On the use of Bayesian Networks to develop behaviours for mobile robots (original) (raw)
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2011
Flexible and adaptive behavior of mobile robots is characterized by context-awareness and the ability to reason using uncertain and imprecise information. A coherent behaviour of mobile robots placed multiple real-time design requirements on the controller, sensors, and actuators, at both hardware and software levels. In the context of use, perceptual-oriented capabilities more dynamically adapt to robot resources are a principle that drives mobile robotics to plans its sensor and effectors actions. The unknown, hidden variables in the mobile robotics can be model by the means of probabilistic inference that take into account incomplete and uncertain information. A sensing plan for mobile robots is enviable based on sensor nodes that consist of sharing small, inexpensive, and robustly inter-networked sensors. The paper investigates the methodology of network for deployment of combined proximity sensors as a localization method for robotic systems in structured and dynamic environmen...
A bayesian conceptualization of space for mobile robots
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007
The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is -conceptualization and classification for representation and spatial cognition.
Topology learning and recognition using Bayesian programming for mobile robot navigation
International Conference on Intelligent RObots and Systems - IROS, 2004
This paper proposes an approach allowing topology learning and recognition in indoor environments by using a probabilistic approach called Bayesian programming. The main goal of this approach is to cope with the uncertainty, imprecision and incompleteness of handled information. The Bayesian program for topology recognition and door detection is presented. The method has been successfully tested in indoor environments with
Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics
2017
This chapter discusses the use of dynamic Bayesian networks (DBNs) for time-dependent classification problems in mobile robotics, where Bayesian inference is used to infer the class, or category of interest, given the observed data and prior knowledge. Formulating the DBN as a time-dependent classification problem, and by making some assumptions, a general expression for a DBN is given in terms of classifier priors and likelihoods through the time steps. Since multi-class problems are addressed, and because of the number of time slices in the model, additive smoothing is used to prevent the values of priors from being close to zero. To demonstrate the effectiveness of DBN in time-dependent classification problems, some experimental results are reported regarding semantic place recognition and daily-activity classification.
Learning concepts from sensor data of a mobile robot
Machine Learning, 1996
Machine learningcan be a most valuable tool for improvingthe exibilityand e ciency of robot applications. Many approachesto applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user de nes in terms of grammars.
Lecture on Bayesian Perception & Decision-making for Autonomous Vehicles and Mobile Robots
2017
New technologies for Autonomous Vehicles and Mobile Robots will be presented, with an emphasis on multi-sensors Embedded Perception, Situation Awareness, Collision Risk Assessment, and Decision-making for safe navigation in Dynamic Human Environments. It will be shown that Bayesian approaches are mandatory for developing these technologies and for obtaining the required robustness in presence of uncertainty and complex dynamic situations. The talk will be illustrated by some interesting results obtained in the scope of several collaborative projects involving either national research and development institutes such as CEA-LETI and IRT (Technological Research Institute) Nanoelec, or international industrial companies such as Toyota or Renault-Nissan.
Bayesian network-based behavior control for skilligent robots
2009 IEEE International Conference on Robotics and Automation, 2009
A Skilligent robot must be able to learn skills autonomously to accomplish a task. "Skilligence" is the capacity of the robot to control behaviors reasonably, based on the skills acquired during run-time. Behavior control based on Bayesian networks is used to control reasonable behaviors. To accomplish this, subgoals are first discovered by clustering similar features of state transition tuples, which are composed of current states, actions, and next states. Here, features used in clustering are produced using changes of the states in the state transition tuples. Parameters of Bayesian networks and utility functions are learned separately using state transition tuples belonging to each subgoal. To select the best action while executing a task, the expected utility of each subgoal is calculated by the expected utility function and the robot chooses the action that maximizes expected utility calculated by the maximum expected utility (MEU) function. The MEU function is based on the conditional probabilistic distributions of Bayesian networks and utility functions. We also propose a method for reconstructing learned networks and increasing subgoals by incremental learning. To show the validities of our proposed methods, a task using Dribbling-Box-Into-a-Goal (DBIG) and Obstacle-Avoidance-While-Dribbling-Box (OAWDB) skills is simulated and experimented.
Bayesian space conceptualization and place classification for semantic maps in mobile robotics
The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. Both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. The approaches are based on learning from exemplars, clustering and the use of Bayesian network classifiers. The approaches are generative. Further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. The presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. Thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). Such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of this work is-conceptualization and classification for representation and spatial cognition.