Real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive Kalman filter

2007 ◽  
Vol 55 (2) ◽  
pp. 96-106 ◽  
Author(s):  
Homayoun Najjaran ◽  
Andrew Goldenberg
Keyword(s):  
Kalman Filter ◽  
Motion Planning ◽  
Real Time ◽  
Mobile Manipulator ◽  
Adaptive Kalman Filter ◽  
Time Motion ◽  
Fuzzy Adaptive

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Audelia G. Dharmawan ◽  
Shaohui Foong ◽  
Gim Song Soh
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Computational Time ◽  
Mobile Manipulator ◽  
Optimal Motion ◽  
Task Constraints ◽  
Moving Obstacles ◽  
Time Motion ◽  
Optimal Motion Planning

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

A real-time motion planning scheme for collaborative robots using HRI-based cost function

2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Khawaja Fahad Iqbal ◽  
Akira Kanazawa ◽  
Silvia Romana Ottaviani ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge
Keyword(s):  
Motion Planning ◽  
Cost Function ◽  
Real Time ◽  
Collaborative Robots ◽  
Time Motion ◽  
Planning Scheme
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