Journal articles: 'Probabilistic Robotics'

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Thrun, Sebastian. "Probabilistic robotics." Communications of the ACM 45, no. 3 (March 2002): 52–57. http://dx.doi.org/10.1145/504729.504754.

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Martin, Fred, James Dalphond, and Nat Tuck. "Teaching Localization in Probabilistic Robotics." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 3 (October 4, 2021): 2373–74. http://dx.doi.org/10.1609/aaai.v26i3.18955.

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In the field of probabilistic robotics, a central problem is to determine a robot’s state given knowledge of a time series of control commands and sensor readings. The effects of control commands and the behavior of sensor devices are both modeled probabilistically. A variety of methods are available for deriving the robot’s belief state, which is a probabilistic representation of the robot’s true state (which cannot be directly known). This paper presents a series of five weekly assignments to teach this material at the advanced undergraduate/graduate level. The theoretical aspect of the work is reinforced by practical implementation exercises using ROS (Robot Operating System), and the Bilibot, an educational robot platform.

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Paraschos, Alexandros, Christian Daniel, Jan Peters, and Gerhard Neumann. "Using probabilistic movement primitives in robotics." Autonomous Robots 42, no. 3 (July 15, 2017): 529–51. http://dx.doi.org/10.1007/s10514-017-9648-7.

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KIM, Myungsik, Jong-Min KIM, and Kwangsoo KIM. "2P1-J01 RFID based objects positioning using probabilistic geometrical relations(Network Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2P1—J01_1—_2P1—J01_2. http://dx.doi.org/10.1299/jsmermd.2011._2p1-j01_1.

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Watanabe, Keigo, Shoichi Maeyama, Tetsuo Tomizawa, Ryuichi Ueda, and Masahiro Tomono. "Special Issue on Probabilistic Robotics and SLAM." Journal of Robotics and Mechatronics 31, no. 2 (April 20, 2019): 179. http://dx.doi.org/10.20965/jrm.2019.p0179.

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Intelligent mobile robots need self-localization, map generation, and the ability to explore unknown environments autonomously. Probabilistic processing can be applied to overcome the problems of movement uncertainties and measurement errors. Probabilistic robotics and simultaneous localization and mapping (SLAM) technologies are therefore strongly related, and they have been the focus of many studies. As more and more practical applications are found for intelligent mobile robots, such as for autonomous driving and cleaning, the applicability of these techniques has been increasing. In this special issue, we provide a wide variety of very interesting papers ranging from studies and developments in applied SLAM technologies to fundamental theories for SLAM. There are five academic papers, one each on the following topics: first visit navigation, controls for following rescue clues, indoor localization using magnetic field maps, a new solution for self-localization using downhill simplex method, and object detection for long-term map management through image-based learning. In addition, in the next number, there will be a review paper by Tsukuba University’s Prof. Tsubouchi, who is famous for the Tsukuba Challenge and research related to mobile robotics. We editors hope this special issue will help readers to develop mobile robots and use SLAM technologies and probabilistic approaches to produce successful applications.

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Ye, Kangfeng, Ana Cavalcanti, Simon Foster, Alvaro Miyazawa, and Jim Woodcock. "Probabilistic modelling and verification using RoboChart and PRISM." Software and Systems Modeling 21, no. 2 (October 3, 2021): 667–716. http://dx.doi.org/10.1007/s10270-021-00916-8.

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AbstractRoboChart is a timed domain-specific language for robotics, distinctive in its support for automated verification by model checking and theorem proving. Since uncertainty is an essential part of robotic systems, we present here an extension to RoboChart to model uncertainty using probabilism. The extension enriches RoboChart state machines with probability through a new construct: probabilistic junctions as the source of transitions with a probability value. RoboChart has an accompanying tool, called RoboTool, for modelling and verification of functional and real-time behaviour. We present here also an automatic technique, implemented in RoboTool, to transform a RoboChart model into a PRISM model for verification. We have extended the property language of RoboTool so that probabilistic properties expressed in temporal logic can be written using controlled natural language.

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Patle, B. K., D. R. Parhi, A. Jagadeesh, and Sunil Kumar Kashyap. "Probabilistic fuzzy controller based robotics path decision theory." World Journal of Engineering 13, no. 2 (April 8, 2016): 181–92. http://dx.doi.org/10.1108/wje-04-2016-024.

