Optimal Motion Planning for the Teaching Experimental Mobile Manipulator

An optimal motion planning based on minimum principle is presented to address the motion problem of the mobile manipulator in a sort of experimental system. In view of the characteristic of the practical experimental apparatus, the model of the manipulator is deduced based on the kinetic analysis and mathematic method. An optimal control scheme is then investigated to deal with the optimization problem of the motion planning for the manipulator, so as to guarantee the demand of the teaching experiment. Simulation verifies the control performance of the optimal control scheme for the optimal motion planning of the manipulator, and it helps improve the teaching experiment effect.

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

Journal of Mechanisms and Robotics ◽

10.1115/1.4039395 ◽

2018 ◽

Vol 10 (3) ◽

Cited By ~ 1

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.

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A bilevel optimal motion planning (BOMP) model with application to autonomous parking

International Journal of Intelligent Robotics and Applications ◽

10.1007/s41315-019-00109-z ◽

2019 ◽

Vol 3 (4) ◽

pp. 370-382 ◽

Cited By ~ 1

Author(s):

Shenglei Shi ◽

Youlun Xiong ◽

Jiankui Chen ◽

Caihua Xiong

Keyword(s):

Optimal Control ◽

Optimal Control Problem ◽

Motion Planning ◽

Control Problem ◽

Control Method ◽

Significant Feature ◽

Optimal Motion ◽

Optimal Control Method ◽

Optimal Motion Planning ◽

Upper Level

Abstract In this paper, we present a bilevel optimal motion planning (BOMP) model for autonomous parking. The BOMP model treats motion planning as an optimal control problem, in which the upper level is designed for vehicle nonlinear dynamics, and the lower level is for geometry collision-free constraints. The significant feature of the BOMP model is that the lower level is a linear programming problem that serves as a constraint for the upper-level problem. That is, an optimal control problem contains an embedded optimization problem as constraints. Traditional optimal control methods cannot solve the BOMP problem directly. Therefore, the modified approximate Karush–Kuhn–Tucker theory is applied to generate a general nonlinear optimal control problem. Then the pseudospectral optimal control method solves the converted problem. Particularly, the lower level is the $$J_2$$J2-function that acts as a distance function between convex polyhedron objects. Polyhedrons can approximate objects in higher precision than spheres or ellipsoids. As a result, a fast high-precision BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed. Simulation results and experiment on Turtlebot3 validate the BOMP model, and demonstrate that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method.

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Optimal Nonholonomic Motion Planning of Space Robot System With Dual-Arms

Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C ◽

10.1115/detc2007-34707 ◽

2007 ◽

Author(s):

Li Chen ◽

Xiaoteng Tang

Keyword(s):

Optimal Control ◽

Motion Planning ◽

Attitude Control ◽

Space Robot ◽

State Equations ◽

Robot System ◽

Optimal Motion Planning ◽

Optimal Control Scheme ◽

Radial Basis Function Approximation ◽

Nonholonomic Motion Planning

In this paper, the optimal nonholonomic motion planning of free-floating space robot system with dual-arms is discussed. Base on the linear and angular momentum conservations of the system, the system state equations for control design are established, so the nonholonomic motion planning objective of attitude control of space robot system is translated as the solution of a canonical nonlinear control problem. An optimal control scheme of the system proposed is studied, using radial basis function approximation; a numerical algorithm for computing approximate optimal control of the system proposed is developed. The optimal motion planning approach proposed above possesses the advantages that it can obtain the desired angles of the base’s attitude and arms’ joints only by controlling the arms’ joints motion. A planar free-floating space robot system with dual-arms is simulated to verify the proposed approach.

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SQP-Based Mobile Manipulator Motion Planning With Controlled Infeasibility for Physically Valid Task Failure

Volume 6A: 37th Mechanisms and Robotics Conference ◽

10.1115/detc2013-13377 ◽

2013 ◽

Author(s):

Chang B. Joo ◽

Joo H. Kim

Keyword(s):

Quadratic Programming ◽

Motion Planning ◽

Sequential Quadratic Programming ◽

Merit Function ◽

Mobile Manipulator ◽

Nonlinear Functions ◽

Unified Approach ◽

Optimal Motion ◽

Sqp Algorithm ◽

Optimal Motion Planning

Since anticipating or recovering infeasibility in optimal motion planning is not always possible, infeasibilities occur frequently and are not completely avoidable. We introduce an enhanced sequential quadratic programming (SQP) based framework of controlled infeasibility for physically valid solutions, based on our previous study. A priority weight function is incorporated into an SQP algorithm combined with constraints and objective function normalization to ensure strict satisfaction of high-priority constraints. These are embedded in the SQP algorithm through its merit function and composite cost function, in which general nonlinear functions can be incorporated in a unified approach. Several simple mobile manipulator examples demonstrate the advantages of the proposed method.

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Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

Sensors ◽

10.3390/s21155011 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5011

Author(s):

Juan Parras ◽

Patricia A. Apellániz ◽

Santiago Zazo

Keyword(s):

Optimal Control ◽

Deep Learning ◽

Motion Planning ◽

Optimal Control Problems ◽

Autonomous Vehicle ◽

Control Problems ◽

Planning Problem ◽

Optimal Motion ◽

Optimal Motion Planning ◽

Hamilton Jacobi Bellman

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.

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