Multi-robot cooperation in the MARTHA project

Alami, Rachid; Fleury, Sara; Herrb, Matthieu; Ingrand, Félix; Robert, Frédéric

doi:10.1109/100.667325

Cited by 204 publications

(94 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5.2: Satellite, UM Translog, and CrissCross. Since they represent three different styles of hybrid domain model building and include two former benchmarks 3 , we believe that they are well-suited for our purpose.…”

Section: Domains and Problemsmentioning

confidence: 99%

“…But the normalization terms are worth a second thought: if we assume that the domain model is concise, then the size of the set of task expressions is nondecreasing on any path in the refinement space. 3 If we furthermore assume that in the long run the number of plan modifications decreases, not to speak of the preferred modification class instances, then the normalized measure will necessarily decrease for more developed plans. These considerations indicate that an inverted preference may be a reasonable subsequent strategy.…”

Section: Flaw-and Modification-based Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

View full text Add to dashboard Cite

PrefacePlanning and scheduling are well-established disciplines in the field of Artificial Intelligence. They provide flexibility, robustness, and effectiveness to complex software systems in a variety of application areas. While planning is the process of finding a course of action that achieves a goal or performs a specified task, scheduling deals with the assignment of resources and time to given activities, taking into account resource restrictions and temporal dependencies. In other words, planning focuses on reasoning about causal structures and identifying the necessary actions for achieving a specific goal; scheduling concentrates on resource consumption and production for optimizing a coherent parameter assignment of a plan. As successful these techniques clearly are, the actual demands of complex, real-world applications go far beyond the potential of these single methods, however. They require an adequate integration of these problem solving methods as well as a combination of different planning and scheduling paradigms. Particularly important are abstraction-based, hierarchical approaches because of both their expressive knowledge representation and their efficiency in finding solutions. Current state-of-the-art systems rarely address the question of method integration; isolated approaches do so only in ad hoc implementations and mostly lack a proper formal basis.This thesis presents a formal framework for plan and schedule generation based on a well-founded conceptualization of refinement planning: An abstract problem specification is transformed stepwise into a concrete, executable solution. In each refinement step, plan deficiencies identify faulty or under-developed parts of the plan, which in turn triggers the generation of transformation operators that try to resolve them. All involved entities are explicitly represented and therefore transparent to the framework. This property allows for two novel aspects of our approach: First, any planning and scheduling methodology can be functionally decomposed and mapped on the deficiency announcement and plan transformation generation principle, and second, the framework allows for an explicit declaration of planning strategies. We first investigate the flexibility of the extremely modular system design by instantiating the framework in a variety of system configurations including classical partial-order causal-link (POCL) planning, hierarchical task-network (HTN) planning, and classical scheduling.As a key feature, the presented approach provides a formally integrated treatment of action and state abstraction, thus naturally combining causality-focused reasoning with hierarchical, procedure-oriented methods. While the use of procedural knowledge allows to rely on well-known, predefined solutions to planning problems, the non-hierarchical methods provide the flexibility to come up with non-standard solutions and to complete under-specified problem instances, respectively. The resulting technique of hybrid planning is capable of constructing a plan's caus...

show abstract

Section: Domains and Problemsmentioning

confidence: 99%

Section: Flaw-and Modification-based Strategiesmentioning

confidence: 99%

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

View full text Add to dashboard Cite

show abstract

“…During the run, the robot will generate a plan to accomplish these goals, handle unexpected situations, manage failures and thus execute the plan in a robust manner. The approach developed in [1] has the objective to control and manage a group of autonomous mobile robots for transshipment tasks in a decentralized and cooperative way. Further AI-based cooperative systems were proposed by [7,6,5].…”

Section: Related Workmentioning

confidence: 99%

RoboEarth Action Recipe Execution

Marco¹,

Tenorth²,

Häußermann³

et al. 2013

Frontiers of Intelligent Autonomous Systems

View full text Add to dashboard Cite

The ability of reusing existing task execution plans is an important step towards autonomous behavior. Today, the reuse of sophisticated services allowing robots to act autonomous is usually limited to identical robot platforms and to very similar application scenarios. The approach presented in this paper proposes a way to mitigate this limitation by storing and reusing task plans on a global accessible database. We describe the task execution engine for the RoboEarth project [26] to demonstrate its ability to execute tasks in a flexible and reliable way.

show abstract

“…Capabilities that have been of particular emphasis include task planning [57], fault tolerance [82], swarm control [79], human design of mission plans [77], role assignment [88,65,80], multi-robot path planning [89,76,70,93], traffic control [84], formation generation [58,96,97], formation keeping [60,91], target tracking [83] and target search [75].…”

Section: Swarm Intelligence -Overviewmentioning

confidence: 99%

Logical Foundations for Rule-Based Systems

2006

Studies in Computational Intelligence

View full text Add to dashboard Cite

Multi-robot cooperation in the MARTHA project

Cited by 204 publications

References 20 publications

Hybrid Planning and Scheduling

Hybrid Planning and Scheduling

RoboEarth Action Recipe Execution

Logical Foundations for Rule-Based Systems

Contact Info

Product

Resources

About