به نام خدا Multi Robot System Mehrdad bibak

Multi-Robot Systems

Biological Inspirations Communication Architectures, task allocation, and control Localization, mapping, and exploration Object transport and manipulation Motion coordination Reconfigurable robots

Multi-Robot Systems Biological Inspirations –The most common application of this knowledge is in the use of the simple local control rules of various biological societies ، particularly ants, bees, and birds، to the development of similar behaviors in cooperative robot systems. –Nearly all of the work in cooperative mobile robotics began after the introduction of the new robotics paradigm of behavior based control –Competition in multi-robot systems, such as that found in higher animals including humans, is being studied in domains such as multi-robot soccer.

Multi-Robot Systems Communication –implicit and explicit –implicit communication occurs as a side-effect of other actions. –explicit communication is a specific act designed solely to convey information to other robots on the team. –More recent work in multi-robot communication has focused on representations of languages and the grounding of these representations in the physical world

Multi-Robot Systems Architectures, task allocation, and control –A great deal of research in distributed robotics has focused on the development of architectures, task planning capabilities, and control. –Three a rchitectures (for Example): Linear Parallel of linear Tree structured

Multi-Robot Systems

Localization, mapping, and exploration –Almost all of the work has been aimed at 2D environments. –most of this research took an existing algorithm developed for single robot mapping,localization, or exploration, and extended it to multiple robots. Object transport and manipulation –Enabling multiple robots to cooperatively carry, push, or manipulate common objects has been a long-standing, yet difficult, goal of multi- robot systems. Motion coordination –An advancement in the analysis of motion coordination in multi-robot teams is the development of provable theorems that characterize the cooperative performance of team formations under certain conditions. Reconfigurable robots

Multi-Robot Systems

Cooperation: situation in which several robots operate together to perform some global task that either cannot be achieved by a single robot, or whose execution can be improved by using more than one robot, thus obtaining higher performances. Awareness: the property of a robot in the MRS to have knowledge of the existence of the other members of the system. Coordination: cooperation in which the actions performed by each robotic robot take into account the actions executed by the other robotic robots in such a way that the whole ends up being a coherent and high-performance operation. Multi-Robot Systems

Centralization: the organization of a system having a robotic agent (a leader) that is in charge of organizing the work of the other robots; the leader is involved in the decisional process for the whole team, while the other members act according to the directions of the leader. Distribution: the organization of a system composed by robotic agents which are completely autonomous in the decisional process with respect to each other; in this class of systems a leader does not exist. Strong centralization: centralization in which decisions are taken by a leader that remains the same during the entire mission duration. Multi-Robot Systems

Weak centralization: centralization in which more then one robot is allowed to become a leader during the mission. Direct communication: communication that makes use of some hard-ware on board dedicated device to signal something that the other team members can understand.. Indirect communication MRS social deliberation: a system behavior that allows the team to cope with the environmental changes by providing a strategy that can be adopted to reorganize the team members' tasks, so as to use all the resources available to the system itself to effectively achieve the global goal. MRS reactivity: a system behavior in which every single robot in the team copes with the environmental changes by providing a specific solution to reorganize its own task in order to fulfill the accomplishment of its originally assigned goal. Multi-Robot Systems

1 Task Decomposition Methods

Task Analysis A technique for analyzing existing tasks by observation. Doesn’t require understanding of Users’ goals, just what they do. But because of that its harder to apply to the design of a new system. –Good for training materials and documentation Task Decomposition Methods

Task Analysis: 3 Approaches Tasks decomposition: looks at how a task is split into subtasks and the order in which these are performed. Knowledge-based techniques: what do users need to know about the objects and actions involved in a task? How is that knowledge organized? Entity-relation-based analysis: an object- based approach, identify objects, relationships and actions. Task Decomposition Methods

Task Decomposition Break the task into subtasks: Hierarchical Task Analysis (HTA): –Organize tasks into a hierarchy –Include ordering constraints –Looks something like logic programming (PROLOG) Clean house Get vacuum cleaner Clean rooms Empty dust bag Put everything away Clean hall Clean living room Clean bedrooms Task Decomposition Methods

Task Decomposition 0. In order to clean house 1.Get vacuum cleaner out 2.Fix attachment 3.Clean the rooms 3.1 Clean the hall 3.2 Clean the living rooms 3.3 Clean the bedrooms 4.Empty the dust bag 5.Put the vacuum cleaner away Plan 0: Do in that order Plan 3: Do any of 3.1, 3.2, and 3.3 in any order depending on which rooms need cleaning Task Decomposition Methods

2

Task Decomposition A divide-and-conquer approach can reduce the complexity of a task: smaller subtasks require less capable agents and fewer resources The system must decide among alternative decompositions, if available Successful task decomposition depends greatly on a designer’s choice of operators The decomposition process must consider the resources and capabilities of the robots. Also, there might be interactions among the subtasks and conflicts among the robots Task Decomposition Methods

