Mobile robot navigation based on expected state value under uncertainty of self-localization

Ueda, Ryuichi; Arai, Tamio; Asanuma, Kazunori; Kamiya, Shogo; Kikuchi, T.; Umeda, Kazunori

doi:10.1109/iros.2003.1250674

Cited by 6 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [20], it is reported that Q MDP value method can make a goalkeeper robot for RoboCup wait for additional information on self-localization when the robot does not have enough information to enter its goal without collision. The deadlocks by Q MDP value method in Section 4.2 correspond to this waiting behavior.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Generation of compensation behavior of autonomous robot for uncertainty of information with probabilistic flow control

Ueda

2015

Advanced Robotics

View full text Add to dashboard Cite

We exhibit that a robot shows strategic behaviors for overcoming uncertainty of information in navigation tasks by a modification of the core formula in Q MDP value method. The new formula biases the calculation so as to give a priority, the decision-making to an important part of the state space. Q MDP value method with the modified formula is named as probabilistic flow control (PFC) method. We apply PFC method to a simulated simple task of a mobile robot. In the task, the robot with PFC method shows tricky behaviors that compensate the uncertainty of state information.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Each particle is initially placed at (x, y, θ) that is chosen by the following procedure: 0.1r, 0.1r ), and ω ∼ [0, 360). (20) These distributions in Figure 9 are obtained by r = 200.…”

Section: Evaluation Without Landmarkmentioning

confidence: 99%

Generation of compensation behavior of autonomous robot for uncertainty of information with probabilistic flow control

Ueda

2015

Advanced Robotics

View full text Add to dashboard Cite

show abstract

“…In this paper, however, we use the old one so as not to lead to poor continuity with our past works [8], [9], [10], [11]. Though a computation result is evaluated only in simulation in this paper, results of dynamic programming have been applied to actual robots in the past works.…”

Section: A Robocup Four Legged Robot Leaguementioning

confidence: 99%

Dynamic Programming for Creating Cooperative Behavior of Two Soccer Robots - Part 1: Computation of State-Action Map

Ueda

Sakamoto

Takeshita

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

To solve decision making problems of multi-agent systems, researchers have devised complicated methods, which are expected to solve curse of dimensionality. In this paper, we go to the opposite extreme. We generate cooperative behavior of two soccer robots with a simple dynamic programming (DP), which was proposed in '50s. Through the example, the ability of DP on a recent computer is measured and evaluated both qualitatively and quantitatively. We then show that the simple structure of DP is useful in obtaining behavior of robots in a convincing way. In the implementation of DP, space that is spanned by eight variables for decision making is divided into 610 million states. DP solves the optimal actions of two robots in every division and creates a look-up table, which is called a state-action map. The ability of this state-action map is measured by simulation and the result is discussed.

show abstract

Localization for Legged Robot with Single Low-Resolution Camera using Genetic Algorithm

Tong

Meng

2007

2007 IEEE International Conference on Integration Technology

View full text Add to dashboard Cite

Mobile robot navigation based on expected state value under uncertainty of self-localization

Cited by 6 publications

References 7 publications

Generation of compensation behavior of autonomous robot for uncertainty of information with probabilistic flow control

Generation of compensation behavior of autonomous robot for uncertainty of information with probabilistic flow control

Dynamic Programming for Creating Cooperative Behavior of Two Soccer Robots - Part 1: Computation of State-Action Map

Localization for Legged Robot with Single Low-Resolution Camera using Genetic Algorithm

Contact Info

Product

Resources

About