Behavior-based Architecture Research Articles

Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics

Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans under different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error.

An arrovian analysis on the multi-robot task allocation problem: Analyzing a behavior-based architecture

Research in multi-robot systems is a rich field that has attracted much attention in recent decades. However, robot coordination and task allocation to a correct mission accomplishment are still challenging even with technological advances. Despite many proposals presented in the literature, the applications and theories about the task allocation problem are not yet exhausted. Thus, this work proposes an axiomatic framework based on Social Choice Theory to analyze the task allocation problem in intentional cooperation multi-robot systems. It uses Kenneth J. Arrow’s framework of his famous Impossibility Theorem. The conditions imposed by Arrow aim to create an ideal for preference aggregation mechanisms through axiomatic analysis. This paper aims to transport this analysis to the multi-robot domain. A behavior-based Multi-robot Task Allocation architecture is used to present simulation results and discuss two cases in the ordinal preference domain. The analysis results show that using the proposed framework to analyze, under the Arrovian perspective, implemented MRTA architectures is feasible.

Sistema robótico autónomo para la exploración y construcción de mapas en entornos estructurados


 Objetivo: Construir un sistema robótico para la exploración y el mapeo de entornos interiores de trabajo con una arquitectura y una metodología de diseño propia. Metodología: Se definió una arquitectura y un diseño en la parte de hardware y en la parte lógica. En la parte lógica, se utilizó una representación del entorno basada en celdas de ocupación y una arquitectura de control fundada en comportamientos que fue implementada a través de un control fuzzy. Resultados: Se probó la plataforma robótica en escenarios estructurados como pasillos y salones. Su efectividad se comprobó mediante mapas construidos en un aplicativo desarrollado en Matlab. Conclusiones: Se demostró la capacidad de la plataforma robótica para construir mapas de su entorno de forma explícita, que, aun cuando pueden resultar más demandantes en lo computacional, permite una representación precisa y comprensible.

Adapting to environmental dynamics with an artificial circadian system

One of the core challenges of long-term autonomy is the environmental dynamics that agents must interact with. Some of these dynamics are driven by reliable cyclic processes, and thus are predictable. The most dominant of these is the daily solar cycle, which drives both natural phenomena like weather, as well as the activity of animals and humans. Circadian clocks are a widespread solution in nature to help organisms adapt to these dynamics, and demonstrate that many organisms benefit from maintaining simple models of their environments and how they change. Drawing inspiration from circadian systems, this work models relevant environmental states as times series, allowing for forecasts of the state to be generated without any knowledge of the underlying physics. These forecasts are treated as special percepts in a behavior-based architecture; providing estimates of the future state rather than measurements of the current state. They are incorporated into an ethologically based action-selection mechanism, where they influence the activation levels of behaviors. The approach was validated on a simulated agricultural task: a solar-powered agent monitoring pest populations. By using the artificial circadian system to leverage the forecasted state, the agent was able to improve performance and energy management.

Development of Behavior-Based Autonomous Moving System for Field Robot

In this research, we have been developed a field robot with behavior-based autonomous moving for labor-saving of forest industry. We focused on a behavior-based architecture as typified by Subsamption Architecture. In this paper, we describe a behavior-based autonomous moving system with decision of a destination autonomously to make the robot travel in the work space. An individual tree will be treated as landmark in an environment, and these treated as a vertices will construct a polygon which represents an unreached region for the robot. The system will guide the robot to make the polygon is divided.

Coordination mechanism for integrated design of Human-Robot Interaction scenarios

Abstract The ultimate long-term goal in Human-Robot Interaction (HRI) is to design robots that can act as a natural extension to humans. This requires the design of robot control architectures to provide structure for the integration of the necessary components into HRI. This paper describes how HBBA, a Hybrid Behavior-Based Architecture, can be used as a unifying framework for integrated design of HRI scenarios. More specifically, we focus here on HBBA’s generic coordination mechanism of behavior-producing modules, which allows to address a wide range or cognitive capabilities ranging from assisted teleoperation to selective attention and episodic memory. Using IRL-1, a humanoid robot equipped with compliant actuators for motion and manipulation, proximity sensors, cameras and a microphone array, three interaction scenarios are implemented: multi-modal teleoperation with physical guidance interaction, fetching-and delivering and tour-guiding.

Towards a hybrid software architecture and multi-agent approach for autonomous robot software

To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior's objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior-based architectures in term of accuracy, smoothness, traveled distance, and time response.

Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot

This work addresses a new goal oriented navigation framework for autonomous system. In the proposed work, a hybrid control system, comprising deliberative and behavior-based architectures, has been developed. Deliberative layer employs a monocular vision camera to obtain the position of the goal while behavior-based framework makes use of the motor schema technique for safe navigation. Fuzzy logic is also adopted in order to enhance the performance of the navigation system. A rigorous series of experiments has been conducted using two navigation methods, which are the proposed control system and the conventional navigation technique utilizing the potential field method for achieving the desired goals. Both systems are implemented in the simulated experiments using Stage simulator. By employing these two approaches, it is possible to present a comparison of the navigation results between the systems utilizing different navigation techniques. The experimental results reveal that the proposed system produces better navigation performance compared to the conventional method in terms of safe and successful navigation, with a smoother trajectory and consistent motion.