Inherent (free!): the representation of the problem contains its decomposition, as in an AND-OR graph System designer (human does it): decomposition is programmed during implementation. (There are few principles for automatically decomposing tasks) Hierarchical planning (robots do it): decomposition again depends heavily on task and operator representation Task Decomposition Methods

Task Decomposition Examples Spatial decomposition by information source or decision point: Functional decomposition by expertise: Pediatrician Internist Psychologist Neurologist Cardiologist Agent 1 Agent 2 Agent 3 Task Decomposition Methods

Task Distribution Criteria Avoid overloading critical resources Assign tasks to robots with matching capabilities Make an robot with a wide view assign tasks to other robots Assign overlapping responsibilities to robots to achieve coherence Assign highly interdependent tasks to robots in spatial or semantic proximity. This minimizes communication and synchronization costs Reassign tasks if necessary for completing urgent tasks Task Decomposition Methods

Task Distribution Mechanisms Market mechanisms: tasks are matched to robots by generalized agreement or mutual selection (analogous to pricing commodities) Contract net: announce, bid, and award cycles Multiagent planning: planning robots have the responsibility for task assignment Organizational structure: robots have fixed responsibilities for particular tasks Recursive allocation: responsible agent may further decompose task and allocate the resultant subtasks Task Decomposition Methods

3

Task Sharing and Result Sharing Three stages Problem decomposition Sub-problem solution Solution synthesis Problem decomposition Iteratively hierarchically decompose overall problem into smaller subproblems until robot can solve them Different decomposition levels  different levels of abstraction Task Decomposition Methods

Problem decomposition Important: Decomposition granularity.decomposed problem until sub- problems are at the level of programming language commands  too fine grained.  problems with synthesis, management overhead etc. outweigh decomposition advantages Sub-problem solution Sharing of information during sub-problem solution Task Sharing and Result Sharing Solution synthesis may also be hierarchical (respecting different levels of abstraction) Task Decomposition Methods

Coordination Coordination: Managing inter-dependencies between the activities of robots Examples of inter dependencies: 2 people want to go through the same door I cannot proceed with my work until you have given your ok I make you a copy of an interesting paper without being asked to do so Inter dependencies can be positive or negative Positive relationships (benefits for at least one of the robots while leaving others at least as happy (  pareto-optimality) may be requested or non requested

Coordination inter- dependencies positive negative requested (explicit) non- requested (implicit) resource incompatibility consumable resource non-consumable resource

Coordination Three types of non-requested interdependencies: Action-equality-interdependence: Both robots need to have action a done  one of them can do it Consequence-interdependence: Actions of one robot‘s plan have side effect of achieving other robot‘s goal Favour-interdependence: Actions of one robot‘s plan have side effect of partially achieving other robot‘s goal (positively contributing to it) 3 iterated stages: each robot decides about his goals, creates local plan robots exchange plans to determine interdependencies\\ robots alter local plans to achieve better coordination

Black boarding (Strong centralized system) Knowledge sharing (Weak centralized system) Communicative language (Distributed system ) –Same language –Different language –language Structure of language Type of language communication Methods

Message –direct exchange –common language –conversation - sequences of messages Blackboard robot robot A (Sender) robot A (Sender) robot B (Receiver) robot B (Receiver) Message Blackboard information available for all no direct communication simple architecture

communication Methods Consider: –performative = request content = “the door is closed” speech act = “please close the door” –performative = inform content = “the door is closed” speech act = “the door is closed!” –performative = inquire content = “the door is closed” speech act = “is the door closed?”

(Request :Sendersender1 :Receiverreceiver1 :LanguageKIF/FIPA :Ontology Ontology1 :Reply-With1 :Contentcontent1 communication Methods

We now consider robot communication languages (ACLs) — standard formats for the exchange of messages The best known ACL is KQML, developed by the ARPA knowledge sharing initiative KQML is comprised of two parts: –the knowledge query and manipulation language (KQML) –the knowledge interchange format (KIF)

communication Methods KQML is an ‘outer’ language, that defines various acceptable ‘communicative verbs’, or performatives Example performatives: –ask-if (‘is it true that... ’) –perform (‘please perform the following action... ’) –tell (‘it is true that... ’) –reply (‘the answer is... ’) KIF is a language for expressing message content

communication Methods “The temperature of m1 is 83 Celsius”: (= (temperature m1) (scalar 83 Celsius)) “An object is a bachelor if the object is a man and is not married”: (defrelation bachelor (?x) := (and (man ?x) (not (married ?x)))) “Any individual with the property of being a person also has the property of being a mammal”: (defrelation person (?x) :=> (mammal ?x))

communication Methods In order to be able to communicate, robots must have agreed on a common set of terms A formal specification of a set of terms is known as an ontology The knowledge sharing effort has associated with it a large effort at defining common ontologies — software tools like monolingual for this purpose Example KQML/KIF dialogue… A to B: (ask-if (> (size chip1) (size chip2))) B to A: (reply true) B to A: (inform (= (size chip1) 20)) B to A: (inform (= (size chip2) 18))