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  • 1. Christalin, B.
    et al.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Murray, R. M.
    Synthesis of reactive control protocols for switch electrical power systems for commercial application with safety specifications2017In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, IEEE, 2017, article id 7849873Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for the reactive synthesis of fault-tolerant optimal control protocols for a finite deterministic discrete event system subject to safety specifications. A Deterministic Finite State Machine (DFSM) and Behavior Tree (BT) were used to model the system. The synthesis procedure involves formulating the policy problem as a shortest path dynamic programming problem. The procedure evaluates all possible states when applied to the DFSM, or over all possible actions when applied to the BT. The resulting strategy minimizes the number of actions performed to meet operational objectives without violating safety conditions. The effectiveness of the procedure on DFSMs and BTs is demonstrated through three examples of switched electrical power systems for commercial application and analyzed using run-time complexity analysis. The results demonstrated that for large order system BTs provided a tractable model to synthesize an optimal control policy.

  • 2.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Behavior Trees in Robotics2017Doctoral thesis, comprehensive summary (Other academic)
    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).

  • 3.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Almeid, Diogo
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Towards Blended Planning and Acting using Behavior Trees. A Reactive, Safe and Fault Tolerant Approach.Article in journal (Refereed)
  • 4.
    Colledanchise, Michele
    et al.
    Istituto Italiano di Tecnologia - IIT, Genoa, Italy.
    Almeida, Diogo
    Ögren, Petter
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Towards Blended Reactive Planning and Acting using Behavior Trees2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. The planning part of the algorithm is based on the idea of back chaining. Starting from a goal condition we iteratively select actions to achieve that goal, and if those actions have unmet preconditions, they are extended with actions to achieve them in the same way. The fact that BTs are inherently modular and reactive makes the proposed solution blend acting and planning in a way that enables the robot to effectively react to external disturbances. If an external agent undoes an action the robot re- executes it without re-planning, and if an external agent helps the robot, it skips the corresponding actions, again without re- planning. We illustrate our approach in two different robotics scenarios.

  • 5.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Dimarogonas, Dimos V
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Robot navigation under uncertainties using event based sampling2014In: Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, IEEE conference proceedings, 2014, p. 1438-1445Conference paper (Refereed)
    Abstract [en]

    In many robot applications, sensor feedback is needed to reduce uncertainties in environment models. However, sensor data acquisition also induces costs in terms of the time elapsed to make the observations and the computations needed to find new estimates. In this paper, we show how to use event based sampling to reduce the number of measurements done, thereby saving time, computational resources and power, without jeopardizing critical system properties such as safety and goal convergence. This is done by combining recent advances in nonlinear estimation with event based control using artificial potential fields. The results are particularly useful for real time systems such as high speed vehicles or teleoperated robots, where the cost of taking measurements is even higher, in terms of stops or transmission times. We conclude the paper with a set of simulations to illustrate the effectiveness of the approach and compare it with a baseline approach using periodic measurements.

  • 6.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Dimarogonas, Dimos
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Obstacle avoidance in formation using navigation-like functions and constraint based programming2013In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ, IEEE conference proceedings, 2013, p. 5234-5239Conference paper (Refereed)
    Abstract [en]

    In this paper, we combine navigation functionlike potential fields and constraint based programming to achieve obstacle avoidance in formation. Constraint based programming was developed in robotic manipulation as a technique to take several constraints into account when controlling redundant manipulators. The approach has also been generalized, and applied to other control systems such as dual arm manipulators and unmanned aerial vehicles. Navigation functions are an elegant way to design controllers with provable properties for navigation problems. By combining these tools, we take advantage of the redundancy inherent in a multi-agent control problem and are able to concurrently address features such as formation maintenance and goal convergence, even in the presence of moving obstacles. We show how the user can decide a priority ordering of the objectives, as well as a clear way of seeing what objectives are currently addressed and what are postponed. We also analyze the theoretical properties of the proposed controller. Finally, we use a set of simulations to illustrate the approach.

  • 7.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    The advantages of using behavior trees in multi-robot systems2016In: 47th International Symposium on Robotics, ISR 2016, VDE Verlag GmbH, 2016, p. 23-30Conference paper (Refereed)
    Abstract [en]

    Multi-robot teams offer possibilities of improved performance and fault tolerance, compared to single robot solutions. In this paper, we show how to realize those possibilities when starting from a single robot system controlled by a Behavior Tree (BT). By extending the single robot BT to a multi-robot BT, we are able to combine the fault tolerant properties of the BT, in terms of built-in fallbacks, with the fault tolerance inherent in multi-robot approaches, in terms of a faulty robot being replaced by another one. Furthermore, we improve performance by identifying and taking advantage of the opportunities of parallel task execution, that are present in the single robot BT. Analyzing the proposed approach, we present results regarding how mission performance is affected by minor faults (a robot losing one capability) as well as major faults (a robot losing all its capabilities).

