Problem Of Robot Path Planning Research Articles

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Abstract Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

A novel global path planning method for robot based on dual-source light continuous reflection

Aiming to address the problem of robot path planning in environments containing narrow passages, this paper proposes a novel global path planning method: the DSR (Dual-source Light Continuous Reflection Exploration) algorithm. This algorithm, inspired by the natural reflection of light, employs the concept of continuous reflection for path planning. It can efficiently generate an asymptotically optimal path on the map containing narrow passages. The DSR algorithm has been evaluated on different maps with narrow passages and compared with other algorithms. In comparison with the bidirectional Rapidly-exploring Random Tree algorithm, the DSR algorithm achieves a significant reduction in both path length (by 27.08% and 34.35%) and time consumption (by 98.47% and 91.03%). Numerical simulations and experimental analysis have demonstrated the excellent performance of the DSR algorithm.

Shortest path planning via the rapid Dhouib-Matrix-SPP (DM-SPP) method for the autonomous mobile robot

Planning the shortest path for an autonomous mobile robot is a challenging problem. It consists in designing for the robot the optimal path to join the ending location from a starting position with avoiding the obstacle spaces. In this paper, the innovative greedy Dhouib-Matrix-SPP (DM-SPP) method is proposed to address this problem in a static environment with a grid model representation. At first, the grid model is codified as a graph using the eight movement directions where the maximal time complexity of DM-SPP for this problem is O(9*n) with n is the number of nodes in the graph. Then, DM-SPP uses this graph to rapidly search for the shortest path between the initial and the goal nodes. Hence, DM-SPP is developed with Python language and the planning path is illustrated using the Matplotlib library. Finally, experiments on eleven grid models with comparison on twelve metaheuristics (such as Particle Swarm Optimization integrated with Gray Wolf Optimization, Ant Colony Optimization combined with A* method and other metaheuristics) are studied and the results demonstrated the rapidity and the effectiveness of DM-SPP in designing the shortest path for the autonomous mobile robot. Clearly, DM-SPP improves massively the running time and the quality compared to all the other techniques and it can be concluded that DM-SPP is the fastest artificial intelligence method for the Mobile Robot Path Planning Problem.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning

This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results.

Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLBO) algorithm and optimize the final path. Firstly, a divide-and-conquer design, coupled with the Dijkstra method, is developed to realize the problem transformation so as to pave the way for algorithm deployment. Secondly, the interpolation method is utilized to smooth the traveling route as well as to reduce the problem dimensionality. Thirdly, an opposition-based learning strategy is embedded into the algorithm initialization to create initial solutions with high qualities. Finally, a novel, individual update method is established by hybridizing the TLBO algorithm with differential evolution (DE). Simulations on benchmark functions and MRPP problems are conducted, and the proposed ELTBO is compared with some state-of-the-art algorithms. The results show that, in most cases, the ELTBO algorithm performs better than other algorithms in both optimality and efficiency.

Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Hybridization of genetic algorithm and fuzzy based TOPSIS method to solve the robot path planning problem for multi objectives

A variety of approaches are employed to solve the Mobile Robot Path Planning Problem (MRPP), which is the essential domain of research from industrial automation to domestic appliances. Since MRPP is categorized as an NP-hard problem, non-deterministic approaches are preferred to obtain optimal results. The Genetic Algorithm(GA) is the popular methodology for determining the optimal path for robot navigation. The complexity increases in finding the optimal path for the MRPP problem, if more than one objective is to be considered. To reduce the complexity, one of the multi-criteria decision analysis (MCDA) methodologies with fuzzy logic is hybridized to solve MRPP without losing the advantage of the GA approach. In this article, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for determining the best path out of given paths. As a preprocessing of the population, a Genetic Algorithm (GA) is employed, which is having the advantage of exploring the population with crossover, mutation, and selection operators. The output of the GA is given to the Fuzzy TOPSIS method where the degree of preference is given as the linguistic variable.

Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Cubic 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.

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

Path control of panoramic visual recognition for intelligent robots based-edge computing

The application of service-oriented intelligent robots in hospitals solves the problems of manual service such as delivery of medicines and orders from the medical aids. However, there is a problem of robot path planning, which leads to poor service. Taking the self-control robot with visual recognition capability in medical institutions as the research background, this paper proposes an image edge detection algorithm based on three-dimensional features. First, reconstruct the three-dimensional image through visual recognition, and calculate the three-dimensional feature map of the scene in the hospital. Secondly, the edge computing algorithm is used to realize the route area diversion, and the best route area is planned in the 3D feature map to realize the delivery of medicines and other items in the complex environment of the hospital, so as to achieve the purpose of improving the technical level of medical auxiliary equipment. Finally, the image restoration technology can improve the image clarity of the robot vision system and the robot automation level, and it is also helpful for the promotion of robot automatic path planning and control technology. The experimental results show that, compared with the hidden Markov algorithm, the method in this paper is significantly better than the hidden Markov algorithm, which meets the needs of medical institutions for intelligent robots.

