State Space Partitioning and Clustering with Sensor Alignment for Autonomous Robots

2006 ◽  
Author(s):  
T. Hamagami ◽  
H. Hirata
Keyword(s):  
State Space ◽  
Autonomous Robots ◽  
Space Partitioning

scholarly journals Verification of visibility-based properties on multiple moving robots in an environment with obstacles

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878665 ◽  
Author(s):  
Ali Narenji Sheshkalani ◽  
Ramtin Khosravi
Keyword(s):  
State Space ◽  
Autonomous Robots ◽  
The State ◽  
Robot System ◽  
Discrete State ◽  
Verification Method ◽  
Distributed Processes ◽  
Analytical Proof ◽  
Motion Strategy ◽  
Multi Robot

A multi-robot system consists of a number of autonomous robots moving within an environment to achieve a common goal. Each robot decides to move based on information obtained from various sensors and gathered data received through communicating with other robots. In order to prove the system satisfies certain properties, one can provide an analytical proof or use a verification method. This article presents a new notion to prove visibility-related properties of a multi-robot system by introducing an automated verification method. Precisely, we propose a method to automatically generate a discrete state space of a given multi-robot system and verify the correctness of the desired properties by means of model-checking tools and algorithms. We construct the state space of a number of robots, each moves freely inside a bounded polygonal area with obstacles. The generated state space is then used to verify visibility properties (e.g. if the communication graph of robots is connected) by means of the construction and analysis of distributed processes model checker. Using our method, there is no need to analytically prove that the properties are preserved with every change in the motion strategy of the robots. We have implemented a tool to automatically generate the state space and verified some properties to demonstrate the applicability of our method in various environments.

Hierarchical Neuro-Fuzzy Systems Part II

2011 ◽  
pp. 817-824
Author(s):  
Marley Vellasco ◽  
Marco Pacheco ◽  
Karla Figueiredo ◽  
Flavio Souza
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Learning Process ◽  
Fuzzy Systems ◽  
Space Partitioning ◽  
Learning Methods ◽  
New Class ◽  
Neuro Fuzzy ◽  
Binary Space Partitioning ◽  
Priori Information

This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical Neuro- Fuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure and rules (ANFIS [Jang,1997], NEFCLASS [Kruse,1995] and FSOM [Vuorimaa,1994]). These new models, named Reinforcement Learning Hierarchical Neuro-Fuzzy BSP (RL-HNFB) and Reinforcement Learning Hierarchical Neuro-Fuzzy Politree (RL-HNFP), descend from the original HNFB that uses Binary Space Partitioning (see Hierarchical Neuro-Fuzzy Systems Part I). By using hierarchical partitioning, together with the Reinforcement Learning (RL) methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent, dismissing a priori information (number of rules, fuzzy rules and sets) relative to the learning process. These characteristics represent an important differential when compared with existing intelligent agents learning systems, because in applications involving continuous environments and/or environments considered to be highly dimensional, the use of traditional Reinforcement Learning methods based on lookup tables (a table that stores value functions for a small or discrete state space) is no longer possible, since the state space becomes too large. This second part of hierarchical neuro-fuzzy systems focus on the use of reinforcement learning process. The first part presented HNFB models based on supervised learning methods. The RL-HNFB and RL-HNFP models were evaluated in a benchmark control application and a simulated Khepera robot environment with multiple obstacles.

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