scholarly journals Optimization-based approach to path planning for closed chain robot systems

2011 ◽  
Vol 21 (4) ◽  
pp. 659-670 ◽  
Author(s):  
Wojciech Szynkiewicz ◽  
Jacek Błaszczyk
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Closed Chain ◽  
Robot Systems ◽  
Equality And Inequality Constraints ◽  
Primal Dual ◽  
The Given

Optimization-based approach to path planning for closed chain robot systems An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.

Optimum Path Planning for Mechanical Manipulators

1981 ◽  
Vol 103 (2) ◽  
pp. 142-151 ◽  
Author(s):  
J. Y. S. Luh ◽  
C. S. Lin
Keyword(s):  
Path Planning ◽  
Feasible Solution ◽  
Computing Time ◽  
Inequality Constraints ◽  
Programming Algorithm ◽  
Planning Problem ◽  
Linear Inequality Constraints ◽  
Nonlinear Inequality ◽  
Angular Velocities ◽  
Nonlinear Inequality Constraints

To assure a successful completion of an assigned task without interruption, such as the collision with fixtures, the hand of a mechanical manipulator often travels along a preplanned path. An advantage of requiring the path to be composed of straight-line segments in Cartesian coordinates is to provide a capability for controlled interaction with objects on a moving conveyor. This paper presents a method of obtaining a time schedule of velocities and accelerations along the path that the manipulator may adopt to obtain a minimum traveling time, under the constraints of composite Cartesian limit on linear and angular velocities and accelerations. Because of the involvement of a linear performance index and a large number of nonlinear inequality constraints, which are generated from physical limitations, the “method of approximate programming (MAP)” is applied. Depending on the initial choice of a feasible solution, the iterated feasible solution, however, does not converge to the optimum feasible point, but is often entrapped at some other point of the boundary of the constraint set. To overcome the obstacle, MAP is modified so that the feasible solution of each of the iterated linear programming problems is shifted to the boundaries corresponding to the original, linear inequality constraints. To reduce the computing time, a “direct approximate programming algorithm (DAPA)” is developed, implemented and shown to converge to optimum feasible solution for the path planning problem. Programs in FORTRAN language have been written for both the modified MAP and DAPA, and are illustrated by a numerical example for the purpose of comparison.

A Study of Airline Fuel Planning Optimization Using R-Ga

2020 ◽  
Vol 46 ◽  
pp. 189-197
Author(s):  
Ekene Gabriel Okafor ◽  
Osaretin Kole Uhuegho ◽  
Christopher Manshop ◽  
Paul Olugbeji Jemitola ◽  
Osichinaka Chiedu Ubadike
Keyword(s):  
Regression Analysis ◽  
Fuel Consumption ◽  
Large Time ◽  
Inequality Constraints ◽  
Flight Time ◽  
Planning Problem ◽  
Study Objective ◽  
Planning Optimization ◽  
Equality And Inequality Constraints ◽  
Human Effort

In this study, airline planning optimization problem based on ferry strategy was considered. Cost was the study objective function subject to forty equality and inequality constraints. Regression analysis as well a genetic algorithm (GA) was used to solve the problem. The mathematical relationship between flight fuel consumption and flight time was established using regression analysis, while GA was used for the optimization. The established mathematical model was used to predict the fuel consumption for the twenty scheduled flight consider based on their respective flight time. The result was found to be satisfactory, as optimal fuel lift plan was achieved in approximately twenty seconds of program run time, as against the large time usually spend using human effort to solve the fuel planning problem. The optimized fuel lift plan was compared with the actual fuel lift plan executed by the airline for the twenty scheduled flight considered. The result revealed thirty percent savings using the optimized plan in comparison to the actual fuel lift plan executed by the airline.

scholarly journals Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2461 ◽  
Author(s):  
Jungyun Bae ◽  
Woojin Chung
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Target Location ◽  
Travel Cost ◽  
Unmanned Vehicles ◽  
Traveling Salesman ◽  
Planning Problem ◽  
Travel Costs ◽  
Primal Dual ◽  
Multiple Depot

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times.

Real-Time Optimal Coherent Phantom Track Generation via the Virtual Motion Camouflage Approach

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Yunjun Xu ◽  
Gareth Basset
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Inequality Constraints ◽  
Early Termination ◽  
Planning Problem ◽  
Motion Camouflage ◽  
Equality And Inequality Constraints ◽  
Time Optimal ◽  
And Control ◽  
Air Vehicles

Coherent phantom track generation through controlling a group of electronic combat air vehicles is currently an area of great interest to the defense agency for the purpose of deceiving a radar network. However, generating an optimal or even feasible coherent phantom trajectory in real-time is challenging due to the high dimensionality of the problem and severe geometric, as well as state, control, and control rate constraints. In this paper, the bio-inspired virtual motion camouflage based methodology, augmented with the derived early termination condition, is investigated to solve this constrained collaborative trajectory planning problem in two approaches: centralized (one optimization loop) and decentralized (two optimization loops). Specifically, in the decentralized approach, the first loop finds feasible phantom tracks based on the early termination condition and the equality and inequality constraints of the phantom track. The second loop uses the virtual motion camouflage method to solve for the optimal electronic combat air vehicle trajectories based on the feasible phantom tracks obtained in the first loop. Necessary conditions are proposed for both approaches so that the initial and final velocities of the phantom and electronic combat air vehicles are coherent. It is shown that the decentralized approach can solve the problem much faster than the centralized one, and when the decentralized approach is applied, the computational cost remains roughly the same for the cases when the number of nodes and/or the number of electronic combat air vehicles increases. It is concluded that the virtual motion camouflage based decentralized approach has promising potential for usage in real-time implementation.

Economic Dispatch Problems Using Backtracking Search Optimization

2018 ◽  
Vol 7 (2) ◽  
pp. 39-60
Author(s):  
Kuntal Bhattacharjee
Keyword(s):  
Large Scale ◽  
Transmission Loss ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Backtracking Search ◽  
Search Optimization ◽  
Set Up ◽  
Optimum Solution

The purpose of this article is to present a backtracking search optimization technique (BSA) to determine the feasible optimum solution of the economic load dispatch (ELD) problems involving different realistic equality and inequality constraints, such as power balance, ramp rate limits, and prohibited operating zone constraints. Effects of valve-point loading, multi-fuel option of large-scale thermal plants, system transmission loss are also taken into consideration for more realistic application. Two effective operations, mutation and crossover, help BSA algorithms to find the global solution for different optimization problems. BSA has the capability to deal with multimodal problems due to its powerful exploration and exploitation capability. BSA is free from sensitive parameter control operations. Simulation results set up the proposed approach in a better stage compared to several other existing optimization techniques in terms quality of solution and computational efficiency. Results also reveal the robustness of the proposed methodology.

Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Path Planning ◽  
Data Acquisition ◽  
Large Scale ◽  
Cluster Formation ◽  
Optimal Path ◽  
Research Work ◽  
Planning Problem ◽  
Sensing Data ◽  
Planning Algorithm ◽  
Load Balanced

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

An Advanced Quantum Optimization Algorithm for Robot Path Planning

2019 ◽  
Vol 29 (08) ◽  
pp. 2050122
Author(s):  
Liming Gao ◽  
Rong Liu ◽  
Fei Wang ◽  
Weizong Wu ◽  
Baohua Bai ◽  
...  
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Computationally Efficient ◽  
Planning Algorithm ◽  
Scale Optimization ◽  
Robot Path

In this paper, a new robot path planning algorithm based on Quantum-inspired Evolutionary Algorithm (QEA) is proposed. QEA is an advanced evolutionary computing scheme with the quantum computing features such as qubits and superposition. It is suitable for solving large scale optimization problems. The proposed QEA algorithm works in the discretized environment, and approximates the optimal robot planing path in a highly computationally efficient fashion. The simulation results indicate that the proposed QEA algorithm is suitable for both complex static and dynamic environment and considerably outperforms the conventional genetic algorithm (GA) for solving the robot path planning problem. Our algorithm runs in only about 2[Formula: see text]s, which demonstrates that it can well tackle the optimization problem in robot path planning.

scholarly journals Improving the Scalability of the Magnitude-Based Deceptive Path-Planning Using Subgoal Graphs

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 162
Author(s):  
Kai Xu ◽  
Yue Hu ◽  
Yunxiu Zeng ◽  
Quanjun Yin ◽  
Mei Yang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimal Path ◽  
Computation Time ◽  
General Purpose ◽  
Optimization Techniques ◽  
Hybrid Solution ◽  
Recent Developments ◽  
Road Maps ◽  
Path Planner

Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. Magnitude-based deceptive path-planning takes advantage of the quantified deceptive values upon each grid or position to generate paths that are deceptive. Existing methods using optimization techniques cannot satisfy the time constraints when facing with the large-scale terrain, as its computation time grows exponentially with the size of road maps or networks. In this work, building on recent developments in the optimal path planner, the paper proposes a hybrid solution between map scaling and hierarchical abstractions. By leading the path deception information down into a general purpose but highly-efficient path-planning formulation, the paper substantially speeds up the task upon large scale terrains with an admissible loss of deception.

Swarm Intelligence Optimization Techniques in Mobile Path Planning - A Review

2018 ◽  
Vol 37 ◽  
pp. 62-71 ◽  
Author(s):  
Chika O. Yinka-Banjo ◽  
Ukamaka Hope Agwogie
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Crucial Problem ◽  
Swarm Intelligence Optimization ◽  
Present World

In the present world, mobile robot has been widely used for many functions across different areas of life. These mobile robots can be engaged in a static or dynamic environment where they are expected to accomplish a task optimally against all odds. Path planning for mobile robot is a very crucial problem in robotics that has been greatly researched upon; it is aimed at finding an optimal path in a given environment from a start point to the goal point. Several techniques have been employed in solving this crucial problem. These techniques are broadly classified as classical and heuristics. The Swarm Intelligence Techniques form a sub-class of the heuristics approach. The aim of this research is to review the swarm intelligence techniques in solving the mobile robot path planning problem. The drawbacks and merits of each of the techniques were discussed and a comparative analysis was given.

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