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Purpose This paper aims to comprise the new ideas for the efficient implementation to autonomous mobile robot navigation over the complex environment in presence of static obstacles. Design/methodology/approach The fuzzy decision via probability and its distribution is applied for the generating objective function subject to the robotics path and obstacle avoidance. The present objective function is formed to achieve high level of significance for the real-time obstacle avoidance and the efficiency. Findings The proposed controller makes a robot take its decision effectively in complex environment in a feasible time. In comparison with other AI approaches the proposed controller reflects that the proposed method outperforms in terms of optimal path and quality of solution. The experimental and simulation results are nearly same. Originality/value It has been tested in a complex crowded environment to find shorter path than existed approaches. For the validation, the experimental and simulation result using Matlab Software (R2008) has provided at the end of the paper.

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Damgaard, Malte Rørmose, Rasmus Pedersen, and Thomas Bak. "Study of Variational Inference for Flexible Distributed Probabilistic Robotics." Robotics 11, no. 2 (March 24, 2022): 38. http://dx.doi.org/10.3390/robotics11020038.

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By combining stochastic variational inference with message passing algorithms, we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases, the robots share the operating space and need to cross each other’s paths multiple times without colliding.

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Matsumoto, Yoshio, and Kae Doki. "On special issue “Probabilistic Approach to Robotics”." Journal of the Robotics Society of Japan 29, no. 5 (2011): 403. http://dx.doi.org/10.7210/jrsj.29.403.

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Sturm, J., C. Stachniss, and W. Burgard. "A Probabilistic Framework for Learning Kinematic Models of Articulated Objects." Journal of Artificial Intelligence Research 41 (August 30, 2011): 477–526. http://dx.doi.org/10.1613/jair.3229.

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Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.

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Mori, Taketoshi. "Special Issue on Human Modeling in Robotics." Journal of Robotics and Mechatronics 17, no. 6 (December 20, 2005): 607. http://dx.doi.org/10.20965/jrm.2005.p0607.

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Human modeling is becoming an essential key technology for robotics and mechatronics systems that aid and expand human activities. Human modeling is indispensable in designing systems that conduct tasks difficult or even impossible for human beings to accomplish. Such systems include humanoid robots, power assistance suits, communication robots, intelligent support rooms, and user interface devices. This special issue focuses on the latest state-of-the-art human modeling research, especially in robotics, presenting a wide variety of human modeling areas. To support human beings in real-world environments, human behavior model is considerably important. Adaptation to personal characteristics may be the core function of next-generation system mechanisms, and human social modeling is the principal focus of interfacing for interaction systems. Cognitive and psychological models of human beings have always been an important domain in human-machine systems. Probabilistic and static methods have attracted attention in this research field. Not only mechanical but physiological human modeling may soon become 'vital' for all kind of robotic systems. This special issue is the kernel node for cultivating these rapidly advancing areas. I thank the authors of the articles in this issue for their invaluable effort and contributions. I also thank the members of the Editorial board, without whose work this special issue would not have been possible.

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SHON, AARON P., JOSHUA J. STORZ, ANDREW N. MELTZOFF, and RAJESH P. N. RAO. "A COGNITIVE MODEL OF IMITATIVE DEVELOPMENT IN HUMANS AND MACHINES." International Journal of Humanoid Robotics 04, no. 02 (June 2007): 387–406. http://dx.doi.org/10.1142/s0219843607001059.

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Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals offer a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. We present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. Bayesian algorithms, based on Meltzoff and Moore's AIM hypothesis for action imitation, implement the core of an imitation learning framework. Our algorithms are computationally efficient, allowing real-time learning and imitation in an active stereo vision robotic head and on a humanoid robot. We present simulated and real-world robotics results demonstrating the viability of our approach. We conclude by advocating a research agenda that promotes interaction between cognitive and robotic studies of imitation.

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Jain, Siddarth, and Brenna Argall. "Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics." ACM Transactions on Human-Robot Interaction 9, no. 1 (January 31, 2020): 1–23. http://dx.doi.org/10.1145/3359614.

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Kovalchuk, Alexander, Shashank Shekhar, and Ronen I. Brafman. "Verifying Plans and Scripts for Robotics Tasks Using Performance Level Profiles." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 673–81. http://dx.doi.org/10.1609/icaps.v31i1.16016.

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Performance-Level Profiles (PLPs) were introduced as a type of action representation language suitable for capturing the behavior of functional code for robotics. This paper addresses two issues that PLPs raise: (1) Their formal semantics. (2) How to verify a script or a plan that schedule the use of components that have been documented by PLPs. We provide a formal semantics for PLPs by mapping them to probabilistic timed automata (PTAs). We also show how, given a script that refers to components specified using PLPs, we derive a PTA specification of the entire system. This PTA can be used to verify the system’s properties and answers queries about its behavior. Finally, we empirically evaluate an implemented system based on these ideas, demonstrating its scalability. The result is a pragmatic approach for verifying component-based robotic systems.