Mobile Robot Based on the Selection of Fuzzy Behaviours for following Trajectories in Crops

This article addresses the problem of trajectory tracking in crops by a weed sprayer mobile robot (WSMR). This problem arises because to fumigate, the robot must follow a predefined path and avoid any obstacles it may encounter. To achieve both trajectory tracking and obstacle avoidance, a control scheme based on different behaviours is proposed, which consists essentially of an adaptive controller with a reference model for trajectory tracking and a fuzzy reactive for obstacle avoidance. Each of these controllers is executed according to the selection of the fuzzy behaviour controller, which uses information delivered by anti-collision sensors located on the robot. As a result of the implementation of this behaviour-based architecture and by means of computer simulations and experimental laboratory tests, the WSMR demonstrates the capability of autonomously following a desired trajectory between the rows of a crop in the presence of obstacles. The results are evaluated by taking into account trajectory tracking curves and the operating requirements of each controller, as well as the application of different errors indices for quantitatively evaluating the proposed control scheme.

Proposed Methodology for Application of Human-like gradual Multi-Agent Q-Learning (HuMAQ) for Multi-robot Exploration

Several attempts have been made by the researchers around the world to develop a number of autonomous exploration techniques for robots. But it has been always an important issue for developing the algorithm for unstructured and unknown environments. Human-like gradual Multi-agent Q-leaming (HuMAQ) is a technique developed for autonomous robotic exploration in unknown (and even unimaginable) environments. It has been successfully implemented in multi-agent single robotic system. HuMAQ uses the concept of Subsumption architecture, a well-known Behaviour-based architecture for prioritizing the agents of the multi-agent system and executes only the most common action out of all the different actions recommended by different agents. Instead of using new state-action table (Q-table) each time, HuMAQ uses the immediate past table for efficient and faster exploration. The proof of learning has also been established both theoretically and practically. HuMAQ has the potential to be used in different and difficult situations as well as applications. The same architecture has been modified to use for multi-robot exploration in an environment. Apart from all other existing agents used in the single robotic system, agents for inter-robot communication and coordination/ co-operation with the other similar robots have been introduced in the present research. Current work uses a series of indigenously developed identical autonomous robotic systems, communicating with each other through ZigBee protocol.

A New Navigation of Behavior-Based Olfactory Mobile Robot

In this paper a new olfactory mobile robot application is proposed where dynamic olfaction system is used on a mobile robot in order to acquire the gas/odour property of objects. Olfaction system with two dynamic gas/odour sensors can be moved in 14180o' type="#_x0000_t75"> in order to be able to detect source in many directions. We examine the problem of deciding when, how and where the gas/odour sensor should be activated by planning for active perception use behavior-based architecture. Simple form of cooperation between Fuzzy Logic control and Particle Swarm Optimization (PSO) is implemented in the navigation strategies. The real experiments performed on a simple mobile robot equipped with dynamic gas/odour sensor and three infra-red sensor. The initial result shows that olfactory mobile robot that is capable of locating the source of a simulated chemical leak in an environment while detecting and avoiding obstacles along its path.

Scalable Task Assignment for Heterogeneous Multi-Robot Teams

This work deals with the development of a dynamic task assignment strategy for heterogeneous multi-robot teams in typical real world scenarios. The strategy must be efficiently scalable to support problems of increasing complexity with minimum designer intervention. To this end, we have selected a very simple auction-based strategy, which has been implemented and analysed in a multi-robot cleaning problem that requires strong coordination and dynamic complex subtask organization. We will show that the selection of a simple auction strategy provides a linear computational cost increase with the number of robots that make up the team and allows the solving of highly complex assignment problems in dynamic conditions by means of a hierarchical sub-auction policy. To coordinate and control the team, a layered behaviour-based architecture has been applied that allows the reusing of the auction-based strategy to achieve different coordination levels.

Fuzzy Motivations in a Multiple Agent Behaviour-Based Architecture

In this article we introduce a blackboard-based multiple agent system framework that considers biologically-based motivations as a means to develop a user friendly interface. The framework includes a population-based heuristic as well as a fuzzy logic-based inference system used toward scoring system behaviours. The heuristic provides an optimization environment and the fuzzy scoring mechanism is used to give a fitness score to possible system outputs (i.e. solutions). This framework results in the generation of complex behaviours which respond to previously specified motivations. Our multiple agent blackboard and motivation-based framework is validated in a low cost mobile robot specifically built for this task. The robot was used in several navigation experiments and the motivation profile that was considered included “curiosity”, “homing”, “energy” and “missions”. Our results show that this motivation-based approach permits a low cost multiple agent-based autonomous mobile robot to acquire a diverse set of fit behaviours that respond well to user and performance expectations. These results also validate our multiple agent framework as an incremental, flexible and practical method for the development of robust multiple agent systems.

