement movement here. here. % Completion Scheme Comparison - 12 Robots Clustered Forage Items Attract & Move Until Timeout Attract & Still Until Timeout No Attract (Random) Attracting Attracting all all the the time, time, can can be be worse worse than than moving moving randomly. randomly. Increasing Time ====> Attract Until Near or Timeout Attract Always Attraction Attraction with with timed timed beacon beacon always always improves improves performance. performance

30 Foraging Improvement Even when varying the number of robots, the attraction scheme performs well: Scheme Comparison - 4 Robots Clustered Forage Items Scheme Comparison Robots Clustered Forage Items % Completion Attract & Move Until Timeout Attract Until Near or Timeout Attract & Still Until Timeout Attract Always No Attract (Random) % Completion Attract & Move Until Timeout Attract Until Near or Timeout Attract & Still Until Timeout Attract Always No Attract (Random) Increasing Time ====> Increasing Time ====> The The time time scales scales between between the the graphs graphs is is different, different, in in order order accentuate accentuate the the differences differences in in the the schemes. schemes

31 Foraging Improvement Of course, in non-clustered environments, the attraction scheme performance degrades and actually reduces efficiency over random scheme: Recall Recall that that repel repel scheme scheme works works best best in in unclustered unclustered environments. environments. % Completion Scheme Comparison - 12 Robots Evenly Spread Out Forage Items Attract & Move Until Timeout Attract & Still Until Timeout No Attract (Random) Increasing Time ====> Attract Until Near or Timeout Attract Always Repel Always Attraction Attraction schemes schemes perform perform worse worse than than simple simple random random movement. movement

32 Foraging Improvement What about environments with both clustered items AND spread out items? Scheme Comparison - 12 Robots Clustered AND Evenly Spread Out Forage Items Attract & Move Until Timeout Attract & Still Until Timeout No Attract (Random) Attract Until Near or Timeout Attract Always Repel Always % Completion Increasing Time ====> Performance Performance is is near near random random but but provides provides only only a a small small improvement. improvement

33 Foraging Improvement Can mix various kinds of robots: e.g., some attract, some repel Scheme Comparison - 12 Robots Clustered AND Evenly Spread Out Forage Items % Completion No Attract (Random) Random AND Attract Move Random AND Attract Near Attract & Move Until Timeout Attract Until Near or Timeout Increasing Time ====> Combining Combining 6 6 random random with with 6 6 attract attract robots robots performs performs best best despite despite type type of of environment environment!!!! Scheme Comparison - 12 Robots Evenly Spread Out Forage Items No Attract (Random) Random AND Attract Near Attract Until Near or Timeout Random AND Attract Move Attract & Move Until Timeout Scheme Comparison - 12 Robots Clustered Forage Items No Attract (Random) Random AND Attract Near Attract Until Near or Timeout Random AND Attract Move Attract & Move Until Timeout % Completion Increasing Time ====> % Completion Increasing Time ====> Certainly, Certainly, less less robots robots are are attracted attracted cluster, cluster, so so in in clustered clustered environment, environment, there there is is a a performance performance tradeoff tradeoff when when combining combining robot robot types. types

34 Other Similar Problems Similar attraction/repel strategies can be implemented for other problem scenarios such as coordinated mapping, searching, patrolling, floor cleaning etc. same principles apply, but results may differ. As seen, using heterogeneous groups (i.e., mixing different kinds of robots) may prove be the most robust and efficient solution overall. Experimentation helps tweak solutions: wanna do an honours project or a Master s thesis? 11-34

35 Hierarchical Communication 11-35

36 Communication Another important issue with respect multi-robot algorithms has deal with communications: do the robots need communicate (e.g., send data)? is there any advantage doing so? how often should they communicate? should there be unlimited communication between robots or should there be restrictions (i.e., groups)? We will look here at one aspect of using hierarchical communication

37 Hierarchical Communication Consider robots organized in a hierarchy: Each robot belongs a group and all group members can communicate a group leader via wireless communication. The leaders are also grouped gether with a higher level leader which they communicate. High level leader communicates High level leader communicates with 3 middle level leaders. with 3 middle level leaders. 5 5 Low Low level level worker worker robots robots communicate communicate with with their their leader leader as as long long as as they they are are within within communication communication range. range

38 Hierarchical Communication Within a hierarchy, worker robots must always remain within communication range: allows data be transmitted leader (e.g., map data) allows leader send commands at any time (e.g., new directions and updated task assignments) allows quick docking for battery recharging, working in shifts etc an warning buffer zone should be used inform worker turn back. Communication Communication range range limit limit Almost Almost out out of of range, range, needs needs turn turn back. back. Warning Warning zone zone 11-38

39 Hierarchical Communication A main issue with botm-up behavior-based programming is that only local information (i.e., information from a robot s own sensors) is usually available. With such a hierarchical scheme, lower level robots can be given global knowledge of the environment and/or of task completion. should provide benefit over no-communication schemes for more complex problems can allow steering of robots accomplish task more efficiently

