MPC-Graph: Feedback motion planning using sparse sampling based neighborhood graph

Date

2020-10-24

Author

Karagoz, O. Kaan
Atasoy, Simay
Ankaralı, Mustafa Mert

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

274
views

0
downloads

© 2020 IEEE.Robust and safe feedback motion planning and navigation is a critical task for autonomous mobile robotic systems considering the highly dynamic and uncertain nature scenarios of modern applications. For these reasons motion planning and navigation algorithms that have deep roots in feedback control theory has been at the center stage of this domain recently. However, the vast majority of such policies still rely on the idea that a motion planner first generates a set of open-loop possibly time-dependent trajectories, and then a set of feedback control policies track these trajectories in closed-loop while providing some error bounds and guarantees around these trajectories. In contrast to trajectory-based approaches, some researchers developed feedback motion planning strategies based on connected obstacle-free regions, where the task of the local control policies is to drive the robot(s) in between these particular connected regions. In this paper, we propose a feedback motion planning algorithm based on sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). The algorithm first generates a sparse neighborhood graph as a set of connected simple rectangular regions. After that, during navigation, an MPC based online feedback control policy funnels the robot with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs with guaranteed stability. In this framework, we can drive the robot to any goal location provided that the connected region network covers both the initial condition and the goal position. We demonstrate the effectiveness and validity of the algorithm on simulation studies.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102398755&origin=inward
https://hdl.handle.net/11511/89399

DOI

https://doi.org/10.1109/iros45743.2020.9341225

Conference Name

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

MPC-Graph: Nonlinear feedback motion planning using sparse sampling based neighborhood graph Atasoy, Simay; Ankaralı, Mustafa Mert; Department of Electrical and Electronics Engineering (2022-1) Robust and safe feedback motion planning and navigation is a critical task for autonomous mobile robotic systems considering the highly dynamic and uncertain nature scenarios of modern applications. For these reasons motion planning and navigation algorithms that have deep roots in feedback control theory has been at the center stage of this domain recently. However, the vast majority of such policies still rely on the idea that a motion planner first generates a set of open-loop possibly time-dependent tra...
Feedback motion planning of unmanned surface vehicles via random sequential composition Ege, Emre; Ankaralı, Mustafa Mert (SAGE Publications, 2019-08-01) In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In ...
Smart Switchable Beam Antennas for Internet of Things Applications Aragbaiye, Yewande Mariam; Nesimoğlu, Tayfun; Electrical and Electronics Engineering (2021-9) The main objective of this thesis is to design, fabricate and test novel and compact switchable and steerable beam smart antennas working at 2.4 GHz frequency that will be integrated into 6TiSCH networks for IoT applications. In this thesis, two switchable beam antenna designs are presented. The first antenna design which is a dual-port multi-layered switchable beam antenna can steer and switch its main beam both in the azimuth and the elevation plane in four directions. A parasitic Yagi-Uda layer made up o...
Simultaneous localization and mapping for a mobile robot operating in outdoor environments Sezginalp, Emre; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2007) In this thesis, a method to the solution of autonomous navigation problem of a robot working in an outdoor application is sought. The robot will operate in unknown terrain where there is no a priori map present, and the robot must localize itself while simultaneously mapping the environment. This is known as Simultaneous Localization and Mapping (SLAM) problem in the literature. The SLAM problem is attempted to be solved by using the correlation between range data acquired at different poses of the robot. A...
Decision and feature fusion over the fractal inference network using camera and range sensors Erkmen, İsmet; Erkmen, Aydan Müşerref; Ucar, E (1998-11-03) The objective of the ongoing work is to fuse information from uncertain environmental data taken by cameras, short range sensors including infrared and ultrasound sensors for strategic target recognition and task specific action in Mobile Robot applications. Our present goal in this paper is to demonstrate target recognition for service robot in a simple office environment. It is proposed to fuse all sensory signals obtained from multiple sensors over a fully layer-connected sensor network system that provi...

Citation Formats

O. K. Karagoz, S. Atasoy, and M. M. Ankaralı, “MPC-Graph: Feedback motion planning using sparse sampling based neighborhood graph,” presented at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Nevada, Amerika Birleşik Devletleri, 2020, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102398755&origin=inward.