In behavior-based robotics, robots are controlled by behavioral modules that are triggered on data perceived by sensors or according to some model of the situation to be faced. However, data are acquired by imperfect ...
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In behavior-based robotics, robots are controlled by behavioral modules that are triggered on data perceived by sensors or according to some model of the situation to be faced. However, data are acquired by imperfect sensors and, whenever a world modeler exists, are usually interpreted by modelling algorithms that abstract the relevant features inevitably loosing information. In this paper, we present our approach to data reliability and to its propagation through the path from data to actuation. We show how reliability is different from truth value or from approximation and how this impacts on the agent's performance
This paper describes the Semi-Online Neural-Q-leaming (SONQL) algorithm designed for real-time learning of reactive robot behaviors. The Q-function is generalized by a multilayer neural network allowing the use of con...
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This paper describes the Semi-Online Neural-Q-leaming (SONQL) algorithm designed for real-time learning of reactive robot behaviors. The Q-function is generalized by a multilayer neural network allowing the use of continuous states. The algorithm uses a database of the most recent learning samples to accelerate and improve the convergence. Each SONQL algorithm represents an independent, reactive and adaptive state-action mapping, which implements the function of a robot behavior for one degree of freedom (DOF). The generalization capability of the SONQL algorithm was demonstrated by computer simulation with the '' mountain-car '' benchmark. The SONQL was also investigated by experiment on a mobile robot for a target-following task. Experimental results show that the SONQL is promising for online robot learning. (c) 2007 Elsevier B.V. All rights reserved.
A simulation software package (UF Library) implementing the utility function (UF) method for behavior selection in autonomous robots, is introduced and described by means of an example involving a simple exploration r...
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A simulation software package (UF Library) implementing the utility function (UF) method for behavior selection in autonomous robots, is introduced and described by means of an example involving a simple exploration robot equipped with a repertoire of five different behaviors. The UF Library (as indeed the UF method itself) is aimed at providing a rapid yet reliable and generally applicable procedure for generating behavior selection systems for autonomous robots, while at the same time minimizing the amount of hand-coding related to the activation of behaviors. It is demonstrated how the UF Library allows a user to rapidly implement individual behaviors and to set up and carry out simulations of a robot in its arena, in order to generate and optimize, by means of an evolutionary algorithm,the behavior selection system of the robot.
We discuss decision making for a behavior-based robot with modules which determining robot action. The subsumption architecture (SA) arranges modules in layers, giving upper-layer module action priority over lower-lay...
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We discuss decision making for a behavior-based robot with modules which determining robot action. The subsumption architecture (SA) arranges modules in layers, giving upper-layer module action priority over lower-layer modules. Although implementation is easy, results in many inefficient actions because upper-layer module are used regardless of other modules. We solve this problem by representing actions by Potential Function (PF), in which maximum votes are collected from modules. Using event-driven state transition, the robot decides its action with appropriate sets of modules changed based on the situation. We apply this to navigation tasks in a corridor and show simulation results. When we give a map and path designation to the robot, we use a handwriting map interface. We compare object-oriented design SA and PMF with our proposal and show how inefficient actions are reduced using our proposal.
Bioinspired navigation strategies require landmark identification subsystems for goal identification. Door recognition is a key problem to be solved during mobile robot navigation. Doors give access to many locations ...
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ISBN:
(纸本)9781424417612
Bioinspired navigation strategies require landmark identification subsystems for goal identification. Door recognition is a key problem to be solved during mobile robot navigation. Doors give access to many locations that are defined as goals for the robot. This paper presents an approach to door identification by means of recognition of the door handle. Rather than using the fines defined by the door blades, the region of interest of an image is extracted by means of the Hough transform and afterwards SIFT keypoints are obtained and matched against a database in order to positively recognize the door. The extraction of the ROI highly reduces the computational load of the SIFT algorithm and increases the handle recognition performance. The approach is evaluated and tested on a real Peoplebot robot, using a behavior-based control architecture that combines the identification module with free-space balancing (for corridor following), door knocking and crossing behaviors.
In the paper a novel approach to representation of a history in a mobile robot navigation is presented. The main assumptions and key definitions of the proposed approach are discussed in this paper. An application of ...