40 Hierarchical Schemes Consider robots moving randomly cover a simple environment: good enough investigate the general problem of robot coverage under various communication schemes. more efficient schemes can be used cover environment and techniques can be tweaked each application. random coverage actually performs well over time. Random coverage of Random coverage of 4 robots over time. 4 robots over time

41 Hierarchical Schemes Now consider a leader with 4 worker robots: worker robots move randomly within leader s communication range: Robots Robots all all move move randomly randomly within within communication communication range. range. we can restrict worker movements fixed or variable-sized wedges/quadrants: Robots Robots may may cross cross over over in in other other quadrants, quadrants, but but treat treat it it as as out out of of range. range

42 Hierarchical Schemes Leader must also move in order cover whole environment properly. Consider various leader movement schemes: - Random: move in random direction - Sequential: move along a fixed path in sequence - Vecr: move in direction wards quadrant that had most out of safe zone occurrences - Toward Average: vecr scheme with added pull wards leader s average location - Away From Average: vecr scheme with added push away from leader s average location 11-42

43 Hierarchical Scheme - Sequential The basic sequential scheme works as follows: Leader moves slower than workers (e.g., 1/10 th of speed) 5 4 Leader heads wards next location in some sequence (e.g., along a predetermined path) Leader may remain at each location for a while or leave immediately. Timeout may be used if location is not reached within certain time limit Leader Leader moves moves along along path, path, while while workers workers move move randomly randomly within within the the safe safe range. range. Good if need unload workers, then Good if need unload workers, then reload and transport new site. reload and transport new site. Necessary Necessary in in order order avoid avoid getting getting stuck stuck behind behind obstacles. obstacles

44 Hierarchical Scheme - Vecr The basic vecr scheme works as follows: Leader moves slower than workers (e.g., 1/10 th of speed) Each time worker leaves safe range, a counter is incremented Leader computes 4 vecrs facing 4 quadrants with magnitudes equal these counters Leader moves - in combined direction of these vecrs, or - in direction of strongest magnitude vecr 5 2 Leader Leader moves moves in in combined combined vecr vecr direction. direction

45 Hierarchical Scheme Average Vecr The average vecr scheme works as follows: Same 4 vecrs as Vecr scheme are used Leader also keeps track of its overall average position Leader computes 1 new vecr facing either wards or away from the global average according its current location Leader includes this new vecr in its computations Magnitude of global average vecr set scalar multiple of maximum of other vecrs (e.g., 2x, 1x, ½x, etc ) 5 2 Average Average vecr, vecr, set set 1x 1x maximum maximum

46 Hierarchical Results Results from the Random movement scheme: Leader Leader moves moves randomly, randomly, while while workers workers stay stay nearby. nearby. This This strategy strategy may may not not reach reach all all parts parts of of the the environment. environment. Combined Paths of Workers Too much Too much clustering clustering on edges. on edges. Leader s Path 11-46

47 Hierarchical Results Results from the 4-Point Sequential scheme: Leader Leader moves moves predefined predefined locations locations (in (in this this case, case, 4 4 corners ), corners ), while while workers workers stay stay nearby. nearby. Nice coverage in general, but corners Nice coverage in general, but corners are missed. How can we fix this? are missed. How can we fix this? Combined Paths of Workers Leader s Path 11-47

48 Hierarchical Results Results from the Vecr scheme: Performs Performs ok, ok, but but not not better better than than without without communication. communication. Leader Leader moves moves ward ward direction direction of of worker worker that that was was out out of of safe safe range range the the most most times. times. Combined Paths of Workers Leader s Path 11-48

49 Hierarchical Results Results from the Toward Average Vecr scheme: good for applications such as focused searching in which the likelihood of success is localized about some known location. Can Can keep keep less less focus focus Can Can really really focus focus attention attention of of workers workers around around a a specific specific area. area. allow allow outward outward expansion. expansion. Can Can form form search search rings rings by by varying varying magnitude magnitude over over time. time. 2x Attraction Magnitude 1x Attraction Magnitude ½ x Attraction Magnitude 11-49

50 Hierarchical Results Results from Away From Average Vecr scheme: good for applications such as mapping force exploration away from previously mapped areas. Can Can really really focus focus attention attention of of workers workers away away from from a a specific specific area. area. Can Can keep keep less less focus focus allow allow inward inward expansion. expansion. Can Can use use a a hint hint of of focus focus allow allow more more randomness. randomness. 2x Repel Magnitude 1x Repel Magnitude ½ x Repel Magnitude 11-50

51 Hierarchical Results Results in environments with obstacles: Good Good overall overall coverage, coverage, but but does does not not consider consider obstacles obstacles as as different. different. Can Can provide provide a a better better coverage coverage around around obstacles obstacles resulting resulting in in more more accurate accurate mapping. mapping. Can Can provide provide a a coverage coverage more more focused focused along along path path (in (in this this case case around around outer outer obstacle obstacle cluster). cluster). No Communication Vecr Scheme Sequential Scheme 11-51

52 Summary You should now understand: The issues involved with coordinating multiple robots How produce self-organization using simple behaviors The simple foraging problem and how improve performance in various ways How provide simple hierarchical communication focus multi-robot coverage

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