Combining probabilistic map and dialog for robust life-long office navigation

Asoh, Hideki; Motomura, Y.; Hara, Isao; Akaho, Shotaro; Hayamizu, Satoru; Matsui, Toshihiro

doi:10.1109/iros.1996.571056

Cited by 27 publications

(10 citation statements)

References 6 publications

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“…There are some robots that have realized interactions with humans using such a model (e.g., [25]). In this paper, we have discussed the accumulation of the statetransition model based on deterministic input and output.…”

Section: Left Problems and Future Researchmentioning

confidence: 99%

An extensible dialogue script for robot based on unification of state transition models

Matsusaka

Fujii

Hara

2009

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA)

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We propose extension-by-unification method to improve reusability of the dialogue components in the development of communication function of the robot. Compared to previous extension-by-connection method used in behavior-based communication robot developments, the extension-by-unification method has the ability to decompose the script into components. The decomposed components can be recomposed to build a new application easily. In this paper, first we, explain a reformulation we have applied to the conventional state-transition model. Second, we explain a set of algorithms to decompose, recompose, and detect the conflict of each component. Third, we explain a dialogue engine and a script management server we have developed. The script management server has a function to propose reusable components to the developer in real time by implementing the conflict detection algorithm. The dialogue engine SEAT (Speech Event-Action Translator) has flexible adapter mechanism to enable quick integration to robotic systems. We have confirmed that by the application of three robots, development efficiency has improved by 30%.

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Section: Left Problems and Future Researchmentioning

confidence: 99%

An extensible dialogue script for robot based on unification of state transition models

Matsusaka

Fujii

Hara

2009

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA)

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“…The robots DERVISH (Nourbakhsh et al, 1995), developed in the Stanford University, and Xavier (Koenig & Simmons, 1998), in the Carnegie Mellon University, were the first robots successfully using this kind of navigation strategies for localization and action planning. Other successful robots guided with POMDPs are those proposed by (Zanichelli, 1999) or (Asoh et al, 1996). In the nursing applications field, in which robots interact with people and uncertainty is pervasive, robots such as Flo (Roy et al, 2000) or Pearl (Montemerlo et al, 2002) use POMDPs at all levels of decision making, and not only in low-level navigation routines.…”

Section: Related Previous Workmentioning

confidence: 99%

Global Navigation of Assistant Robots using Partially Observable Markov Decision Processes

López¹,

Barea²,

Bergasa³

et al. 2007

Mobile Robots: Perception &Amp; Navigation

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“…In Asoh et al [11], Bayesian networks were applied for the localization problem using local information but no action planning. Zhou and Sakane [12] proposed a general hierarchical approach to solve sensor planning for the global localization.…”

Section: Introductionmentioning

confidence: 99%

Robot Task Planning on Multiple Environments

Pinheiro¹,

Wainer²,

Cardozo³

2014

IJCEE

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This paper presents a hierarchical planning approach to solving the global localization problem for mobile robots using a compaction map method. This approach uses architectural design features such as walls and doors to break the environment into rooms and cluster them by similarity constraints. Each group of similar rooms is mixed into a single, compact representative map. Lighter POMDP plans are generated only for these compact maps and not for the whole environment, decreasing size of the set of possible states. In the higher layer of the planner, the robots use the compact maps to define which group of rooms they are located in. In the lower layer, the specific room of a representative map is isolated and its probability distribution is used to find out the precise localization. We demonstrate the hierarchical approach and the new map compaction with experiments on both V-REP Simulator and a Pioneer 3DX robot. This approach allows the robot to perform both the localization and tasking in a large-scale environment while keeping the accuracy.

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Combining probabilistic map and dialog for robust life-long office navigation

Cited by 27 publications

References 6 publications

An extensible dialogue script for robot based on unification of state transition models

An extensible dialogue script for robot based on unification of state transition models

Global Navigation of Assistant Robots using Partially Observable Markov Decision Processes

Robot Task Planning on Multiple Environments

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