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Jaramillo-Cabrera, Esteban, Eduardo F. Morales, and Jose Martinez-Carranza. "Enhancing object, action, and effect recognition using probabilistic affordances." Adaptive Behavior 27, no. 5 (April 12, 2019): 295–306. http://dx.doi.org/10.1177/1059712319839057.

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Recent advances in deep learning, in particular in convolutional neural networks (CNNs), have been widely used in robotics for object classification and action recognition, among others, with very high performance. Nevertheless, this high performance, mostly in classification tasks, is rarely accompanied by reasoning processes that consider the relationships between objects, actions, and effects. In this article, we used three CNNs to classify objects, actions, and effects that were trained with the CERTH-SOR3D dataset that has more than 20,000 RGB-D videos. This dataset involves 14 objects, 13 actions, and in this article was augmented with seven effects. The probabilistic vector output of each trained CNN was combined into a Bayesian network (BN) to capture the relationships between objects, actions, and effects. It is shown that by probabilistically combining information from the three classifiers, it is possible to improve the classification performance of each CNN or to level the same performance with less training data. In particular, the recognition performance improved from 71.2% to 79.7% for actions, 85.0%–86.7% for objects, and 77.0%–82.1% for effects. In the article, it is also shown that with missing information, the model can still produce reasonable classification performance. In particular, the system can be used for reasoning purposes in robotics, as it can make action planning with information from object and effects or it can predict effects with information from objects and actions.

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Valiente, David, Luis Payá, José M. Sebastián, Luis M. Jiménez, and Oscar Reinoso. "Dynamic Catadioptric Sensory Data Fusion for Visual Localization in Mobile Robotics." Proceedings 15, no. 1 (July 5, 2019): 2. http://dx.doi.org/10.3390/proceedings2019015002.

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This approach presents a localization technique within mobile robotics sustained by visual sensory data fusion. A regression inference framework is designed with the aid of informative data models of the system, together with support of probabilistic techniques such as Gaussian Processes. As a result, the visual data acquired with a catadioptric sensor is fused between poses of the robot in order to produce a probability distribution of visual information in the 3D global reference of the robot. In addition, a prediction technique based on filter gain is defined to improve the matching of visual information extracted from the probability distribution. This work reveals an enhanced matching technique for visual information in both, the image reference frame, and the 3D global reference. Real data results are presented to confirm the validity of the approach when working in a mobile robotic application for visual localization. Besides, a comparison against standard visual matching techniques is also presented. The suitability and robustness of the contributions are tested in the presented experiments.

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Stein, Alfred. "Spatial statistics in geographical information science: from interpolation to probabilistic robotics." Annals of GIS 16, no. 4 (December 17, 2010): 211–21. http://dx.doi.org/10.1080/19475683.2010.539986.

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Takahashi, Takeshi, Michael Lanighan, and Roderic Grupen. "Hybrid Task Planning Grounded in Belief: Constructing Physical Copies of Simple Structures." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 567–71. http://dx.doi.org/10.1609/icaps.v27i1.13859.

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Symbolic planning methods have proved to be challenging in robotics due to partial observability and noise as well as unavoidable exceptions to rules that symbol semantics depend on. Often the symbols that a robot considers to support for planning are brittle, making them unsuited for even relatively short term use. Maturing probabilistic methods in robotics, however, are providing a sound basis for symbol grounding that supports using probabilistic distributions over symbolic entities as the basis for planning. In this paper, we describe a belief-space planner that stabilizes the semantics of feedback from the environment by actively interacting with a scene. When distributions over higher-level abstractions stabilize, powerful symbolic planning techniques can provide reliable guidance for problem solving. We present such an approach in a hybrid planning scheme that actively controls uncertainty and yields robust state estimation with bounds on uncertainty that can make effective use of powerful symbolic planning techniques. We illustrate the idea in a hybrid planner for autonomous construction tasks with a real robot system.

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Sridharan, Mohan, Michael Gelfond, Shiqi Zhang, and Jeremy Wyatt. "REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics." Journal of Artificial Intelligence Research 65 (June 17, 2019): 87–180. http://dx.doi.org/10.1613/jair.1.11524.

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This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

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Lioutikov, Rudolf, Guilherme Maeda, Filipe Veiga, Kristian Kersting, and Jan Peters. "Learning attribute grammars for movement primitive sequencing." International Journal of Robotics Research 39, no. 1 (November 17, 2019): 21–38. http://dx.doi.org/10.1177/0278364919868279.

