Investigating the Use of Bayesian Network and k-NN Models to Develop Behaviours for Autonomous Robots

2016 ◽  
pp. 751-764
Author(s):  
Isaac O. Osunmakinde ◽  
Chika O. Yinka-Banjo ◽  
Antoine Bagula
Keyword(s):  
Bayesian Network ◽  
Autonomous Robots

LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
Author(s):  
PAUL A. BOXER
Keyword(s):  
Bayesian Network ◽  
Probabilistic Reasoning ◽  
Autonomous Robots ◽  
Visual Observation ◽  
Spatial Representations ◽  
Physical Behavior ◽  
Naive Physics ◽  
Action At A Distance ◽  
General Physical ◽  
Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

Collision Avoidance in Unstructured Environments for Autonomous Robots: A Behavioural Modelling Approach

2011 ◽  
Vol 403-408 ◽  
pp. 3559-3569
Author(s):  
Chika O. Yinka-Banjo ◽  
Isaac O. Osunmakinde ◽  
Antoine Bagula
Keyword(s):  
Bayesian Network ◽  
Collision Avoidance ◽  
Autonomous Robots ◽  
Reasoning Ability ◽  
Degrees Of Belief ◽  
Bayesian Network Model ◽  
Degree Of Belief ◽  
Unstructured Environment ◽  
Good Potential ◽  
Modelling Approach

Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.

Denoising of Images using wavelet Bayesian Network

2017 ◽  
Author(s):  
Prof. Anil Bavaskar ◽  
Sangita Kulkarni
Keyword(s):  
Bayesian Network

Bayesian network ensemble learning with inadequate information

2014 ◽  
Author(s):  
Ruijie Du ◽  
Shuangcheng Wang ◽  
Cuiping Leng ◽  
Yunbin Fu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
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