A Modular Hierarchical Behavior-Based Architecture

Lenser, Scott; Bruce, James; Veloso, Manuela

doi:10.1007/3-540-45603-1_54

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…Since then, it has been used in a large variety of applications including: indoor robots, e.g., [6], [7], [8], [9], [10], [11], [12], [13], [14], land vehicles, e.g., [15], planetary rovers, e.g., [16], [17], [18], and marine vehicles, e.g., [19], [20], [21], [22], [23], [24], [25].…”

Section: Background a Behavior-based Controlmentioning

confidence: 99%

Navigation of unmanned marine vehicles in accordance with the rules of the road

Benjamin

Curcio

Leonard

et al. 2006

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

100

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This paper is concerned with the in-field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the Coast Guard Collision Regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture the flexibility exploited by humans, this work applies a novel method of multi-objective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in-field demonstration of multiobjective optimization applied to autonomous COLREGS-based marine vehicle navigation.

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Section: Background a Behavior-based Controlmentioning

confidence: 99%

Navigation of unmanned marine vehicles in accordance with the rules of the road

Benjamin

Curcio

Leonard

et al. 2006

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

100

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“…The fundamental building block of our robot's control system are functions, referred to as behaviors, that map a set of inputs, including sensor information as well as derived state information, to a set of actions [1]. Internally, every behavior is defined as a finite state machine with an explicit start state and potentially multiple termination states, depending on whether the behavior was successful in achieving its goals [13]. Behaviors are responsible for relatively simple control operations such as tracking a person or navigating between waypoints.…”

Section: Task Trainingmentioning

confidence: 99%

Using dialog and human observations to dictate tasks to a learning robot assistant

Rybski

Stolarz

Yoon

et al. 2008

Intel Serv Robotics

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Robot assistants need to interact with people in a natural way in order to be accepted into people's day-today lives. We have been researching robot assistants with capabilities that include visually tracking humans in the environment, identifying the context in which humans carry out their activities, understanding spoken language (with a fixed vocabulary), participating in spoken dialogs to resolve ambiguities, and learning task procedures. In this paper, we describe a robot task learning algorithm in which the human explicitly and interactively instructs a series of steps to the robot through spoken language. The training algorithm fuses the robot's perception of the human with the understood speech data, maps the spoken language to robotic actions, and follows the human to gather the action applicability state information. The robot represents the acquired task as a conditional procedure and engages the human in a spokenlanguage dialog to fill in information that the human may have omitted.

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“…Also in the RoboCup domain, the architecture proposed by Manuela Veloso et al [21] shows an hybrid hierarchical behaviour-based architecture. This architecture was divided in levels.…”

Section: Robotics Control Architectures In Literaturementioning

confidence: 99%

“…This architecture was based in a fuzzy approach, extended in [23]. The perceptual and global modelling components managed information in a fuzzy way and were used to generate the next actions.Also in the RoboCup domain, the architecture proposed by Manuela Veloso et al [21] shows an hybrid hierarchical behaviour-based architecture. This architecture was divided in levels.…”

mentioning

confidence: 99%

Design an evaluation of RoboCup humanoid goalie

Sierra¹,

Lera²,

Llamas³

et al. 2010

jopha

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Abstract-In this article we describe the ethological inspired architecture we have developed and how it has been used to implement a humanoid goalkeeper according to the regulations of the two-legged Standard Platform League of the RoboCup Federation. We present relevant concepts borrowed from ethology that we have successfully used for generating autonomous behaviours in mobile robotics, such as the use of ethograms in robotic pets or the ideas of schemata, or the use of fixed actions patterns to implement reactivity. Then we discuss the implementation of this architecture on the Nao biped robot. Finally, we propose a method for its evaluation and validation and analyse the results obtained during RoboCup real competition, which allowed us to test first hand how it worked in a real environment.Index Terms-reactive, vision, humanoid, schema I. ROBOTICS CONTROL ARCHITECTURES IN LITERATUREG ENERATING autonomous behaviours in mobile robotics is really a complex problem. In this section we present a non in-depth outline about those robotics control architectures close to our research. We are going to be neither exhaustive, mainly because it would be impossible to describe all control architectures in just one section, nor hierarchycal, since there would be too many criteria to take into account.Summarising the complex history of the AI, we can state that two main schools of thought have coexisted, the subsymbolic one, interested on modeling intelligence in a level similar to neurons; and the symbolic AI, which models knowledge and planning in data structures that make sense to the programmers that build them. Another way of explaining the difference between both schools is referring to their foundations: Biology in the subsymbolic AI, and cognitive psychology in the symbolic AI [9]. Hybrid systems are a pragmatic approach, where ethology based systems can be included because they successfully integrate deliberative and reactive perspectives in natural autonomous systems.Hybrid architectures intend to combine reactive and deliberative control, and usually consist of three components: a reactive layer, a planner, and a layer that links the other two. Opportunistic architectures are a subset of the hybrid architectures that take its name from Barbara Hayes-Roth approach to hybrid control [7]. The agents architecture on her system was also made up by three components: an event-triggered reactive level, an strategic planner, and a control process in charge of matching triggered actions with the generated plan. A similar architecture is used in where the term "agent" is used to name each of the three modules of the system.Another implementation of these ideas are RAPs (Reactive Action Packages) proposed by Firby [5]. RAPs were designed to allow the reactive execution of symbolic plans. In this way, each RAP defines different alternatives of execution depending on the environment, and an agenda is used to select the next action to execute. Another approach is the TCA (Task Control Architecture) by Simmons [17], which inte...

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A Modular Hierarchical Behavior-Based Architecture

Cited by 18 publications

References 5 publications

Navigation of unmanned marine vehicles in accordance with the rules of the road

Navigation of unmanned marine vehicles in accordance with the rules of the road

Using dialog and human observations to dictate tasks to a learning robot assistant

Design an evaluation of RoboCup humanoid goalie

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