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Movement primitives are a well studied and widely applied concept in modern robotics. However, composing primitives out of an existing library has shown to be a challenging problem. We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of primitives. The rule-based nature of formal grammars allows an intuitive encoding of hierarchically structured tasks. This hierarchical concept strongly connects with the way robot policies can be learned, organized, and re-used. However, the induction of context-free grammars has proven to be a complicated and yet unsolved challenge. We exploit the physical nature of robot movement primitives to restrict and efficiently search the grammar space. The grammar is learned by applying a Markov chain Monte Carlo optimization over the posteriors of the grammars given the observations. The proposal distribution is defined as a mixture over the probabilities of the operators connecting the search space. Moreover, we present an approach for the categorization of probabilistic movement primitives and discuss how the connectibility of two primitives can be determined. These characteristics in combination with restrictions to the operators guarantee continuous sequences while reducing the grammar space. In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks with the capability to adapt single primitives within the sequence. The method was validated on tasks that require the generation of complex sequences consisting of simple movement primitives using a seven-degree-of-freedom lightweight robotic arm.

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Feng, Bin, and Yang Liu. "An Improved RRT Based Path Planning with Safe Navigation." Applied Mechanics and Materials 494-495 (February 2014): 1080–83. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1080.

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Path planning has been a crucial problem in robotics research. Some algorithms, such as Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT), have been proposed to tackle this problem. However, these algorithms have their own limitations when applying in a real-world domain, such as RoboCup Small-Size League (SSL) competition. This paper raises a novel improvement to the existing RRT algorithm to make it more applicable in real-world.

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Vázquez, Andrés S., and Antonio Adán. "Nonprobabilistic anytime algorithm for high-quality trajectories in high-dimensional spaces." Robotica 30, no. 2 (June 23, 2011): 289–303. http://dx.doi.org/10.1017/s0263574711000506.

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SUMMARYThis paper proposes a feasible variation of approximate grid-based path-planning methods, which have been replaced with probabilistic methods nowadays, due mainly to their impracticality in high-dimensional spaces. Our aim is to demonstrate that, with an incremental exploration of the CSpace by means of Interpolated walk primitives and ANY-Time algorithms, we can generate – online – high-quality solutions that can be compared with probabilistic methods and can even improve some aspects, such as the completeness. Computational time, path smoothness, path clearance, and path distance are the qualifiers used to evaluate the path planner. These quality factors are critical in robotics. In fact, in both mobile and industrial robots, the computational time is a primary requirement to work online, the clearance gives security to the movements of the robot, and the smoothness can prolong the life of the mechanical components. Our method has been compared to probabilistic path planners, with the feasibility and benefits of our algorithm being proved in terms of the quality factors aforementioned.

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Cheng, Po-Jen, Yuan-Hsun Liao, and Pao-Ta Yu. "Micro:bit Robotics Course: Infusing Logical Reasoning and Problem-Solving Ability in Fifth Grade Students Through an Online Group Study System." International Review of Research in Open and Distributed Learning 22, no. 1 (March 11, 2021): 21–40. http://dx.doi.org/10.19173/irrodl.v22i1.4844.

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With rising societal interest in the subject areas of science, technology, engineering, art and mathematics (STEAM), a micro:bit robotics course with an online group study (OGS) system was designed to foster student learning anytime and anywhere. OGS enables the development of a learning environment that combines real-world and digital-world resources, and can enhance the effectiveness of learning among students from a remote area. In this pre- and post-test experiment design, we studied 22 (8 males and 14 females) 5th grade students from a remote area of Taiwan. A t test performed before and after the robotics course showed a positive increase in students’ proportional reasoning, probabilistic reasoning, and ability to analyze a problem. Results also revealed a gender difference in the association between students’ logical reasoning and problem-solving ability.

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Wang, Zhi, Liron Cohen, Sven Koenig, and T. K. Satish Kumar. "The Factored Shortest Path Problem and Its Applications in Robotics." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 527–31. http://dx.doi.org/10.1609/icaps.v28i1.13932.

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Many real-world combinatorial problems exhibit structure in the way in which their variables interact. Such structure can be exploited in the form of "factors" for representational as well as computational benefits. Factored representations are extensively used in probabilistic reasoning, constraint satisfaction, planning, and decision theory. In this paper, we formulate the factored shortest path problem (FSPP) on a collection of constraints interpreted as factors of a high-dimensional map. We show that the FSPP is not only a generalization of the regular shortest path problem but also particularly relevant to robotics. We develop factored-space heuristics for A* and prove that they are admissible and consistent. We provide experimental results on both random and handcrafted instances as well as on an example robotics domain to show that A* with factored-space heuristics outperforms A* with the Manhattan Distance heuristic in many cases.

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Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

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Cárdenas, Edwin Francis, Luis Miguel Mendez, and Jorge Sofrony Esmeral. "Collision-free path planning in multi-dimensional environments." Ingeniería e Investigación 31, no. 2 (May 1, 2011): 5–17. http://dx.doi.org/10.15446/ing.investig.v31n2.23458.

