Computational geometry

The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.

S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

Heuristic approaches in robot path planning

A review: On path planning strategies for navigation of mobile robot

This paper reviews the major contributions to the Motion Planning (MP) field throughout a 35-year period, from classic approaches to heuristic algorithms Due to the NP-Hardness of the MP problem, heuristic methods have outperformed the classic approaches and have gained wide popularity After surveying around 1400 papers in the field, the amount of existing works for each method is identified and classified Especially, the history and applications of numerous heuristic methods in MP is investigated The paper concludes with comparative tables and graphs demonstrating the frequency of each MP method’s application, and so can be used as a guideline for MP researchers Keywords—Robot motion planning, Heuristic algorithms

Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s and since then, it has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. In this paper, following a brief introduction to the PSO algorithm, the chronology of its evolution is presented and all major PSO-based methods are comprehensively surveyed. Next, these methods are studied separately and their important factors and parameters are summarized in a comparative table. In addition, a new taxonomy of PSO-based methods is presented. It is the purpose of this paper is to present an overview of previous and present conditions of the PSO algorithm as well as its opportunities and challenges. Accordingly, the history, various methods, and taxonomy of this algorithm are discussed and its different applications together with an analysis of these applications are evaluated. among agents on survival of the fittest. Algorithms related to this group include Evolutionary Programming (EP), Genetic Programming (GP), and Differential Evolutionary (DE). The Ontogeny group is associated with the algorithms in which the adaptation of a special organism to its environment is happened. The algorithms like PSO and Genetic Algorithms (GA) are of this type and in fact, they have a cooperative nature in comparison with other types (16). The advantages of above-mention ed categories can be noted as their ability to be developed for various applications and not needing the previous knowledge of the problem space. Their drawbacks include no guarantee in finding an optimum solution and high computational costs in completing Fitness Function (F.F.) in intensive iterations. Among the aforementioned paradigms, the PSO algorithm seems to be an attractive one to study since it has a simple but efficient nature added to being novel. It can even be a substitution for other basic and important evolutionary algorithms. The most important similarity between these paradigms and the GA is in having the seam interactive population. This algorithm, compared to GA, has a faster speed in finding the solutions close to the optimum and it is faster than GA in premature convergence (4).

Particle Swarm Optimization Methods, Taxonomy and Applications

Assembly Planning (AP) is one of the most important elements of process planning in manufacturing industries, and is defined as the process of creating a detailed assembly plan to craft a whole product from separate parts considering the final product geometry, available resources, fixture design, feeder and tool descriptions, etc. AP has three main subproblems: (1) Assembly Sequence Planning (ASP), in which a sequence of collision-free operations is computed for bringing assembly parts together, (2) Assembly Line Balancing (ALB), in which some groups of subassemblies are formed and assigned to assembly stations in a way that their workloads are balanced, and (3) Assembly Path Planning (APP), in which collision-free paths for adding parts to a subassembly are computed. Each of the above subproblems has a disassembly version, creating DASP, DALB, and DAPP problems. All of the above problems have proven to be either NP-hard or NP-Complete, and many researches have been conducted to solve them efficiently. While some surveys and reviews exist on the ASP/DASP and ALB/DALB problems, no comprehensive survey exists for APP/DAPP problems, despite their important role in the design process of products as invaluable tools for deploying concurrent engineering, end-of-life processing, maintenance and repair, and decreasing the cost and time of manufacturing products. This paper investigates the relations between the above six subproblems and reviews the state-of-the-art of the APP and DAPP problems and their solution approaches. Through two new taxonomies the properties and categories of APP/DAPP problems and solution approaches are identified and described, the characteristics and applications of the reviewed 60 most relevant works are exposed and analyzed comprehensively, and open problems in the field are identified. State-of-the-art review of the Assembly/Disassembly Path Planning (APP/DAPP) field.New taxonomies for categorizing APP/DAPP problem types and solution methods.Critical discussions on research trends, applications and open problems in APP/DAPP.

Review and taxonomies of assembly and disassembly path planning problems and approaches

In this paper a novel method is presented for robot motion planning with respect to two objectives, the shortest and smoothest path criteria A Particle Swarm Optimization (PSO) algorithm is employed for global path planning, while the Probabilistic Roadmap method (PRM) is used for obstacle avoidance (local planning) The two objective functions are incorporated in the PSO equations in which the path smoothness is measured by the difference of the angles of the hypothetical lines connecting the robot's two successive positions to its goal The PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM The proposed algorithm is compared in path length and runtime with the mere PRM method searched by Dijkstra's algorithm, and the results showed that the generated paths are shorter and smoother and are calculated in less time

A multi-objective PSO-based algorithm for robot path planning

Numerous methods have been developed to solve the motion planning problem, among which the Voronoi diagram, visibility graph, and potential fields are well-known techniques. In this paper, a new path planning algorithm is presented where these three methods are integrated for the first time in a single architecture. After constructing the generalized Voronoi diagram of C-space, we introduce a novel procedure for its abstraction, producing a pruned generalized Voronoi diagram. A broad freeway net is then developed through a new a-MID (maximal inscribed discs) concept. A potential function is assigned to the net to form an obstacle-free network of valleys. Afterwards we take advantage of a bidirectional search, where the visibility graph and potential field modules execute alternately from both start and goal configurations. A steepest descent mildest ascent search technique is used for local planning and avoiding local minima. The algorithm provides a parametric tradeoff between safest and shortest paths and generally yields shorter paths than the Voronoi and potential field methods, and faster than the visibility graph. It also performs well in complicated environments. © 2004 Wiley Periodicals, Inc.

Ellips Masehian

Papers

Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review

Particle Swarm Optimization Methods, Taxonomy and Applications

Review and taxonomies of assembly and disassembly path planning problems and approaches

A multi-objective PSO-based algorithm for robot path planning

A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning