Behavior-based Architecture Research Articles

An Integrated Approach for Achieving Multirobot Task Formations

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.

Construction in a Simulated Environment Using Temporal Goal Sequencing and Reinforcement Learning

A behavior-based architecture ( ConAg) with a connectionist action selection mechanism is introduced that enables a society of autonomous agents to construct arbitrary structures in their simulated two-dimensional world. Construction in this environment involves the agents picking up colored discs and dropping them at incomplete parts of the structure being built. The ConAg architecture provides both reactive behaviors which are used to maintain the viability of the agent and navigational planning behaviors that are used for construction. The action selection mechanism enables learning the sequence of behaviors required for construction by reinforcement learning. The navigational planning behaviors use a grid-based representation of the world. The shape of the structure to be built is also encoded on an internal spatial map. Path planning is implemented by spreading activations on sets of grid-based maps so that the agents perform the construction task efficiently. Construction of arbitrary structures is supported by temporal sequencing of goals. We present simulation results that demonstrate the performance of the architecture and algorithms.

Multi-agent Architecture Model for Driving Mobile Manipulator Robots

In this article, we present generic hierarchical behavior-based architecture model for driving mobile manipulator robots. Two behaviors are of high-level. They constitute the Supervisory agent, which manages the global system. Two others are of intermediate-level and finally one behavior is of low-level. These last ones constitute the Mobile Robot agent and the Manipulator Robot agent controlling, respectively, the mobile base and the manipulator arm. The choice of the suggested model is justified by the generic character of the proposed agent model and by the possibility of integrating the whole in a distributed robotic system. The model is formalized in Agent UML from the conceptual level to the implementation level. The interaction between the various agents is modeled by the use of the interaction diagrams of Agent UML (states and protocol diagrams).

Development of a Real-time Onboard and Ground Station Software System for a UAV Helicopter

We report in this paper the development of a real-time software system for an unmanned aerial vehicle (UAV) helicopter, which consists of an embedded computer onboard and a ground station served by a laptop. The software system for the onboard computer performs multiple tasks including data acquisition and measurement, servo driving, automatic flight control implementation, communications and data logging. The system for the ground station gives a flexible graphical interface monitoring the real-time status of the UAV helicopter. We present the frameworks and structures of the onboard and ground station systems. The onboard system employs a framework of multiple task threads with each thread being assigned to perform a specific task. Management and time scheduling for task threads together with the detailed implementation of automatic flight control laws are the main focuses of the onboard software system. A behavior-based architecture is designed to address task scheduling and event disposal for automatic control. A hierarchical and componential structure is developed to integrate multiple control laws and perform various helicopter behaviors. The ground station software system employs a two-layer framework, i.e., data transferring in background and data visualization in foreground.A variety of views are developed to display in-flight data received from the UAV helicopter in different forms including a 3D monitoring panel, which displays real-time data in 3D on the ground station.

Sensor-based global planning for mobile robot navigation

SUMMARYThe proposed algorithm integrates in a single planner the global motion planning and local obstacle avoidance capabilities. It efficiently guides the robot in a dynamic environment. This eliminates some of the traditional problems of planned architectures (model-plan-act scheme) while obtaining many of the qualities of behavior-based architectures. The computational efficiency of the method allows the planner to operate at high-rate sensor frequencies. This avoids the need for using both a collision-avoidance algorithm and a global motion planner for navigation in a cluttered environment. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. Operating on a simple grid-based world model, the method uses a fast marching technique to determine a motion plan on a Voronoi extended transform extracted from the environment model. In addition to this real-time response ability, the method produces smooth and safe robot trajectories.

Jde-neoc: component oriented software architecture for robotics

In this paper we present our software framework for robotic applications, jde-neoc. This is the second implementation of our cognitive behavior-based architecture JDE, and it is aimed to overcome some of the limitations observed in three years using the first one. jde-neoc uses schemas as the basic component of robot applications, which are combined in dynamic hierarchies to unfold the global behavior. Each schema is built separatedly into a plugin and dynamically linked to the framework when needed. It keeps its own graphical user interface. Some tools like a hierarchy oscilloscope and a 3D sensors-and-motors GUI have been created and added to the framework.

Hierarchical Behavior Control of Mobile Robot Based on Space & Time Sensor Fusion(STSF)

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

Evolution of fuzzy behaviors for multi-robotic system

In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.

Sustainable cooperative robotic technologies for human and robotic outpost infrastructure construction and maintenance

Robotic Construction Crew (RCC) is a heterogeneous multi-robot system for autonomous acquisition, transport, and precision mating of components in construction tasks. RCC minimizes use of resources constrained by a space environment such as computation, power, communication, and sensing. A behavior-based architecture provides adaptability and robustness despite low computational requirements. RCC successfully performs several construction related tasks in an emulated outdoor environment despite high levels of uncertainty in motions and sensing. This paper provides quantitative results for formation keeping in component transport, precision instrument placement, and construction tasks.

