Path Planning Approach For Robots Research Articles

Comparative Analysis of GA-PRM Algorithm Performance in Simulation and Real-World Robotics Applications

Abstract: This paper presents a comprehensive analysis of the performance of the Genetic Algorithm Probabilistic Roadmap (GA-PRM) algorithm in both simulated and real-world robotic environments. The GA-PRM algorithm is a promising approach for robot path planning, and understanding its behavior in different settings is crucial for its practical applications. In simulations, we explore the advantages of controlled and reproducible test conditions, allowing for extensive parameter tuning and algorithm improvement. Real-world testing is employed to validate the algorithm's performance in actual robotic environments, taking into account the inherent complexities and uncertainties present. In our comparative analysis, we found that the GA-PRM algorithm demonstrates significant improvements in real-world scenarios compared to simulations. Specifically, the algorithm produced shorter paths in real-world robot testing, with an average length of 21.428 cm, as opposed to 25.6235 units in simulations. Moreover, the computational efficiency of the algorithm was notably enhanced in the real-world environment, where it took only 0.375 seconds on average to plan paths, compared to 0.6881 seconds in simulations. The algorithm also exhibited higher path smoothness in the real world, with an average smoothness score of 0.432, compared to 0.3133 in simulations. These results underscore the algorithm's adaptability to real-world conditions and its potential for efficient navigation in practical healthcare and automation applications. Our research bridges the gap between simulation and reality, facilitating the development of more reliable and adaptable robotic systems. The insights gained from this comparative evaluation contribute to a deeper understanding of the GA-PRM algorithm's behavior and its potentials.  

Hybrid metaheuristic approach for robot path planning in dynamic environment

Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

Heuristic approaches in robot path planning: A survey

Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided.

Path planning for mobile robot with artificial endocrine system

In this paper, we present an artificial endocrine system-based path planning (AES-PP) algorithm for robot path planning. Different from the traditional evolutionary algorithms, hormone is firstly secreted by the endocrine cell from the start point. And then, it will diffuse to the goal point where the robot wants to go. With the diffusing of the hormone, some special marks will be signed by the target cells. Finally, the optimal path will be gotten by linking the signed marks on order. We evaluate the performance of the proposed AES-PP approach for robot path planning and the results have shown that the proposed approach is efficient in dealing with the problem with complex search space. At the same time, planning accuracy has been achieved. This is especially important for environments in which the search space is very huge.

Continuous Genetic Algorithms for Collision-Free Cartesian Path Planning of Robot Manipulators

A novel continuous genetic algorithm (CGA) along with distance algorithm for solving collisions-free path planning problem for robot manipulators is presented in this paper. Given the desired Cartesian path to be followed by the manipulator, the robot configuration as described by the D-H parameters, and the available stationary obstacles in the workspace of the manipulator, the proposed approach will autonomously select a collision free path for the manipulator that minimizes the deviation between the generated and the desired Cartesian path, satisfy the joints limits of the manipulator, and maximize the minimum distance between the manipulator links and the obstacles. One of the main features of the algorithm is that it avoids the manipulator kinematic singularities due to the inclusion of forward kinematics model in the calculations instead of the inverse kinematics. The new robot path planning approach has been applied to two different robot configurations; 2R and PUMA 560, as non-redundant manipulators. Simulation results show that the proposed CGA will always select the safest path avoiding obstacles within the manipulator workspace regardless of whether there is a unique feasible solution, in terms of joint limits, or there are multiple feasible solutions. In addition to that, the generated path in Cartesian space will be of very minimal deviation from the desired one.

Optimized robot trajectory generation for thermal spraying operations and high quality coatings on free-form surfaces

For high process reproducibility and optimized coating quality in thermal spray applications on complex geometries, APS (Atmospheric Plasma Spraying) and HVOF (High Velocity Oxygen Fuel) torches are guided by advanced robot systems. The process parameters defined by the robot movement in thermal spraying operations, such as the trajectory, the relative distance, velocity profile and angle between the spraying torch and the substrate, have a major influence on the heat and mass transfer to the component during the process, and therefore on the coating microstructure, physical and chemical properties and their quality. This fact, coupled with the increasing demand of reproducible, high dimensional accurate coatings on free-form surfaces, leads to the necessity of developing software tools for optimized robot trajectory generation. The purpose of this study was to implement an advanced computer aided robot path planning approach for thermal spraying operations which allows the generation, simulation and implementation of the robot trajectory. The optimized generated trajectory and speed profile are integrated in coupled CFD (Computational Fluid Dynamics) and FEM (Finite Element Method) models to analyze their influence on the heat and mass transfer during coating deposition. The developed software tools were applied to coat real components with complex geometry by using the High Velocity Oxygen Fuel (HVOF) spraying system and with WC–Co as the reference spraying material.

Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning

SUMMARY Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of pathfragments matches well to the environment.

Path Planning for Mobile Robots in Irregular Environment Using Immune Evolutionary Algorithm

The traditional evolution algorithm usually traps into local optimization easily. Aiming to the drawback of evolution algorithm, we propose a novel mobile robot path planning approach of immune evolutionary algorithm in irregular environment. The advantages of this method lie in two aspects. The diversity of antibodies can be maintained. Meanwhile immunity operation and optimizing operator can be used to improve the global search ability. The results of simulation experiment show that immune evolution algorithm is feasible and efficient, because it enhances the performance and quality of mobile robot path planning.

Novel approach for robot path planning based on numerical artificial potential field and genetic algorithm

为没有碰撞的路径计划 multipledegree-of-freedom (DOF ) 的一条新奇途径在复杂环境明白表示机器人被建议。基于视觉邻居点(VNP ) ，第一，一块数字人工的潜在的地在 Cartesianspace 被构造，它为机器人关节的运动提供启发式的信息，到目标的有效距离和运动方向。第二，一个基因算法，与启发式的规则结合了，在联合空间被使用决定一系列连续配置 piecewisefrom 起始的配置直到目标，配置被达到。模拟证明方法不能仅仅在路径与复杂障碍在环境计划明白表示的机器人上处理问题，而且改进路径计划的效率和质量。

A genetic algorithm–based area coverage approach for controlled drug delivery using microrobots

Background The use of microrobots for controlled drug delivery shows great potential to achieve precise targeting with controllable side effects. One of the major challenges for controlled drug delivery is robot path planning for area coverage. Methods This article proposes a genetic algorithm (GA)–based area coverage approach for robot path planning. The GA-based area coverage approach is characterized by (1) online path planning combined with offline path planning to cope with environmental uncertainties and (2) optimal path planning for selecting an optimal path by evaluating path lengths and turning angles. The expandable chromosome concept is proposed and implemented for the area coverage. Results Simulation results from 5 different map environments show that the proposed approach achieved significant improvement in path effectiveness compared with the fixed-path approach. Conclusion The proposed GA approach has advantages over traditional path planning approaches in terms of computational costs and has advantages over existing online path planning approaches (eg, fixed-path plan approaches or path-length optimization approaches) in terms of path optimality.

A real-time path planning approach without the computation of Cspace obstacles

An important concept proposed in the early stage of robot path planning field is the shrinking of the robot to a point and meanwhile expanding of the obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision free path for a point robot among the Cspace obstacles. However, the research experiences obtained so far have shown that the calculation of the Cspace obstacles is very hard work when the following situations occur: 1. both the robot and obstacles are not polygons and 2. the robot is allowed to rotate. This situation is even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. Obviously a direct path planning approach without the calculation of the Cspace obstacles is strongly needed. This paper presents such a new real-time robot path planning approach which, to the best of our knowledge, is the first one in the robotic community. The fundamental ideas are the utilization of inequality and optimization technique. Simulation results have been presented to show its merits.

A comparative study of Dirichlet and Neumann conditions for path planning through harmonic functions

Harmonic functions, by virtue of their extrema appearing only on the domain boundary, are known to have an advantage as a global potential function in the potential field-based approach for robot path planning. However, a wide range of possibilities exist for the global harmonic function for a particular situation, depending on the boundary conditions. This paper conducts a comparison of two major classes of boundary conditions, namely Dirichlet and Neumann conditions, and attempts to discover their relative merits and demerits. It is found that the Neumann conditions offer a surer and faster approach to the path-planning problem, though suffering from the disadvantage of occasional tendency of the planned path to graze along the domain boundary. This minor disadvantage, however, can be remedied by a two-stage strategy, in which the solution with the Neumann condition is used to generate the Dirichlet boundary conditions for the second stage of solution.