  • 8.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Ögren, Peter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Performance Analysis of Stochastic Behavior Trees2014In: ICRA 2014, 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a mathematical framework for performance analysis of Behavior Trees (BTs). BTs are a recent alternative to Finite State Machines (FSMs), for doing modular task switching in robot control architectures. By encoding the switching logic in a tree structure, instead of distributing it in the states of a FSM, modularity and reusability are improved.

    In this paper, we compute performance measures, such as success/failure probabilities and execution times, for plans encoded and executed by BTs. To do this, we first introduce Stochastic Behavior Trees (SBT), where we assume that the probabilistic performance measures of the basic action controllers are given. We then show how Discrete Time Markov Chains (DTMC) can be used to aggregate these measures from one level of the tree to the next. The recursive structure of the tree then enables us to step by step propagate such estimates from the leaves (basic action controllers) to the root (complete task execution). Finally, we verify our analytical results using massive Monte Carlo simulations, and provide an illustrative example of the results for a complex robotic task.

  • 9.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Marzinotto, Alejandro
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Stochastic Behavior Trees for Estimating and Optimizing the Performance of Reactive Plan ExecutionsArticle in journal (Refereed)
  • 10.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Murray, R. M.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Synthesis of correct-by-construction behavior trees2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 6039-6046, article id 8206502Conference paper (Refereed)
    Abstract [en]

    In this paper we study the problem of synthesizing correct-by-construction Behavior Trees (BTs) controlling agents in adversarial environments. The proposed approach combines the modularity and reactivity of BTs with the formal guarantees of Linear Temporal Logic (LTL) methods. Given a set of admissible environment specifications, an agent model in form of a Finite Transition System and the desired task in form of an LTL formula, we synthesize a BT in polynomial time, that is guaranteed to correctly execute the desired task. To illustrate the approach, we present three examples of increasing complexity.

  • 11.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Murray, Richard M.
    CALTECH, Dept Control & Dynam Syst, Pasadena, CA 91125 USA..
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Synthesis of Correct-by-Construction Behavior Trees2017In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 6039-6046Conference paper (Refereed)
    Abstract [en]

    In this paper we study the problem of synthesizing correct-by-construction Behavior Trees (BTs) controlling agents in adversarial environments. The proposed approach combines the modularity and reactivity of BTs with the formal guarantees of Linear Temporal Logic (LTL) methods. Given a set of admissible environment specifications, an agent model in form of a Finite Transition System and the desired task in form of an LTL formula, we synthesize a BT in polynomial time, that is guaranteed to correctly execute the desired task. To illustrate the approach, we present three examples of increasing complexity.

  • 12.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Parasuraman, Ramviyas
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Learning of Behavior Trees for Autonomous Agents.Article in journal (Refereed)
  • 13.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    How Behavior Trees Generalize the Teleo-Reactive Paradigm and And-Or-Trees2016Conference paper (Refereed)
    Abstract [en]

    Behavior Trees (BTs) is a way of organizing the switching structure of a control system, that was originally developed in the computer gaming industry but is now also being used in robotics. The Teleo-Reactive programs (TRs) is a highly cited reactive hierarchical robot control approach suggested by Nilsson and And-Or-Trees are trees used for heuristic problems solving. In this paper, we show that BTs generalize TRs as well as And-Or-Trees, even though the two concepts are quite different. And-Or-Trees are trees of conditions, and we show that they transform into a feedback execution plan when written as a BT. TRs are hierarchical control structures, and we show how every TR can be written as a BT. Furthermore, we show that so-called Universal TRs, guaranteeing that the goal will be reached, are a special case of so-called Finite Time Successful BTs. This implies that many designs and theoretical results developed for TRs can be applied to BTs.

  • 14.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees2017In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 33, no 2, p. 372-389Article in journal (Refereed)
  • 15.
    Colledanchise, Michele
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    How Behavior Trees Modularize Robustness and Safety in Hybrid Systems2014In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), IEEE , 2014, p. 1482-1488Conference paper (Refereed)
    Abstract [en]

    Behavior Trees (BTs) have become a popular framework for designing controllers of in-game opponents in the computer gaming industry. In this paper, we formalize and analyze the reasons behind the success of the BTs using standard tools of robot control theory, focusing on how properties such as robustness and safety are addressed in a modular way. In particular, we show how these key properties can be traced back to the ideas of subsumption and sequential compositions of robot behaviors. Thus BTs can be seen as a recent addition to a long research effort towards increasing modularity, robustness and safety of robot control software. To illustrate the use of BTs, we provide a set of solutions to example problems.