A Behaviour-Based Architecture for Mapless Navigation Using Vision

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot in a cluttered environment to navigate successfully to a goal while avoiding obstacles is a challenging problem. This paper presents a new behaviour-based architecture design for mapless navigation. The architecture is composed of several modules and each module generates behaviours. A novel method, inspired from a visual homing strategy, is adapted to a monocular vision-based system to overcome goal-based navigation problems. A neural network-based obstacle avoidance strategy is designed using a 2-D scanning laser. To evaluate the performance of the proposed architecture, the system has been tested using Microsoft Robotics Studio (MRS), which is a very powerful 3D simulation environment. In addition, real experiments to guide a Pioneer 3-DX mobile robot, equipped with a pan-tilt-zoom camera in a cluttered environment are presented. The analysis of the results allows us to validate the proposed behaviour-based navigation strategy.

A Simulation Environment for Deep-Sea Hydrothermal Plume Tracing with Autonomous Underwater Vehicles

For using an autonomous underwater vehicle(AUV) to localize seafloor hydrothermal vents fast and accurately, a biomimetic approach to hydrothermal plume tracing is proposed.And to support the investigation of AUV's biologicallyinspired control strategies,a graphical simulation environment is designed and developed.In this paper,hydrothermal plume tracing with AUVs and the modular architecture of the simulation environment are firstly introduced and described.Then,the modules of hydrothermal plume and flow field in simulation environment are modeled,and the efficient numerical solution algorithms are given.To facilitate Monte Carlo simulation,a set of stochastic initial and boundary conditions are set.And the modular behavior-based AUV control system architecture employed in the control system module is mainly described. The simulation environment addresses the key factors that complicate the investigation of AUV's biologically-inspired control strategies for the hydrothermal plume tracing,including the current field being non-uniform and non-steady,the plume distribution being irregular,intermittent and of large scale,the plume centreline being meandrous,and the plume containing buoyant part and nonconservative tracer.In addition,this simulation environment achieves good visual effect and high computational efficiency,which allows it to be suitable for real-time simulation and Monte Carlo simulation studies.The presented simulation environment provides sufficient support for the investigation of AUVs biologically-inspired control strategies for hydrothermal plume tracing.

Behaviour-Based Off-Road Robot Navigation

This paper describes concepts for the control of the autonomous off-road vehicle ravon. The complexity of the target environment comprising rough terrain as well as vegetation requires capabilities reaching from low-level safety aspects to high-level planning. It is shown how the modular implementation using the behaviour-based architecture iB2C allows for the realisation of complex behaviour networks based on a concept for the uniform representation of sensor data. The effectiveness of the presented approach is briefly shown in a real-world experiment.

Using Sun’s Java Real-Time System to Manage Behavior-Based Mobile Robot Controllers

Implementing a robot controller that can effectively manage limited resources in a deterministic, real-time manner is challenging. Behavior-based architectures that decompose autonomy into levels of intelligence are popular due to their robustness but do not provide real-time features that enforce timing constraints or support determinism. We propose an architecture and approach for using the real-time features of the Real-Time Specification for Java (RTSJ) in a behavior-based mobile robot controller to show that timing constraints affect performance. This is accomplished by extending a real-time aware architecture that explicitly enumerates timing requirements for each behavior. It is not enough to reduce latency. The usefulness of this approach is demonstrated via an implementation on Solaris 10 and the Sun Java Real-Time System (Java RTS). Experimental results are obtained using a K-team Koala robot performing path following with four composite behaviors. Experiments were conducted using several task period sets in three cases: real-time threads with the real-time garbage collector, real-time threads with the non- real-time garbage collector, and non-real-time threads with the non-real-time garbage collector. Results show that even if latency and determinism are improved, the timing of each individual behavior significantly affects task performance.

Combining Dense Structure from Motion and Visual SLAM in a Behavior-Based Robot Control Architecture

In this paper, we present a control architecture for an intelligent outdoor mobile robot. This enables the robot to navigate in a complex, natural outdoor environment, relying on only a single on-board camera as sensory input. This is achieved through a twofold analysis of the visual data stream: a dense structure from motion algorithm calculates a depth map of the environment and a visual simultaneous localization and mapping algorithm builds a map of the surroundings using image features. This information enables a behavior-based robot motion and path planner to navigate the robot through the environment. In this paper, we show the theoretical aspects of setting up this architecture.

Balanced reactive-deliberative architecture for multi-agent system for simulation league of RoboCup

This paper presents an architecture for a multi-agent system for the RoboCup simulation league. It consists of a dynamic dual behavior-based architecture for an intelligent agent, a behavior-based decision algorithm, and a dynamic role-based multi-agent cooperation model. A new concept called confidence function is introduced to balance reactivity and deliberation. This architecture was implemented in a team, and match results demonstrate its validity.