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Reliable path-planning and generation of collision-free trajectories has become an area of active research over the past decade where the field robotics has probably been the most active area. This paper' s main objective is to analyse the advantages and disadvantages of two of the most popular techniques used in collision-free trajectory generation in n-dimensional spaces. The importance of analysing such techniques within a generalised framework is evident as path-planning is used in a variety of fields such as designing medical drugs, computer animation and artificial intelligence and, of course, robotics. The review provided in this paper starts by drawing a historical map of path-planning and the techniques used in its early stages. The main concepts involved in artificial potential fields and probabilistic roadmaps will be addressed as these are the most influential methods and have been widely used in specialised literature.

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Sequeira, João S., and Duarte Gameiro. "A Probabilistic Approach to RFID-Based Localization for Human-Robot Interaction in Social Robotics." Electronics 6, no. 2 (April 17, 2017): 32. http://dx.doi.org/10.3390/electronics6020032.

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Bongard, Josh. "Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. (2005, MIT Press.) 647 pages." Artificial Life 14, no. 2 (April 2008): 227–29. http://dx.doi.org/10.1162/artl.2008.14.2.227.

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Li, Jie, and Ying Tan. "A probabilistic finite state machine based strategy for multi-target search using swarm robotics." Applied Soft Computing 77 (April 2019): 467–83. http://dx.doi.org/10.1016/j.asoc.2019.01.023.

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Mirzaei, Faezeh, Ali Akbar Pouyan, and Mohsen Biglari. "Automatic Controller Code Generation for Swarm Robotics Using Probabilistic Timed Supervisory Control Theory (ptSCT)." Journal of Intelligent & Robotic Systems 100, no. 2 (September 24, 2020): 729–50. http://dx.doi.org/10.1007/s10846-020-01201-4.

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Kupcsik, Andras, Marc Deisenroth, Jan Peters, and Gerhard Neumann. "Data-Efficient Generalization of Robot Skills with Contextual Policy Search." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1401–7. http://dx.doi.org/10.1609/aaai.v27i1.8546.

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In robotics, controllers make the robot solve a task within a specific context. The context can describe the objectives of the robot or physical properties of the environment and is always specified before task execution. To generalize the controller to multiple contexts, we follow a hierarchical approach for policy learning: A lower-level policy controls the robot for a given context and an upper-level policy generalizes among contexts. Current approaches for learning such upper-level policies are based on model-free policy search, which require an excessive number of interactions of the robot with its environment. More data-efficient policy search approaches are model based but, thus far, without the capability of learning hierarchical policies. We propose a new model-based policy search approach that can also learn contextual upper-level policies. Our approach is based on learning probabilistic forward models for long-term predictions. Using these predictions, we use information-theoretic insights to improve the upper-level policy. Our method achieves a substantial improvement in learning speed compared to existing methods on simulated and real robotic tasks.

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Tsubouchi, Takashi, and Keiji Nagatani. "Special Issue on Modern Trends in Mobile Robotics." Journal of Robotics and Mechatronics 14, no. 4 (August 20, 2002): 323. http://dx.doi.org/10.20965/jrm.2002.p0323.

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Since the dawning of the Robotics age, mobile robots have been important objectives of research and development. Working from such aspects as locomotion mechanisms, path and motion planning algorithms, navigation, map building and localization, and system architecture, researchers are working long and hard. Despite the fact that mobile robotics has a shorter history than conventional mechanical engineering, it has already accumulated a major, innovative, and rich body of R&D work. Rapid progress in modern scientific technology had advanced to where down-sized low-cost electronic devices, especially highperformance computers, can now be built into such mobile robots. Recent trends in ever higher performance and increased downsizing have enabled those working in the field of mobile robotics to make their models increasingly intelligent, versatile, and dexterous. The down-sized computer systems implemented in mobile robots must provide high-speed calculation for complicated motion planning, real-time image processing in image recognition, and sufficient memory for storing the huge amounts of data required for environment mapping. Given the swift progress in electronic devices, new trends are now emerging in mobile robotics. This special issue on ""Modern Trends in Mobile Robotics"" provides a diverse collection of distinguished papers on modern mobile robotics research. In the area of locomotion mechanisms, Huang et al. provide an informative paper on control of a 6-legged walking robot and Fujiwara et al. contribute progressive work on the development of a practical omnidirectional cart. Given the importance of vision systems enabling robots to survey their environments, Doi et al., Tang et al., and Shimizu present papers on cutting-edge vision-based navigation. On the crucial subject of how to equip robots with intelligence, Hashimoto et al. present the latest on sensor fault detection in dead-reckoning, Miura et al. detail the probabilistic modeling of obstacle motion during mobile robot navigation, Hada et al. treat long-term mobile robot activity, and Lee et al. explore mobile robot control in intelligent space. As guest editors, we are sure readers will find these articles both informative and interesting concerning current issues and new perspectives in modern trends in mobile robotics.