Architectural Mechanisms for Dynamic Changes of Behavior Selection Strategies in Behavior-Based Systems

Behavior selection is typically a "built-in" feature of behavior-based architectures and hence, not amenable to change. There are, however, circumstances where changing behavior selection strategies is useful and can lead to better performance. In this paper, we demonstrate that such dynamic changes of behavior selection mechanisms are beneficial in several circumstances. We first categorize existing behavior selection mechanisms along three dimensions and then discuss seven possible circumstances where dynamically switching among them can be beneficial. Using the agent architecture framework activation, priority, observer, and component (APOC), we show how instances of all (nonempty) categories can be captured and how additional architectural mechanisms can be added to allow for dynamic switching among them. In particular, we propose a generic architecture for dynamic behavior selection, which can integrate existing behavior selection mechanisms in a unified way. Based on this generic architecture, we then verify that dynamic behavior selection is beneficial in the seven cases by defining architectures for simulated and robotic agents and performing experiments with them. The quantitative and qualitative analyzes of the results obtained from extensive simulation studies and experimental runs with robots verify the utility of the proposed mechanisms.

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Técnico (ISR/IST) in Lisbon. The acronym of the project stands both for “Society of Robots” and “Soccer Robots”, the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams.

Fuzzy Behavior-Based Control of Mobile Robots

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.

A multiagent cooperative mobile robotics approach for search and rescue missions

This paper presents a reusable framework for coordinating a team of mobile robots that can accomplish high level or tightly coupled missions, which could not be easily achieved using single robot solutions. To complete this task, we consider the behavior-based architecture model for the basic control layer. For coordinating the team of autonomous mobile robots, we use the agent-oriented software engineering paradigm. We apply the combination of a single and of multiagent techniques for evaluating a search-and-rescue mission. The experiments are using the Pioneer 2 DX robot and the Saphira simulator.

Modular behavior-based control for team humanoids

When developing teams of humanoids — rather than an individual humanoid — there are a number of issues that become important to consider, including robustness, scalability, versatility, and also development and production costs. Therefore, we used a modern approach to AI that puts emphasis on the balance between control, electronic hardware, material, sensory system and energy in order to develop the team of Viki humanoid robots. In contrast to the top-down approach of equipping a humanoid with as many sensors, motors, power, etc., as possible, we developed a bottom-up approach to the construction of humanoids, regarding both hardware and software (modular behaviorbased control). The approach is shown with the development of the Viki humanoid team that won the RoboCup Humanoids Free Style World Championship 2002. In this paper, we focus on the main result of the behavior-based architecture with many layers of behaviors at different levels, which make it easy for both engineers to design new behaviors and for end-users to develop humanoid behaviors at different levels of complexity, dependent on the competencies of the individual end-user. With this architecture, it becomes possible to develop simple user interfaces with a user-guided implementation of our modular behavior-based approach, in order to allow any user to design performances with the humanoid robots.

Behavior-based dual dynamic agent architecture

The objective of the architecture is to make agent promptly and adaptively accomplish tasks in the real-time and dynamic environment. The architecture is composed of elementary level behavior layer and high level behavior layer. In the elementary level behavior layer, the reactive architecture is introduced to make agent promptly react to events; in the high level behavior layer, the deliberation architecture is used to enhance the intelligence of the agent. A confidence degree concept is proposed to combine the two layers of the architecture. An agent decision making process is also presented, which is based on the architecture. The results of experiment in RoboSoccer simulation team show that the proposed architecture and the decision process are successful.

On the behavior-based architectures of autonomous agency

A number of autonomous robots with varying degrees of reactive functionality have been built, based on different architectures. We review the foundations, limitations, and achievements of a number of architectures of such autonomous agents from the three categories: (1) reactive; (2) deliberative; and (3) hybrid. Most of these architectures contain behaviors. The principle of avoiding an explicit representation of goals in the purely behavior-based robots has limited their achievements to simple tasks like box pushing, pipe inspection, and navigation. This paper makes two contributions: (1) reviewing autonomous agent architectures and (2) proposing a new class of architectures where behaviors are coupled and/or markers are introduced in environment, without a planner or sequencer and without an explicit representation of goals and investigating tradeoffs in these architectures. We develop a model of behaviors, environmental modification and goals and then show how the behavior-based robots can be made goal-directed. The tradeoffs in increasing their goal directedness are examined. Defining the notion of coupling that captures dependency within the internal structure of a behavior space, it is shown that more complex goals demand higher coupling or more behaviors or a modification to the environment. These novel tradeoffs show a new spectrum of architectures for integrating goals and the behavior-based reactive functionality.

A behavior-based architecture for autonomous underwater exploration

Abstract We present a system for behavior-based control of an autonomous underwater vehicle for the purpose of inspection of coral reefs, a task currently performed by divers holding a video camera while following a rope. Using sonar and vision-based approaches, behaviors have been developed for guiding the robot along its intended course, for maintaining a constant height above the sea floor, and for avoiding obstacles. A task-level controller selects which behaviors should be active according to user-defined plans and in response to system failures. Behavior arbitration has been implemented using both fuzzy logic and utility fusion. Initial experiments have been conducted in a natural coastal inlet, and the system is to be soon demonstrated in the coral reef environment.

Simulation in the Development of a Control Strategy for an Automated Guided Vehicle System

An automated guided vehicle system (AGVS) consists of several automated guided vehicles (AGV) operating concurrently to achieve a specific task. Decentralized, autonomous control of AGVS used for material handling in manufacturing environments is expected to lead to high system efficiency, flexibility and robustness. The simulations developed and used for the development process of a decentralized control methodology based on a hierarchical fuzzy behavior-based architecture for the control of an AGVS are presented.

2P1-F2 行動規範型ロボットアキテクチャへの動的状態評価指数の導入(30. マルチエージェントロボットシステムII)

2P1-F2 行動規範型ロボットアキテクチャへの動的状態評価指数の導入(30. マルチエージェントロボットシステムII)