  • 16.
    Marzinotto, Alejandro
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Smith, Christian
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Towards a Unified Behavior Trees Framework for Robot Control2014In: Robotics and Automation (ICRA), 2014 IEEE International Conference on , IEEE Robotics and Automation Society, 2014, p. 5420-5427Conference paper (Refereed)
    Abstract [en]

    This paper presents a unified framework for Behavior Trees (BTs), a plan representation and execution tool. The available literature lacks the consistency and mathematical rigor required for robotic and control applications. Therefore, we approach this problem in two steps: first, reviewing the most popular BT literature exposing the aforementioned issues; second, describing our unified BT framework along with equivalence notions between BTs and Controlled Hybrid Dynamical Systems (CHDSs). This paper improves on the existing state of the art as it describes BTs in a more accurate and compact way, while providing insight about their actual representation capabilities. Lastly, we demonstrate the applicability of our framework to real systems scheduling open-loop actions in a grasping mission that involves a NAO robot and our BT library.

  • 17. McGhan, C. L. R.
    et al.
    Wang, Y. -S
    Murray, R. M.
    Vaquero, T.
    Williams, B. C.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Towards architecture-wide analysis, verification, and validation for total system stability during goal-seeking space robotics operations2016In: AIAA Space and Astronautics Forum and Exposition, SPACE 2016, American Institute of Aeronautics and Astronautics, 2016Conference paper (Refereed)
    Abstract [en]

    In this paper we discuss the beginnings of an attempt to define and analyze the stability of an entire modular robotic system architecture - one which includes a three-tier (3T) layer breakdown of capabilities, with symbolic, deterministic planning at the highest level. We approach the problem from the standpoint of a control theory outlook, and try to formalize the issues that result from trying to quantitatively characterize the overall performance of a well-defined system without a need for exhaustive testing. We start by discussing the concept of bounded-input bounded-output stability, giving examples where the technique might not be sufficient to guarantee what we term “total system stability” due to complications associated with the levels of abstraction between the modules and components that are being chained together in the architecture. We then go on to discuss necessary conditions that may fall out of this naturally as a result. We further try to better-define the input and output constraints needed to guarantee total system stability, using an assumption-guarantee-like contractual framework that sits alongside the architecture; the requirements then may have influence across multiple modules, in order to keep consistency. We also discuss how the structure of the architectural modules may help or hinder the process of capability characterization and performance analysis of each module and a given architecture configuration as a whole. We then discuss two overlapping methods that, combined, should allow us to analyze the effectiveness of the architecture, and help towards verification and validation of both the components and the system as a whole. Demonstrative examples are given using a specific architectural implementation called the Resilient Spacecraft Executive. In future work, we hope to define both necessary and sufficient conditions for total system stability across such a system architecture for robotics use.

  • 18.
    Wang, Yuquan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Ögren, Petter
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    A Distributed Convergent Solution to the Ambulance Positioning Problem on a Streetmap Graph2014In: / [ed] Boje, Edward, Xia, Xiaohua, IFAC Papers Online, 2014, Vol. 19, p. 9190-9196Conference paper (Refereed)
    Abstract [en]

    In this paper, we combine ideas from multi-agent cooperative coverage control, with problem formulations from the resource allocation field, to create a distributed convergent approach to the ambulance positioning problem. Inspired by coverage control we use the graph version of so-called Voronoi regions, making the solution distributed and reactive, thereby freeing computational resources. The solution is distributed in the sense that each vehicle only needs to know the positions of its neighbors, and the computations of each vehicle only depend on the size of its Voronoi region/set. This implies that considering a problem of twice the size, using twice the number of vehicles will leave the computational load per vehicle unchanged. The freed resources are used to capture the allocation problem in more detail: maximizing an estimate of the victim survival probability instead of more coarse measures of ambulance availability. Using real city street map data from OpenStreetMap (OSM), we provide simulation results illustrating the applicability of our approach. Finally, we prove that the proposed distributed algorithm is convergent in the sense that it finds a local optimum in finite time.

  • 19.
    Ögren, Petter
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Colledanchise, Michele
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Behavior Trees in Robotics and AI: An Introduction2018 (ed. First)Book (Refereed)
    Abstract [en]

    Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game.  Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality.  BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs.

1 - 19 of 19
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