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Oge, Yuto. "Project on Student Editorial Committee: Report on the 39th Annual Conference of the Robotics Society of Japan (General Session: Probabilistic Robotics and Data Engineering Robotics 〜 Recognition,・Action Learning・Symbolic Emergence〜)." Journal of the Robotics Society of Japan 40, no. 8 (2022): 692–93. http://dx.doi.org/10.7210/jrsj.40.692.

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Vinanzi, Samuele, Massimiliano Patacchiola, Antonio Chella, and Angelo Cangelosi. "Would a robot trust you? Developmental robotics model of trust and theory of mind." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1771 (March 11, 2019): 20180032. http://dx.doi.org/10.1098/rstb.2018.0032.

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Trust is a critical issue in human–robot interactions: as robotic systems gain complexity, it becomes crucial for them to be able to blend into our society by maximizing their acceptability and reliability. Various studies have examined how trust is attributed by people to robots, but fewer have investigated the opposite scenario, where a robot is the trustor and a human is the trustee. The ability for an agent to evaluate the trustworthiness of its sources of information is particularly useful in joint task situations where people and robots must collaborate to reach shared goals. We propose an artificial cognitive architecture based on the developmental robotics paradigm that can estimate the trustworthiness of its human interactors for the purpose of decision making. This is accomplished using Theory of Mind (ToM), the psychological ability to assign to others beliefs and intentions that can differ from one’s owns. Our work is focused on a humanoid robot cognitive architecture that integrates a probabilistic ToM and trust model supported by an episodic memory system. We tested our architecture on an established developmental psychological experiment, achieving the same results obtained by children, thus demonstrating a new method to enhance the quality of human and robot collaborations. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

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Kuroda, Eri. "Project on Student Editorial Committee: Report on the 40th Annual Conference of the Robotics Society of Japan（Probabilistic Robotics and Data Engineering Robotics 〜Recognition, Behavioral Learning, and Symbolic Emergence〜（1/4））." Journal of the Robotics Society of Japan 41, no. 1 (2023): 44–45. http://dx.doi.org/10.7210/jrsj.41.44.

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Kuroda, Eri. "Project on Student Editorial Committee: Report on the 40th Annual Conference of the Robotics Society of Japan (Probabilistic Robotics and Data Engineering Robotics 〜Recognition, Behavioral Learning, and Symbolic Emergence〜（3/4））." Journal of the Robotics Society of Japan 41, no. 1 (2023): 46–47. http://dx.doi.org/10.7210/jrsj.41.46.

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Arun Srivatsan, Rangaprasad, Mengyun Xu, Nicolas Zevallos, and Howie Choset. "Probabilistic pose estimation using a Bingham distribution-based linear filter." International Journal of Robotics Research 37, no. 13-14 (June 25, 2018): 1610–31. http://dx.doi.org/10.1177/0278364918778353.

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Pose estimation is central to several robotics applications such as registration, hand–eye calibration, and simultaneous localization and mapping (SLAM). Online pose estimation methods typically use Gaussian distributions to describe the uncertainty in the pose parameters. Such a description can be inadequate when using parameters such as unit quaternions that are not unimodally distributed. A Bingham distribution can effectively model the uncertainty in unit quaternions, as it has antipodal symmetry, and is defined on a unit hypersphere. A combination of Gaussian and Bingham distributions is used to develop a truly linear filter that accurately estimates the distribution of the pose parameters. The linear filter, however, comes at the cost of state-dependent measurement uncertainty. Using results from stochastic theory, we show that the state-dependent measurement uncertainty can be evaluated exactly. To show the broad applicability of this approach, we derive linear measurement models for applications that use position, surface-normal, and pose measurements. Experiments assert that this approach is robust to initial estimation errors as well as sensor noise. Compared with state-of-the-art methods, our approach takes fewer iterations to converge onto the correct pose estimate. The efficacy of the formulation is illustrated with a number of examples on standard datasets as well as real-world experiments.

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Matsumoto, Takazumi, and Jun Tani. "Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network." Entropy 22, no. 5 (May 18, 2020): 564. http://dx.doi.org/10.3390/e22050564.

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It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.

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Sanner, Scott, and Ehsan Abbasnejad. "Symbolic Variable Elimination for Discrete and Continuous Graphical Models." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1954–60. http://dx.doi.org/10.1609/aaai.v26i1.8406.

