Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Quadratic Polynomial Equation

Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

ABSTRACTDue to interference phenomena among unnatural dimensions of the motion robots’ operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

For mobile robot autonomous navigation, path planning algorithm matters. For the reason that traditional A* algorithm is unable to achieve the global path planning perfectly and avoid unknown obstacles at the same time, the mobile robot algorithm combining improved A* with adaptive DWA was proposed for path planning. Firstly, safe distance judgment is added in A* algorithm to avoid the problem that the path crosses the vertex of obstacle diagonally. Secondly, the dynamic tangential method is used to smooth the path of A* algorithm, which is more consistent with the actual motion curve of mobile robot. Then, the path key points explored by improved A* algorithm are extracted as intermediate target points of the velocity adaptive DWA algorithm. Unknown obstacles avoidance based on the optimal global path planning is realized. Finally, the proposed fusion algorithm’s feasibility is verified through mobile robot ROS simulation.

Optimal path planning is a key component in recent autonomous driving technology. Many navigation companies have developed services for predicting and navigating the optimal path by receiving information from users in real-time. However, these global path-based methods cannot predict the local, refined path due the resolution limitation of the GPS. In this paper, we present an object detection and lane detection system to predict the optimal local path using point cloud data(PCD) acquired by an autonomous dataset collection vehicle.

Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

A key issue in mobile robot applications involves building a map of the environment that may be used by the robot for path planning. In this paper, a new approach for uneven environment reconstruction based on the information supplied by a millimetre wave (MMW) radar has been proposed. The returned raw data from the MMW are stored in a database and then transferred to the CAD model, and represented as objects using an algorithm. The non-uniform rational B-splines have been used to extract the polygonal mesh decomposition equivalent to the 3D uneven environment and obstacle surfaces. The polygonal mesh decomposition helps to locate the different obstacles and to build the optimal path to reach the target starting from any initial position, by taking into consideration the terrain traversability. The optimal path is obtained by applying distance calculation method and speed limits. Indeed, the returned path is smooth which is a crucial performance consideration, especially for practical service robots.

Main study areas for robotics research can be given as: mapping, localization, navigation and exploration. Given a robot’s current position, partial map of the environment and a goal position; navigation problem can be defined as optimal path planning and path following. Path planning and path following problem should be handled according to environment being static or dynamic, robot's mobility capabilities, sensors used on the robot and the roughness of the environment. In the study a four wheeled, skid-steering robot with laser range finder and depth sensor is built for Gazebo simulation environment. Also a statically structured labyrinth that consists of 15 degree continuous ramps, 15 degree discontinuous ramps, amorphous holes that robot cannot autonomously escape from if fallen into, walls and discontinuous obstacles that are below the robot laser height. 2D simultaneous localization and mapping, 3D mapping, path planning and path following with respect to the 3D map are implemented on Robot Operating System (ROS). Optimal path planning in rough terrain is accomplished by combining A* heuristic with a function of height difference of the 3D map nodes. Path following is carried out by turning-to and moving-towards actions on each sequential path node pairs. Tests performed on the labyrinth shows that obstacle avoidance, path planning and path following can be carried out successfully with the given implementation.

Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

Mobile robots path planning: Electrostatic potential field approach

Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO) algorithm is proposed to get the optimal collision-free path. Dijstra algorithm is applied to search a sub-optimal collision-free path in our algorithm; then the RCPSO algorithm is developed to tackle this optimal path planning problem in order to generate the global optimal path. The crossover operator of genetic algorithm and random coding are introduced into the particle swarm optimization to optimize the location of the sub-optimal path. The experiment results show that the proposed method is effective and feasible compared with different algorithms.

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.