Goal-directed affordance prediction at the subtask level

2016 ◽  
Vol 43 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Huaqing Min ◽  
Chang'an Yi ◽  
Ronghua Luo ◽  
Jinhui Zhu
Keyword(s):  
Mobile Robot ◽  
Hybrid Control ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Control Architecture ◽  
Autonomous Mobile Robot ◽  
Reactive Control ◽  
Navigation Task ◽  
Content Type ◽  
Hybrid Control Architecture

Purpose – This paper aims to present a hybrid control approach that combines learning-based reactive control and affordance-based deliberate control for autonomous mobile robot navigation. Unlike many current navigation approaches which only use learning-based paradigms, the authors focus on how to utilize the machine learning methods for reactive control together with the affordance knowledge that is simultaneously inherent in natural environments to gain advantages from both local and global optimization. Design/methodology/approach – The idea is to decompose the complex and large-scale robot navigation task into multiple sub-tasks and use the hierarchical reinforcement learning (HRL) algorithm, which is well-studied in the learning and control algorithm domains, to decompose the overall task into sub-tasks and learn a grid-topological map of the environment. An affordance-based deliberate controller is used to inspect the affordance knowledge of the obstacles in the environment. The hybrid control architecture is then designed to integrate the learning-based reactive control and affordance-based deliberate control based on the grid-topological and affordance knowledge. Findings – Experiments with computer simulation and an actual humanoid NAO robot have demonstrated the effectiveness of the proposed hybrid approach for mobile robot navigation. Originality/value – The main contributions of this paper are a new robot navigation framework that decomposes a complex navigation task into multiple sub-tasks using the HRL approach, and hybrid control architecture development that integrates learning-based and affordance-based paradigms for autonomous mobile robot navigation.

DEVELOPMENT OF A CONTROL STRATEGY FOR MOBILE ROBOT NAVIGATION IN UNKNOWN TERRAIN

2019 ◽  
Author(s):  
Diego Gabriel Gomes Rosa ◽  
Carlos Luiz Machado de souza junior ◽  
Marco Antonio Meggiolaro ◽  
Luiz Fernando Martha
Keyword(s):  
Mobile Robot ◽  
Control Strategy ◽  
Robot Navigation ◽  
Mobile Robot Navigation

Real-time road edge following for mobile robot navigation

1990 ◽  
Vol 2 (1) ◽  
pp. 35 ◽  
Author(s):  
R.A. Lotufo ◽  
A.D. Morgan ◽  
E.L. Dagless ◽  
D.J. Milford ◽  
J.F. Morrissey ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Robot Navigation ◽  
Mobile Robot Navigation

scholarly journals Path Planning and Replanning for Mobile Robot Navigation on 3D Terrain: An Approach Based on Geodesic

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Kun-Lin Wu ◽  
Ting-Jui Ho ◽  
Sean A. Huang ◽  
Kuo-Hui Lin ◽  
Yueh-Chen Lin ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Tangent Plane ◽  
Intersection Point ◽  
Mobile Robot Navigation ◽  
Complete Manifold ◽  
Geodesic Triangle ◽  
Planning Stage ◽  
3D Terrain ◽  
Bonnet Theorem

In this paper, mobile robot navigation on a 3D terrain with a single obstacle is addressed. The terrain is modelled as a smooth, complete manifold with well-defined tangent planes and the hazardous region is modelled as an enclosing circle with a hazard grade tuned radius representing the obstacle projected onto the terrain to allow efficient path-obstacle intersection checking. To resolve the intersections along the initial geodesic, by resorting to the geodesic ideas from differential geometry on surfaces and manifolds, we present a geodesic-based planning and replanning algorithm as a new method for obstacle avoidance on a 3D terrain without using boundary following on the obstacle surface. The replanning algorithm generates two new paths, each a composition of two geodesics, connected via critical points whose locations are found to be heavily relying on the exploration of the terrain via directional scanning on the tangent plane at the first intersection point of the initial geodesic with the circle. An advantage of this geodesic path replanning procedure is that traversability of terrain on which the detour path traverses could be explored based on the local Gauss-Bonnet Theorem of the geodesic triangle at the planning stage. A simulation demonstrates the practicality of the analytical geodesic replanning procedure for navigating a constant speed point robot on a 3D hill-like terrain.

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