Robot In Static Environment Research Articles

Development of metaheuristic algorithms for efficient path planning of autonomous mobile robots in indoor environments

The application of efficient path planning algorithms for two-wheeled Autonomous Mobile Robots (AMRs) in static environments with obstacles is a significant challenge in robotics research. Existing methods, such as the A star (A*) algorithm utilized in Robot Operating System 2 (ROS2), can provide optimal paths but may have high computational complexity in intricate environments. This study explores the potential of three metaheuristic algorithms - Improved Particle Swarm Optimization (IPSO), Improved Grey Wolf Optimizer (IGWO), and Artificial Bee Colony (ABC) - for planning efficient and smooth paths in static environments. These algorithms are selected due to their ability to efficiently find near-optimal solutions and avoid local minima. In this study, the researchers designed and built a two-wheeled AMR using a Raspberry Pi 4 microcontroller as the main processing unit, working in conjunction with an Arduino Mega for controlling the DC motor drive through an MDD10A motor driver circuit. The robot is equipped with an RPLiDAR A1 sensor to read 360-degree distance values for mapping and obstacle avoidance. The experimental results clearly indicate that the metaheuristic algorithms, especially ABC, can calculate paths up to 7% shorter than A* while requiring only one-tenth of the time. Moreover, ABC demonstrates superior motion smoothness when applied to the actual two-wheeled robot in static environments. This work represents a significant step in developing algorithms for two-wheeled robots that are ready to support real-world operations in industries, logistics, healthcare, or various service sectors, which can help increase efficiency and reduce operating costs in the future.

A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments.

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

Research on Path Planning of Mobile Robot in Static Environment Based on Improved Ant Colony Algorithm

In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.

Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

AbstractThis paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

Dynamic Path Planning for Mobile Robots Based on the Improved A-Star Algorithm

Based on the path planning and obstacle avoidance problems of mobile robots in dynamic environments, a planning algorithm combining global path planning and local path planning is proposed. Firstly, four different path planning algorithms for robots in static environments are compared. On the basis of the A_star algorithm, an improved A_Star algorithm is proposed. The search direction is reduced from eight to five to improve the search efficiency; a path smoothing optimization algorithm is designed to improve the smoothness of the path; and the adaptive function is optimized to speed up the convergence speed of the algorithm. Secondly, the path planning problem of robots in unknown dynamic environments is studied, and an optimization algorithm combining the improved A_Star algorithm with the rolling window algorithm is proposed. The algorithm can plan a global optimal path and obtain map information through the scrolling window of Dynamic Window Approach (DWA) to calculate a suitable obstacle avoidance strategy in real time for the obstacles that appear, so as to plan the optimal path after avoiding the obstacles. Finally, the simulation platform is used to verify whether the fusion algorithm has better path planning performance through comparative experiments in a randomly changing environment.

A COMPARATIVE STUDY FOR WHEELED MOBILE ROBOT PATH PLANNING BASED ON MODIFIED INTELLIGENT ALGORITHMS

From the time being, there are even instances for application of mobile robots in our lifelike in home, schools, hospitals, etc. The goal of this paper is to plan a path and minimizing thepath lengths with obstacles avoidance for a mobile robot in static environment. In this work wedepict the issue of off-line wheeled mobile robot (WMR) path planning, which best route forwheeled mobile robot from a start point to a target at a plane environment represented by 2-Dwork space. A modified optimization technique to solve the problem of path planning problemusing particle swarm optimization (PSO) method is given. PSO is a swarm intelligence basedstochastic optimization technique which imitate the social behavior of fish schooling or birdflocking, was applied to locate the optimum route for mobile robot so as to reach a target.Simulation results, which executed using MATLAB 2014 programming language, confirmedthat the suggested algorithm outperforms the standard version of PSO algorithm with the sameenvironment conditions by providing the shortest path for mobile robot.

D* lite algorithm based path planning of mobile robot in static Environment

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.

Path Planning for Mobile Robot Based on ACA-GA

Path planning for mobile robot is a kernel problem in the robot technology area, with the characteristics of complexity, binding and nonlinearity. On account of global path planning for mobile robot in static environment, this paper discussed a method of combining ant colony algorithm and genetic algorithm. After completing a cycle of ant colony algorithm, two paths ants walked were randomly selected, and these two paths were further optimized genetically on the basis of certain crossover rate, if more optimal paths were obtained, the pheromone would be released in the more optimal paths, by this method the diversity of solution could be increased and solution speed be improved. The simulation result has verified the effectiveness of the proposed method.

Modified Newton's method applied to potential field based navigation for nonholonomic robots in dynamic environments

SUMMARYThis paper is to investigate inherent oscillations problems of Potential Field Methods (PFMs) for nonholonomic robots in dynamic environments. In prior work, we proposed a modification of Newton's method to eliminate oscillations for omnidirectional robots in static environment. In this paper, we develop control laws for nonholonomic robots in dynamic environment using modifications of Newton's method. We have validated this technique in a multirobot search-and-forage task. We found that the use of the modifications of Newton's method, which applies anywhere C2 continuous navigation functions are defined, can greatly reduce oscillations and speed up robot's movement, when compared to the standard gradient approaches.

Modified Newton's method applied to potential field-based navigation for nonholonomic robots in dynamic environments

SUMMARYThis paper is to investigate inherent oscillations problems of potential field methods (PFMs) for nonholonomic robots in dynamic environments. In prior work, we proposed a modification of Newton's method to eliminate oscillations for omnidirectional robots in static environment. In this paper, we develop control laws for nonholonomic robots in dynamic environment using modifications of Newton's method. We have validated this technique in a multi-robot search-and-forage task. We found that the use of the modifications of Newton's method, which applies anywhere C2 continuous navigation functions are defined, can greatly reduce oscillations and speed up the robot's movement, when compared to the standard gradient approaches.