Performance metrics and evaluation of a path planner based on genetic algorithms

2007 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithms ◽  
Performance Metrics ◽  
Path Planner

scholarly journals A Comparison of the Effectiveness of the RRT, PRM, and Novel Hybrid RRT-PRM Path Planners

2021 ◽  
Vol 9 (12) ◽  
pp. 600-611
Author(s):  
Jacqueline Jermyn
Keyword(s):  
Path Length ◽  
Performance Metrics ◽  
Search Tree ◽  
Random Tree ◽  
The Novel ◽  
Search Area ◽  
Probabilistic Roadmap ◽  
Path Planner ◽  
Number Of Iterations ◽  
Path Number

Abstract: Sampling-based path planners develop paths for robots to journey to their destinations. The two main types of sampling-based techniques are the probabilistic roadmap (PRM) and the Rapidly Exploring Random Tree (RRT). PRMs are multi-query methods that construct roadmaps to find routes, while RRTs are single-query techniques that grow search trees to find paths. This investigation evaluated the effectiveness of the PRM, the RRT, and the novel Hybrid RRT-PRM methods. This novel path planner was developed to improve the performance of the RRT and PRM techniques. It is a fusion of the RRT and PRM methods, and its goal is to reduce the path length. Experiments were conducted to evaluate the effectiveness of these path planners. The performance metrics included the path length, runtime, number of nodes in the path, number of nodes in the search tree or roadmap, and the number of iterations required to obtain the path. Results showed that the Hybrid RRT-PRM method was more effective than the PRM and RRT techniques because of the shorter path length. This new technique searched for a path in the convex hull region, which is a subset of the search area near to the start and end locations. The roadmap for the Hybrid RRT-PRM could also be re-used to find pathways for other sets of initial and final positions. Keywords: Path Planning, Sampling-based algorithms, search tree, roadmap, single-query planners, multi-query planners, Rapidly Exploring Random Tree (RRT), Probabilistic Roadmap (PRM), Hybrid RRT-PRM

scholarly journals MULTIOBJECTIVE PATH PLANNER FOR UAVS BASED ON GENETIC ALGORITHMS

2008 ◽  
Author(s):  
J. M. DE LA CRUZ-GARCÍA ◽  
E. BESADA-PORTAS ◽  
L. DE LA TORRE-CUBILLO ◽  
B. DE ANDRÉS-TORO
Keyword(s):  
Genetic Algorithms ◽  
Path Planner

A Comparison of Metamodel-Assisted Pre-Screening Criteria for Multi-Objective Genetic Algorithms

2009 ◽  
Author(s):  
Vero´nica E. Mari´n ◽  
Jose´ A. Rinco´n ◽  
David A. Romero
Keyword(s):  
Genetic Algorithms ◽  
Performance Metrics ◽  
Potential Effect ◽  
Expected Improvement ◽  
Research Activity ◽  
Screening Criteria ◽  
Design And Optimization ◽  
Multi Objective ◽  
Effectiveness And Efficiency ◽  
Current Approximation

Over the last few years, research activity in approximation (e.g. metamodels) and optimization (e.g. genetic algorithms) methods has improved upon current practices in engineering design and optimization of complex systems with respect to multiple performance metrics, by reducing the number of evaluations of the system’s model that are needed to obtain the set of non-dominated solutions to a given multi-objetive optimal design problem. To this end, several authors have proposed to enhance Multi-Objective Genetic Algorithms (MOGAs) with metamodel-based pre-screening criteria (PSC), so that only those solutions that have the most potential to improve the current approximation of the Pareto Front are evaluated with the (costly) system model. The main goals of this work are to compare the performance of several PSC with an array of test functions taken from the literature, and to study the potential effect on their effectiveness and efficiency of using multi-response metamodels, instead of building independent, individual metamodels for each objective function, as has been done in previous work. Our preliminary results show that no single PSC is observed to be superior overall, though the Minimum of Minimum Distances and Expected Improvement criteria outperformed other PSC in most cases. Results also show that the use of multi-response metamodels improved both the effectiveness and efficiency of PSC and the quality of solution at the end of the optimization in 50% to 60% of test cases.

scholarly journals A Multi-Objective Decision-Based Solution (MODM) for Facility Location-Allocation Problem Using Cuckoo Search and Genetic Algorithms