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Probabilistic reasoning in the real-world often requires inference incontinuous variable graphical models, yet there are few methods for exact, closed-form inference when joint distributions are non-Gaussian. To address this inferential deficit, we introduce SVE -- a symbolic extension of the well-known variable elimination algorithm to perform exact inference in an expressive class of mixed discrete and continuous variable graphical models whose conditional probability functions can be well-approximated as piecewise combinations of polynomials with bounded support. Using this representation, we show that we can compute all of the SVE operations exactly and in closed-form, which crucially includes definite integration w.r.t. multivariate piecewise polynomial functions. To aid in the efficient computation and compact representation of this solution, we use an extended algebraic decision diagram (XADD) data structure that supports all SVE operations. We provide illustrative results for SVE on probabilistic inference queries inspired by robotics localization and tracking applications that mix various continuous distributions; this represents the first time a general closed-form exact solution has been proposed for this expressive class of discrete/continuous graphical models.

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OLSON, BRIAN, KEVIN MOLLOY, S. FARID HENDI, and AMARDA SHEHU. "GUIDING PROBABILISTIC SEARCH OF THE PROTEIN CONFORMATIONAL SPACE WITH STRUCTURAL PROFILES." Journal of Bioinformatics and Computational Biology 10, no. 03 (June 2012): 1242005. http://dx.doi.org/10.1142/s021972001242005x.

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The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.

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Kojima, Mayuka. "Project on Student Editorial Committee: Report on the 38th Annual Conference of the Robotics Society of Japan (Organized Session: Probabilistic Robotics and Data Engineering Robotics —Recognition, Action Learning and Symbol Emergence— (3/5))." Journal of the Robotics Society of Japan 39, no. 3 (2021): 247–48. http://dx.doi.org/10.7210/jrsj.39.247.

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Di Nuovo, Alessandro, and Angelo Cangelosi. "Abstract Concept Learning in Cognitive Robots." Current Robotics Reports 2, no. 1 (January 21, 2021): 1–8. http://dx.doi.org/10.1007/s43154-020-00038-x.

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Abstract Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.

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Ahmetoglu, Alper, M. Yunus Seker, Justus Piater, Erhan Oztop, and Emre Ugur. "DeepSym: Deep Symbol Generation and Rule Learning for Planning from Unsupervised Robot Interaction." Journal of Artificial Intelligence Research 75 (November 6, 2022): 709–45. http://dx.doi.org/10.1613/jair.1.13754.

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Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended environments. Therefore symbol formation and rule extraction should be considered part of robot learning, which, when done properly, will offer scalability, flexibility, and robustness. Towards this goal, we propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-grounded object, effect, and relational categories, we employ a binary bottleneck layer in a predictive, deep encoderdecoder network that takes the image of the scene and the action applied as input, and generates the resulting effects in the scene in pixel coordinates. After learning, the binary latent vector represents action-driven object categories based on the interaction experience of the robot. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, a decision tree is trained to reproduce its decoder function. Probabilistic rules are extracted from the decision paths of the tree and are represented in the Probabilistic Planning Domain Definition Language (PPDDL), allowing off-the-shelf planners to operate on the knowledge extracted from the sensorimotor experience of the robot. The deployment of the proposed approach for a simulated robotic manipulator enabled the discovery of discrete representations of object properties such as ‘rollable’ and ‘insertable’. In turn, the use of these representations as symbols allowed the generation of effective plans for achieving goals, such as building towers of the desired height, demonstrating the effectiveness of the approach for multi-step object manipulation. Finally, we demonstrate that the system is not only restricted to the robotics domain by assessing its applicability to the MNIST 8-puzzle domain in which learned symbols allow for the generation of plans that move the empty tile into any given position.

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Wang, Hanqing, Jiaolong Yang, Wei Liang, and Xin Tong. "Deep Single-View 3D Object Reconstruction with Visual Hull Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8941–48. http://dx.doi.org/10.1609/aaai.v33i01.33018941.

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3D object reconstruction is a fundamental task of many robotics and AI problems. With the aid of deep convolutional neural networks (CNNs), 3D object reconstruction has witnessed a significant progress in recent years. However, possibly due to the prohibitively high dimension of the 3D object space, the results from deep CNNs are often prone to missing some shape details. In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic singleview visual hull inside of the network. Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls. Experiment on both synthetic data and real images show that embedding a single-view visual hull for shape refinement can significantly improve the reconstruction quality by recovering more shapes details and improving shape consistency with the input image.

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Sridharan, Mohan. "Bootstrap Learning and Visual Processing Management on Mobile Robots." Advances in Artificial Intelligence 2010 (February 9, 2010): 1–20. http://dx.doi.org/10.1155/2010/765876.