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ISBN:
(纸本)9789728865832
In the paper a novel approach to representation of a history in a mobile robot navigation is presented. The main assumptions and key definitions of the proposed approach are discussed in this paper. An application of the method to detection a dead end situations that may occur during the work of reactive navigation systems is presented. The potential field method is used to create an algorithm that gets the robot out of the dead-lock. Simulations that show the effectiveness of the proposed method are also presented.
The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper di...
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ISBN:
(纸本)9781595936974
The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behavior framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behavior design. Competitive evolution of the behavior population incrementally develops feasible Solutions for the domain through competitive ranking. By developing and implementing many simple behaviors independently and then evolving a complex behavior structure suited to the domain, this approach allows for the reuse of elemental behaviors and cases the complexity of development for a given domain. Additionally. this approach has the ability to locate a behavior structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behavior structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert.
This paper proposes a mobile printer system (MPS) based on multi-robot cooperation. The system consists of multiple mobile robots. a wireless LAN system, a graphic user interface (GUI), and a host computer. The GUI co...
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This paper proposes a mobile printer system (MPS) based on multi-robot cooperation. The system consists of multiple mobile robots. a wireless LAN system, a graphic user interface (GUI), and a host computer. The GUI comprises a user input section, a task allocation optimization section, and a control and Communication section. Its operation is as follows: a user draws a picture oil all input window of the GUI, and then the host Computer commands client printer-robots to reproduce the same oil a paper in a finite time. To control multiple robots during this process, two kinds of multi-robot control architectures along with a collision-free arbitration configuration are proposed. One is a decentralized control architecture, which employs subsumption architecture based on behavior-based robotics. The robots continue to seek the nearest line and to draw it repeatedly until all lines are drawn. This architecture needs no pre-planning and is fault-tolerant. Another is a centralized control architecture, which employs an evolutionary algorithm (EA). The host computer optimizes the task (finding time-optimal path) allocation for each robot by using all evolutionary algorithm and sends the optimized job sequence to the robots. To minimize the elapsed time in drawing all the lines, an evolutionary algorithm with a representation of an individual Suitable for the MPS is employed for the task optimization. This architecture call minimize the elapsed time effectively and offers the option of distance-optimality in addition to time-optimality. The proposed architectures are simulated and real experiments with three omni-directional robots are carried out to demonstrate the effectiveness and the applicability of the proposed mobile printer system. (C) 2005 Elsevier B.V. All rights reserved.
The generation of a complete robotic brain for locomotion based on the utility function (UF) method for behavioral organization is demonstrated. A simulated, single-legged hopping robot is considered, and a two-stage ...
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The generation of a complete robotic brain for locomotion based on the utility function (UF) method for behavioral organization is demonstrated. A simulated, single-legged hopping robot is considered, and a two-stage process is used for generating the robotic brain. First, individual behaviors are constructed through artificial evolution of recurrent neural networks (RNNs). Thereafter, a behavioral organizer is generated through evolutionary optimization of utility functions. Two systems are considered: a simplified model with trivial dynamics, as well as a model using full newtonian dynamics. In both cases, the UF method was able to generate an adequate behavioral organizer, which allowed the robot to perform its primary task of moving through an arena, while avoiding collisions with obstacles and keeping the batteries sufficiently charged. The results for the simplified model were better than those for the dynamical model, a fact that could be attributed to the poor performance of the individual behaviors (implemented as RNNs) during extended operation.
This paper presents our approach to extend the niche of behavior-based robotics toward manipulation. We use results from neuroscience to derive some qualitative design rules for the mechanics of the manipulator, resul...
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This paper presents our approach to extend the niche of behavior-based robotics toward manipulation. We use results from neuroscience to derive some qualitative design rules for the mechanics of the manipulator, resulting in a next-generation manipulator, the "soft arm" By defining the basic behaviors of the manipulator as trajectory-producing behaviors (which is also biologically plausible), we have designed a first test case: writing on a board with a mobile manipulator. The soft arm has not yet been developed;therefore, we have emulated such a soft robot arm on an industrial robot.
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