2020 ◽  
Author(s):  
Amir Shimi ◽  
Mohammad Reza Ebrahimi Dishabi ◽  
Mohammad Abdollahi Azgomi
Keyword(s):  
Genetic Algorithms ◽  
Performance Metrics ◽  
Road Traffic ◽  
Search Algorithm ◽  
Autonomous Vehicle ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the MODM-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.

scholarly journals Kinematic arrangement optimization of a quadruped robot with genetic algorithms

2018 ◽  
Vol 51 (9-10) ◽  
pp. 406-416 ◽  
Author(s):  
Mehmet Mert Gülhan ◽  
Kemalettin Erbatur
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Metrics ◽  
Dynamic Performance ◽  
Performance Measure ◽  
Kinematic Parameter ◽  
Algorithm Optimization ◽  
Performance Improvements ◽  
Performance Specifications ◽  
Dynamics Simulations

Background: As research on quadruped robots grows, so does the variety of designs available. These designs are often inspired by nature and finalized around various technical, instrumentation-based constraints. However, no systematic methodology of kinematic parameter selection to reach performance specifications is reported so far. Kinematic design optimization with objective functions derived from performance metrics in dynamic tasks is an underexplored, yet promising area. Methods: This article proposes to use genetic algorithms to handle the designing process. Given the dynamic tasks of jumping and trotting, body and leg link dimensions are optimized. The performance of a design in genetic algorithm search iterations is evaluated via full-dynamics simulations of the task. Results: The article presents comparisons of design results optimized for jumping and trotting separately. Significant dimensional dissimilarities and associated performance differences are observed in this comparison. A combined performance measure for jumping and trotting tasks is studied too. It is discussed how significantly various structural lengths affect dynamic performances in these tasks. Results are compared to a relatively more conventional quadruped design too. Conclusions: The task-specific nature of this optimization process improves the performances dramatically. This is a significant advantage of the systematic kinematic parameter optimization over straight mimicking of nature in quadruped designs. The performance improvements obtained by the genetic algorithm optimization with dynamic performance indices indicate that the proposed approach can find application area in the design process of a variety of robots with dynamic tasks.

Initial studies on direct sensor management optimization using tracking performance metrics and genetic algorithms

2003 ◽  
Author(s):  
Lingji Chen ◽  
Adel I. El-Fallah ◽  
Raman K. Mehra ◽  
John R. Hoffman ◽  
Ronald P. S. Mahler ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Performance Metrics ◽  
Tracking Performance ◽  
Sensor Management ◽  
Management Optimization

Immigrants Based Adaptive Genetic Algorithms for Task Allocation in Multi-Robot Systems

2017 ◽  
Vol 16 (04) ◽  
pp. 1750025 ◽  
Author(s):  
Pranab K Muhuri ◽  
Amit Rauniyar
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Task Allocation ◽  
Performance Metrics ◽  
Genetic Operators ◽  
Comparative Performance ◽  
Adaptive Genetic Algorithms ◽  
Robot Systems ◽  
Key Issues ◽  
Multi Robot

Optimal task allocation among the suitably formed robot groups is one of the key issues to be investigated for the smooth operations of multi-robot systems. Considering the complete execution of available tasks, the problem of assigning available resources (robot features) to the tasks is computationally complex, which may further increase if the number of tasks increases. Popularly this problem is known as multi-robot coalition formation (MRCF) problem. Genetic algorithms (GAs) have been found to be quite efficient in solving such complex computational problems. There are several GA-based approaches to solve MRCF problems but none of them have considered the dynamic GA variants. This paper considers immigrants-based GAs viz. random immigrants genetic algorithm (RIGA) and elitism based immigrants genetic algorithm (EIGA) for optimal task allocation in MRCF problem. Further, it reports a novel use of these algorithms making them adaptive with certain modifications in their traditional attributes by adaptively choosing the parameters of genetic operators and terms them as adaptive RIGA (aRIGA) and adaptive EIGA (aEIGA). Extensive simulation experiments are conducted for a comparative performance evaluation with respect to standard genetic algorithm (SGA) using three popular performance metrics. A statistical analysis with the analysis of variance has also been performed. It is demonstrated that RIGA and EIGA produce better solutions than SGA for both fixed and adaptive genetic operators. Among them, EIGA and aEIGA outperform RIGA and aRIGA, respectively.

Sign in / Sign up

Export Citation Format

Share Document