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Towards Blended Planning and Acting using Behavior Trees. A Reactive, Safe and Fault Tolerant Approach.
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.ORCID-id: 0000-0003-0289-7424
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-7714-928X
(engelsk)Artikkel i tidsskrift (Fagfellevurdert) Submitted
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Identifikatorer
URN: urn:nbn:se:kth:diva-202923OAI: oai:DiVA.org:kth-202923DiVA, id: diva2:1078933
Merknad

QCR 20170307

Tilgjengelig fra: 2017-03-07 Laget: 2017-03-07 Sist oppdatert: 2017-03-07bibliografisk kontrollert
Inngår i avhandling
1. Behavior Trees in Robotics
Åpne denne publikasjonen i ny fane eller vindu >>Behavior Trees in Robotics
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Behavior Trees (BTs) are a Control Architecture (CA) that was invented in the video game industry, for controlling non-player characters. In this thesis we investigate the possibilities of using BTs for controlling autonomous robots, from a theoretical as well as practical standpoint. The next generation of robots will need to work, not only in the structured assembly lines of factories, but also in the unpredictable and dynamic environments of homes, shops, and other places where the space is shared with humans, and with different and possibly conflicting objectives. The nature of these environments makes it impossible to first compute the long sequence of actions needed to complete a task, and then blindly execute these actions. One way of addressing this problem is to perform a complete re-planning once a deviation is detected. Another way is to include feedback in the plan, and invoke additional incremental planning only when outside the scope of the feedback built into the plan. However, the feasibility of the latter option depends on the choice of CA, which thereby impacts the way the robot deals with unpredictable environments. In this thesis we address the problem of analyzing BTs as a novel CA for robots. The philosophy of BTs is to create control policies that are both modular and reactive. Modular in the sense that control policies can be separated and recombined, and reactive in the sense that they efficiently respond to events that were not predicted, either caused by external agents, or by unexpected outcomes of robot's own actions. Firstly, we propose a new functional formulation of BTs that allows us to mathematically analyze key system properties using standard tools from robot control theory. In particular we analyze whenever a BT is safe, in terms of avoiding particular parts of the state space; and robust, in terms of having a large domain of operation. This formulation also allows us to compare BTs with other commonly used CAs such as Finite State Machines (FSMs); the Subsumption Architecture; Sequential Behavior Compositions; Decision Trees; AND-OR Trees; and Teleo-Reactive Programs. Then we propose a framework to systematically analyze the efficiency and reliability of a given BT, in terms of expected time to completion and success probability. By including these performance measures in a user defined objective function, we can optimize the order of different fallback options in a given BT for minimizing such function. Finally we show the advantages of using BTs within an Automated Planning framework. In particular we show how to synthesize a policy that is reactive, modular, safe, and fault tolerant with two different approaches: model-based (using planning), and model-free (using learning).

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2017. s. 63
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2017:07
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-202926 (URN)978-91-7729-283-8 (ISBN)
Disputas
2017-04-11, F3, Lindstedtsvägen 26, Stockholm, 09:30 (engelsk)
Opponent
Veileder
Merknad

QC 20170308

Tilgjengelig fra: 2017-03-08 Laget: 2017-03-07 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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