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Mobile Robot Laboratory
Mobile Robot Laboratory
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ItemSpatio-Temporal Case-Based Reasoning for Efficient Reactive Robot Navigation(Georgia Institute of Technology, 2005) Likhachev, Maxim ; Kaess, Michael ; Kira, Zsolt ; Arkin, Ronald C.This paper presents an approach to automatic selection and modification of behavioral assemblage parameters for autonomous navigation tasks. The goal of this research is to make obsolete the task of manual configuration of behavioral parameters, which often requires significant knowledge of robot behavior and extensive experimentation, and to increase the efficiency of robot navigation by automatically choosing and fine-tuning the parameters that fit the robot task-environment well in real-time. The method is based on the Case-Based Reasoning paradigm. Derived from incoming sensor data, this approach computes spatial features of the environment. Based on the robot’s performance, temporal features of the environment are then computed. Both sets of features are then used to select and fine-tune a set of parameters for an active behavioral assemblage. By continuously monitoring the sensor data and performance of the robot, the method reselects these parameters as necessary. While a mapping from environmental features onto behavioral parameters, i.e., the cases, can be hard-coded, a method for learning new and optimizing existing cases is also presented. This completely automates the process of behavioral parameterization. The system was integrated within a hybrid robot architecture and extensively evaluated using simulations and indoor and outdoor real world robotic experiments in multiple environments and sensor modalities, clearly demonstrating the benefits of the approach.
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ItemIncremental Replanning for Mapping(Georgia Institute of Technology, 2002) Koenig, Sven ; Likhachev, MaximIncremental heuristic search methods can often replan paths much faster than incremental or heuristic search methods individually, yet are simple to use. So far, they have only been used in mobile robotics to move a robot to given goal coordinates in unknown terrain. As far as we know, incremental heuristic search methods have not yet been applied to the problem of mapping unknown terrain. In this paper, we therefore describe how to apply our incremental heuristic search method D* Lite, that combines ideas from Lifelong Planning A* and Focussed D*, to mapping unknown terrain, which is rather nontrivial. We then compare its runtime against that of incremental search and heuristic search alone, demonstrating the computational benefits of their combination. By demonstrating the versatility and computational benefits of incremental heuristic search, we hope that this underexploited technique will be used more often in mobile robotics.
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ItemImproved Fast Replanning for Robot Navigation in Unknown Terrain(Georgia Institute of Technology, 2002) Koenig, Sven ; Likhachev, MaximMobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz’ Focussed Dynamic A* is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan one to two orders of magnitudes faster than planning from scratch since it modifies previous search results locally. Consequently, it has been extensively used in mobile robotics. In this paper, we introduce an alternative to Focussed Dynamic A* that implements the same navigation strategy but is algorithmically different. Focussed Dynamic A* Lite is simpler, easier to understand, easier to analyze and easier to extend than Focussed Dynamic A*, yet is more efficient. We believe that our results will make D*-like replanning algorithms even more popular and enable robotics researchers to adapt them to additional applications.
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ItemLearning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning(Georgia Institute of Technology, 2001) Arkin, Ronald C. ; Kaess, Michael ; Likhachev, MaximThis paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It is based on our previous work on a behavior-based robotic system that also employed spatio-temporal case-based reasoning [3] in the selection of behavioral parameters but was not capable of learning new parameterizations. The present method extends the case-based reasoning module by making it capable of learning new and optimizing the existing cases where each case is a set of behavioral parameters. The learning process can either be a separate training process or be part of the mission execution. In either case, the robot learns an optimal parameterization of its behavior for different environments it encounters. The goal of this research is not only to automatically optimize the performance of the robot but also to avoid the manual configuration of behavioral parameters and the initial configuration of a case library, both of which require the user to possess good knowledge of robot behavior and the performance of numerous experiments. The presented method was integrated within a hybrid robot architecture and evaluated in extensive computer simulations, showing a significant increase in the performance over a nonadaptive system and a performance comparable to a non-learning CBR system that uses a hand-coded case library.
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ItemSelection of Behavioral Parameters: Integration of Discontinuous Switching via Case-Based Reasoning with Continuous Adaptation via Learning Momentum(Georgia Institute of Technology, 2001) Arkin, Ronald C. ; Lee, J. Brian ; Likhachev, MaximThis paper studies the effects of the integration of two learning algorithms, Case-Based Reasoning (CBR) and Learning Momentum (LM), for the selection of behavioral parameters in real-time for robotic navigational tasks. Use of CBR methodology in the selection of behavioral parameters has already shown significant improvement in robot performance [3, 6, 7, 14] as measured by mission completion time and success rate. It has also made unnecessary the manual configuration of behavioral parameters from a user. However, the choice of the library of CBR cases does affect the robot's performance, and choosing the right library sometimes is a difficult task especially when working with a real robot. In contrast, Learning Momentum does not depend on any prior information such as cases and searches for the "right" parameters in real-time. This results in high mission success rates and requires no manual configuration of parameters, but it shows no improvement in mission completion time [2]. This work combines the two approaches so that CBR discontinuously switches behavioral parameters based on given cases whereas LM uses these parameters as a starting point for the real-time search for the "right" parameters. The integrated system was extensively evaluated on both simulated and physical robots. The tests showed that on simulated robots the integrated system performed as well as the CBR only system and outperformed the LM only system, whereas on real robots it significantly outperformed both CBR only and LM only systems.
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ItemRobotic Comfort Zones(Georgia Institute of Technology, 2000) Arkin, Ronald C. ; Likhachev, MaximThis paper investigates how the psychological notion of comfort can be useful in the design of robotic systems. A review of the existing study of human comfort, especially regarding its presence in infants, is conducted with the goal being to determine the relevant characteristics for mapping it onto the robotics domain. Focus is placed on the identification of the salient features in the environment that affect the comfort level. Factors involved include current state familiarity, working conditions, the amount and location of available resources, etc. As part of our newly developed comfort function theory, the notion of an object as a psychological attachment for a robot is also introduced, as espoused in Bowlby's theory of attachment. The output space of the comfort function and its dependency on the comfort level are analyzed. The results of the derivation of this comfort function are then presented in terms of the impact they have on robotic behavior. Justification for the use of the comfort function in the domain of robotics is presented with relevance for real-world operations. Also, a transformation of the theoretical discussion into a mathematical framework suitable for implementation within a behavior-based control system is presented. The paper concludes with results of simulation studies and real robot experiments using the derived comfort function.
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ItemSpatio-Temporal Case-Based Reasoning for Behavioral Selection(Georgia Institute of Technology, 2000) Arkin, Ronald C. ; Likhachev, MaximThis paper presents the application of a Case-Based Reasoning approach to the selection and modification of behavioral assemblage parameters. The goal of this research is to achieve an optimal parameterization of robotic behaviors in run-time. This increases robot performance and makes a manual configuration of parameters unnecessary. The case-based reasoning module selects a set of parameters for an active behavioral assemblage in real-time. This set of parameters fits the environment better than hand-coded ones, and its performance is monitored providing feedback for a possible reselection of the parameters. This paper places a significant emphasis on the technical details of the case-based reasoning module and how it is integrated within a schema-based reactive navigation system. The paper also presents the results and evaluation of the system in both in simulation and real world robotic experiments.