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A central goal of robotics and AI is to enable a team of robots to operate autonomously in the real world and collaborate with humans over an extended period of time. Though developments in sensor technology have resulted in the deployment of robots in specific applications the ability to accurately sense and interact with the environment is still missing. Key challenges to the widespread deployment of robots include the ability to learn models of environmental features based on sensory inputs, bootstrap off of the learned models to detect and adapt to environmental changes, and autonomously tailor the sensory processing to the task at hand. This paper summarizes a comprehensive effort towards such bootstrap learning, adaptation, and processing management using visual input. We describe probabilistic algorithms that enable a mobile robot to autonomously plan its actions to learn models of color distributions and illuminations. The learned models are used to detect and adapt to illumination changes. Furthermore, we describe a probabilistic sequential decision-making approach that autonomously tailors the visual processing to the task at hand. All algorithms are fully implemented and tested on robot platforms in dynamic environments.

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Murata, Shingo, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, and Jun Tani. "Learning to Perceive the World as Probabilistic or Deterministic via Interaction With Others: A Neuro-Robotics Experiment." IEEE Transactions on Neural Networks and Learning Systems 28, no. 4 (April 2017): 830–48. http://dx.doi.org/10.1109/tnnls.2015.2492140.

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Nasuriwong, Surasak, and Peerapol Yuwapoositanon. "Gaussian Kernel Posterior Elimination for Fast Look-Ahead Rao-Blackwellised Particle Filtering for SLAM." Applied Mechanics and Materials 781 (August 2015): 555–58. http://dx.doi.org/10.4028/www.scientific.net/amm.781.555.

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In this paper, we explore a method for posterior elimination for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (Fast la-RBPF) algorithm for the simultaneous localization and mapping (SLAM) problem in the probabilistic robotics framework. In the case when a lot of SLAM states need to be estimated, large posterior states associated with the correct state may be outnumbered by multiple non-zero smaller posteriors. We show that by masking the low posterior weight states with a Gaussian kernel prior to weight selection the accuracy of the la-RBPF SLAM algorithm can be improved. Simulation results reveal that integrated with the proposed method the fast la-RBPF SLAM performance is enhanced over both the existing RBPF SLAM and the unmodified la-RBPF SLAM algorithms.

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Pineda Torres, Franklin, and Luis Alejandro Arias Barragán. "PRM navigation in trading drone and Gazebo simulation." Visión electrónica 14, no. 1 (January 31, 2020): 43–50. http://dx.doi.org/10.14483/22484728.16494.

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Starting from a commercial drone AR Dron Parrot 2.0, an autonomous navigation process is developed with a PRM probabilistic route planner in real time, through a ROS network between the drone and the Gazebo simulation software. Using the robotics system toolbox from software Matlab that interacts with Gazebo, it is possible to study the desired trajectory planner, in addition, the creation and connection of the ROS network on the Linux operating system, where the navigation algorithm is analyzed from the practical vs., simulation points of views. The errors that are presented are minimal, taking into account the propagation delays and the control algorithm; this is in charge of receiving location information in order to correct and minimized the mean square error.

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Ligot, Antoine, Jonas Kuckling, Darko Bozhinoski, and Mauro Birattari. "Automatic modular design of robot swarms using behavior trees as a control architecture." PeerJ Computer Science 6 (November 9, 2020): e314. http://dx.doi.org/10.7717/peerj-cs.314.

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We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

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Ciftcioglu, Özer, Michael S. Bittermann, and I. Sevil Sariyildiz. "Multiresolutional Fusion of Perceptions Applied to Robot Navigation." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 688–700. http://dx.doi.org/10.20965/jaciii.2007.p0688.

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Visual perception-based autonomously moving virtual agent in virtual reality as a counterpart of an actual robot moving with a given dynamics is investigated. The visual perception is mathematically modelled as a probabilistic process obtaining and interpreting visual information from an environment. The perception obtained in the form of measurements in 2D is used for perceptual robot navigation. By means of this twofold gain is obtained; while the autonomous robot is navigated, it is equipped with some human-like behaviour, thereby dealing with complexity and environmental dynamics. The visual data is processed in a multiresolutional form via wavelet transform and optimally estimated via extended Kalman filtering in each resolution level and the outcomes are fused for improved estimation of the trajectory. The perceptual robotics experiments are carried out in virtual reality for the demonstration of the feasibility of the investigations in this domain. The computer experiments are carried out with perception measurement data, and the sensor/data fusion experiments are carried out by means of simulation. The improvement on the trajectory estimation by means of sensor/data fusion is demonstrated. The research is connected to building technological robotics, where some form of perceptual intelligence, like reaction to moving objects around, is required during operation.

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Journal articles on the topic 'Probabilistic Robotics'

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