1,144 research outputs found
Development a New Intelligent Mobile Robot to Avoid Obstacle
The project is a robot that automatically by passes barriers to reach a specific goal with an ultrasonic help that senses obstacles and measures the remaining transitions before the collision is meet. The robot changes its course with a couple of DC motors, Robot runs automatically without any interference by the Proportional-Integral-Derivative (PID) algorithm. The goal of this paper is to develop a path planning method that is capable of planning the mobile robot path from the starting position to the target position in different environments. However, the parameters of membership functions and PID controller parameters have optimized by using particle swarm optimization (PSO) algorithm. In addition to that, the proposed method with two Schemes of motion controllers are test with varying static and dynamic environments with and without load. The artificial potential field algorithm is introduce for path planning of mobile robot. However, the potential field algorithm is effective in avoiding unknown obstacles, but it contains minimal local problems, then a modified field algorithm is introduce to overcome some of the local minimum problems in the environment. Therefore, it is enhancing the performance of potential field algorithm and to produce a more efficient path planning method, that to allow mobile robot to navigate in dynamic and complex environments. As well as, simulation of mobile robot is design to test and implement the proposed method and control schemes using MATLAB and the software is develops by using C++ language and Arduino IDE. DOI: 10.7176/CEIS/10-3-03 Publication date: April 30th 201
Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm
The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface
Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization
The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone
Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review
Autonomous ground vehicle systems have found extensive potential and
practical applications in the modern world. The development of an autonomous
ground vehicle poses a significant challenge, particularly in identifying the
best path plan, based on defined performance metrics such as safety margin,
shortest time, and energy consumption. Various techniques for motion planning
have been proposed by researchers, one of which is the use of artificial
potential fields. Several authors in the past two decades have proposed various
modified versions of the artificial potential field algorithms. The variations
of the traditional APF approach have given an answer to prior shortcomings.
This gives potential rise to a strategic survey on the improved versions of
this algorithm. This study presents a review of motion planning for autonomous
ground vehicles using artificial potential fields. Each article is evaluated
based on criteria that involve the environment type, which may be either static
or dynamic, the evaluation scenario, which may be real-time or simulated, and
the method used for improving the search performance of the algorithm. All the
customized designs of planning models are analyzed and evaluated. At the end,
the results of the review are discussed, and future works are proposed
A Fast Path Planning Algorithm for a Mobile Robot
The path planning problem finds a collision free
path for an object from its start position to its goal position while avoiding obstacles and self-collisions. Many methods have been proposed to solve this problem but they are not optimization based. Most of the existing methods find feasible paths but the objective of this current research is to find optimal paths in respect of time, distance covered and safety of the robot. This paper introduces a novel optimization-based method that finds the shortest distance in the shortest time. It uses particle swarm optimization (PSO) algorithm as the base optimization algorithm and a customized algorithm which generates the coordinates of the search space. We experimentally show that the distance covered and the generated points are not affected by the sample size of generated points, hence, we can use a small sample size with minimum time and get optimal results, emphasizing the fact that with little time, optimal paths can be generated in any known environment
Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments
This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges
Mobile Robot Path Planning in Static Environment
The success of Particle Swarm Optimization (PSO) and Genetic algorithm (GA) as single objective optimizer has motivated researchers to extend the use of this bio- inspired techniques to other areas. One of them is multi-objective optimization. As a part of this review we present a classification of the approaches and identify the main approaches here. We describe useful performance measures and simulation results of conventional Genetic algorithm and PSO. We extend this to multi-objective genetic algorithm and PSO. This means that GA and PSO optimizes path based on two criteria: length and difficult. Another method that has new to this field of research is the Artificial Potential field method. In this method the entire space is supposed to contain a potential field and we calculate the net force that is acted upon the robot to reach its goal
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