scholarly journals Wheeled Mobile Robot Trajectory Planning Using Evolutionary Techniques

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
S. Ramabalan ◽  
◽  
V. Sathiya ◽  
M. Chinnadurai ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Trajectory Planning ◽  
Objective Functions ◽  
Nsga Ii ◽  
Differential Drive ◽  
Multi Objective ◽  
Moving Obstacles ◽  
Robot Trajectory ◽  
Planning Optimization ◽  
Simulation Results

This paper proposes two multi-objective trajectory planning optimization algorithms namely Multi-Objective Differential Evolution (MODE) and Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II). They are applied for a differential drive wheels mobile robot (WMR). A cubic NURBS curve is used to constitute the mobile robot’s path. The objective functions considered are travel time, traveled length, and actuators' efforts. All objective functions are to be minimized. The constraints considered are the mobile robot’s kinematic limits, obstacle avoidance, and dynamic limits. Two Stationary and five moving obstacles are present around the robot. Experimental and numerical simulation results are examined and compared.

Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Robotica ◽  
2019 ◽  
Vol 37 (08) ◽  
pp. 1363-1382 ◽  
Author(s):  
V. Sathiya ◽  
M. Chinnadurai
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Trajectory Planning ◽  
Research Study ◽  
Research Problem ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Multi Objective ◽  
Robot Trajectory ◽  
New Variant

SummaryIn this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

Mechatronic Design of a Mobile Robot for Personal Assistance

2021 ◽  
Author(s):  
Luigi Tagliavini ◽  
Andrea Botta ◽  
Luca Carbonari ◽  
Giuseppe Quaglia ◽  
Dario Gandini ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Home Environment ◽  
State Of The Art ◽  
Assistive Robotics ◽  
Personal Assistance ◽  
Differential Drive ◽  
Electrical Systems ◽  
Moving Obstacles ◽  
Design Paradigm ◽  
Axisymmetric Shape

Abstract In this paper, a novel mobile platform for assistive robotics tasks is presented. The machine is designed for working in a home environment, un-structured and possibly occupied by people. To work in this space, the platform must be able to get rid of all the consequent difficulties: to overpass small objects as steps and carpets, to operate with an as-high-as-possible dynamics, to avoid moving obstacles, and to navigate autonomously to track persons for person monitoring purposes. The proposed platform is designed to have an omni-directional mobility that improves the manoeuvrability with respect to state-of-the-art differential drive robots. It also will have a non-axisymmetric shape to easily navigate narrow spaces, and real-time edge computing algorithms for navigation. This work shows the design paradigm adopted for the realization of a novel mobile robot, named Paquitop. For a robust output, the design process used a modular approach which disjointed the several sub-systems which compose the machine. After a brief analysis of the expected features, a set of basic requirements are drawn to guide the functional and executive design. The overall architecture of the platform is presented, together with some details on the mechanical and electrical systems.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Simulation of a Differential-Drive Wheeled Mobile Lego Robot Mindstorms NXT

2015 ◽  
Vol 776 ◽  
pp. 319-324
Author(s):  
I. Wayan Widhiada ◽  
C.G. Indra Partha ◽  
Yuda A.P. Wayan Reza
Keyword(s):  
Mobile Robot ◽  
3D Model ◽  
Hardware Implementation ◽  
Movement Control ◽  
Common Environment ◽  
Matlab Simulink ◽  
Differential Drive ◽  
Simulation Results ◽  
Mechanical Elements ◽  
Drive Model

The aim of this paper is to model and simulate kinematics motion using the differential drive model of a mobile Lego robot Mindstorm NXT. The author’s use integrated two software as a method to solve the simulation of mobile lego robot mindstorms NXT using Matlab/Simulink and Solidworks software. These softwares are enable easier 3D model creation for both simulation and hardware implementation. A fundamental of this work is the use of Matlab/Simulink Toolboxes to support the simulation and understanding of the various kinematics systems and in particular how the SimMechanics toolbox is used to interface seamlessly with ordinary Simulink block diagrams to enable the mechanical elements and its associated control system elements to be investigated in one common environment. The result of simulation shows the mobile robot movement control based on decentralized point algorithm to follow the precision x and y references that has been specified. The design of the mobile robot is validated in simulation results as proof that this design can achieve the good performance.

An algorithm for smooth trajectory planning optimization of isotropic translational parallel manipulators

2015 ◽  
Vol 230 (12) ◽  
pp. 1987-2002 ◽  
Author(s):  
Mahmood Reza Azizi ◽  
Rahmatolah Khani
Keyword(s):  
Trajectory Planning ◽  
Parallel Manipulators ◽  
Geometric Constraints ◽  
Nsga Ii ◽  
End Effector ◽  
B Splines ◽  
Planning Optimization ◽  
Need To Evaluate ◽  
Significant Performance ◽  
Fifth Order

This paper presents a new algorithm for smooth trajectory planning optimization of isotropic translational parallel manipulators (ITPM) that their Jacobian matrices are constant and diagonal over the whole robot workspace. The basic motivation of this work is to formulate the robot kinematic and geometric constraints in terms of optimization variables to reduce the mathematical complexity and running time of the resulting algorithm which are important issues in trajectory planning optimization. To achieve this aim, the end-effector trajectory of ITPMs in Cartesian space is defined using fifth-order B-Splines, and as a main contribution, all of the actuators limitations and robot constraints are formulated in terms of B-Spline parameters with no need of any information about the workspace geometry. Then the total required energy, total time of motion, and maximum absolute value of actuators’ jerk are defined as objective functions and non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve the resulting nonlinear constrained multi-objective optimization problem. Finally, the proposed algorithm is implemented in MATLAB software for Cartesian parallel manipulator (CPM) as a case study, and the results are demonstrated and discussed. The obtained results show the significant performance of the proposed algorithm with no need to evaluate the robot’s constraints and boundaries of its workspace in each point of the end-effector trajectory.

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