Evolutionary Collision Free Optimal Trajectory Planning for Mobile Robots

1999 ◽  
Vol 11 (2) ◽  
pp. 153-164 ◽  
Author(s):  
M.M.A. Hashem ◽  
◽  
Keigo Watanabe ◽  
Kiyotaka Izumi ◽  
◽  
...  
Keyword(s):  
Mobile Robots ◽  
Evolutionary Algorithm ◽  
Trajectory Planning ◽  
Fitness Function ◽  
Coordinate Frame ◽  
Planning Problem ◽  
Evolutionary Trajectory ◽  
Optimum Trajectory ◽  
Insertion And Deletion ◽  
Sensor Modeling

We present an evolutionary trajectory planning method for mobile robots following a novel evolution strategy (NES) algorithm. The 2-D trajectory planning problem of a mobile robot among polygonal obstacles is formulated as a constrained time-optimum control problem considering motion. Unlike traditional evolutionary representation, special representation of individuals and crossover are used for the evolutionary search. Swapping crossover, insertion, and deletion mutations are used as background operators for maximum evolutionary algorithm flexibility. Polygonal obstacles in the world coordinate frame are modeled as circles from visibility and sensor modeling concepts. An appropriate cost (fitness) function is constructed as the only link between the evolutionary algorithm and environment. Our proposed evolution is effective for collision-free optimum trajectory planning in robot simulation within a heavily constrained (obstacle) environment.

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Using Strength Pareto Evolutionary Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 1141-1146
Author(s):  
Jie Yan ◽  
Dao Xiong Gong
Keyword(s):  
Evolutionary Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Speed ◽  
Mathematic Model ◽  
Stair Climbing ◽  
Planning Problem ◽  
Hexapod Robot ◽  
Strength Pareto Evolutionary Algorithm ◽  
Optimal Trajectory Planning

The Strength Pareto Evolutionary Algorithm (SPEA) is adopted to find time-jerk synthetic optimal trajectory of a hexapod robot in the joint space. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of via points to assure overall continuity of velocity and acceleration. Taken both the execution time and minimax approach of jerk as objectives, and expressed the kinematics constraints as upper bounds on the absolute values of velocity and acceleration, the mathematic model of time-jerk synthetic optimal trajectory planning is built. Finally, SPEA is adopted to optimize the stair-climbing trajectory of a hexapod robot, the simulation results show that this method can solve the trajectory planning problem effectively, and the stair-climbing trajectory can meet the contradictory objective functions of high speed and low robot vibration well.

scholarly journals Evolutionary sets of safe ship trajectories with speed reduction manoeuvres within traffic separation schemes

2013 ◽  
Vol 21 (1) ◽  
pp. 20-27
Author(s):  
Rafał Szłapczyński
Keyword(s):  
Collision Avoidance ◽  
Trajectory Planning ◽  
Fitness Function ◽  
Effective Means ◽  
Planning Method ◽  
Evolutionary Trajectory ◽  
Speed Reduction

ABSTRACT In the previous paper the author presented the evolutionary ship trajectory planning method designed to support Traffic Separation Schemes (TSS). This time the extensions of this method are described which allow to combine evolutionary trajectory planning with speed reduction manoeuvres. On TSS regions with higher than usual density of traffic and smaller distances between ships, the course alterations alone are not always sufficient or effective means of collision avoidance. Therefore they must be supplemented by speed reduction manoeuvres to a larger extent than on open waters. The paper includes a brief description of the optimisation problem, descriptions of the new elements of the method (fitness function, algorithms and the evolutionary cycle) and the examples of how the extended method successfully solves the problems unsolvable without applying speed reduction.

A novel path planning method of mobile robots based on an improved bat algorithm

2020 ◽  
pp. 095440622096314
Author(s):  
Zhongbin Wang ◽  
Ziqing Wu ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Cauchy Distribution ◽  
Planning Problem ◽  
Lévy Flight ◽  
Levy Flight ◽  
Global Path Planning ◽  
Search Stage

In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.

Comparison of Single and Multi-Population Evolutionary Algorithm for Path Planning in Navigation Situation

2013 ◽  
Vol 210 ◽  
pp. 166-177 ◽  
Author(s):  
Łukasz Kuczkowski ◽  
Roman Śmierzchalski
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Collision Avoidance ◽  
Fitness Function ◽  
Graphical Form ◽  
Planning Problem ◽  
Avoidance Situation ◽  
Time Simulation ◽  
The Individual ◽  
Path Planning Problem

In this paper a comparison of single and multi-population evolutionary algorithm is presented. Tested algorithms are used to determine close to optimal ship paths in collision avoidance situation. For this purpose a path planning problem is defined. A specific structure of the individual path and fitness function is presented. Principle of operation of single-population and multi-population evolutionary algorithm is described. Using presented algorithms the simulations on three close to real sea environments were performed. Regardless of the test situation constant time simulation was maintained. Obtained results are presented in graphical form (sequences of successive stages of the simulation) and in form of table in which the values of fitness function for best individual in each simulation were compared. Undertaken research allow to select evolutionary algorithm that, assuming constant simulation time, will determine a better path in close to real collision avoidance situation at sea.

scholarly journals A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
Wanjin Guo ◽  
Ruifeng Li ◽  
Chuqing Cao ◽  
Xunwei Tong ◽  
Yunfeng Gao
Keyword(s):  
Trajectory Planning ◽  
Dynamic Load ◽  
Carrying Capacity ◽  
Robot Manipulator ◽  
Time Interval ◽  
Planning Problem ◽  
Load Carrying Capacity ◽  
Objective Functions ◽  
Decision Variables ◽  
Load Carrying

A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC) is presented for a 5-degree of freedom (DOF) hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions) subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II). A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

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