A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization

This paper describes the path planning of an autonomous mobile robot using an advanced version of the swarm bat algorithm in a time-varying situation. The foremost objective of this present contribution is to acquire the shortest path between the initial and end-point avoiding dynamic obstacles. In this work, an adaptation of the frequency parameter of the standard bat algorithm to develop an improved version known as the modified frequency bat (MFB) algorithm is proposed. The modified frequency bat algorithm works in two steps, in the first step, the robot path is generated when there are no obstacles in the environment using the proposed method. As soon as an obstacle is detected near the robot, the second mode is initiated to avoid any obstacle. Extensive simulations are performed to validate the effectiveness of the proposed modified frequency bat algorithm by implementing it on standard benchmark functions and path planning problems and simultaneously matching its performance with the original bat algorithm. It has been concluded that the modified frequency bat algorithm shows superior performance as compared to the standard bat algorithm by drawing a shorter and collision-free path for the mobile robot path-planning problem.

The Wolf pack assignment rule based on ant colony algorithm and the path planning of scout ants in complex raster diagra

The traditional ant colony algorithm based on raster algorithm has slow searching speed and convergence speed in the problem of robot path planning, and it is easy to fall into the problem of local optimal. In order to solve this problem, an improved ant colony algorithm based on Wolf distribution rules and reconnaissance ant strategy is proposed, which can adjust the pheromone coefficient adaptively and obtain a more suitable proportion of pheromone allocation. Experiments show that this algorithm is superior to the traditional ant colony algorithm in terms of optimal path searching speed, quality and convergence speed in simple and complex environments.

Multi‐robot path planning based on a deep reinforcement learning DQN algorithm

The unmanned warehouse dispatching system of the ‘goods to people’ model uses a structure mainly based on a handling robot, which saves considerable manpower and improves the efficiency of the warehouse picking operation. However, the optimal performance of the scheduling system algorithm has high requirements. This study uses a deep Q-network (DQN) algorithm in a deep reinforcement learning algorithm, which combines the Q-learning algorithm, an empirical playback mechanism, and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning. The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem: slow convergence and excessive randomness. Preceding the start of the algorithmic process, prior knowledge and prior rules are used to improve the DQN algorithm. Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems. This improves the efficiency of multi-robot path planning.

Robot Path Planning Method Based on Level Set

For the problem of robot path planning under the action of external force field, the directionality of outward force can promote and hinder the movement of the robot. The continuous path planning method can break through the traditional robot path planning algorithm based on grid search. Limit, effectively utilize the external force field existing in the space, and improve the numerical implementation method to ensure the feasibility of the planned path in the strong external force field. Based on the Level Set method, the path planning problem is defined. The particle tracking of the robot is transformed into the numerical evolution of the curve. The optimal time path is obtained by solving the Hamilton-Jacques equation. The numerical calculation accuracy of the improved backward path tracking is guaranteed. The feasibility of planning the path. The simulation results show that the algorithm can effectively deal with a variety of complex external force fields and maintain the path in a strong external force field.

An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively.

Robotic Path Planning Based on a Triangular Mesh Map

Aiming at the problem of robot path planning in complex maps, an algorithm of robot path planning based on triangular grid graph is proposed. Firstly, a weighted undirected loop graph and a feasible domain of nodes are obtained by discretizing the triangular mesh map. Next, the Dijkstra search algorithm is applied to find the feasible shortest path from an initial to a final configuration. Finally, The Douglas-Peucker algorithm is applied to remove duplicate and redundant nodes in the feasible path, and the waypoint are extracted. The final path is a curve that is obtained by connecting the several extracted waypoint. The proposed algorithm is tested for various maps. Compared with probabilistic roadmap method, the experimental results show that the proposed method can overcome the shortcomings of the random sampling method. Furthermore, the experimental result of triangular mesh map method used in two labyrinth maps show that the triangular mesh map method can solve the robot path planning problem in complex map very well, and it is an excellent algorithm for robot path planning.

A new global best guided artificial bee colony algorithm with application in robot path planning

Artificial bee colony has received much attention in recent years as a competitive population-based optimization algorithm. However, its slow convergence speed and one-dimensional search strategy limit it from demonstrating advantage in separable functions. To address these concerning issues, this paper introduces a coevolution framework into ABC and designs a global best leading artificial bee colony algorithm with an improved strategy to accelerate its convergence and conquer the dependency of dimension separately. A set of classical and Congress on Evolutionary Computation 2015 benchmark functions are adopted for validating the efficiency of our algorithm. In addition, in order to show the practicality of our algorithm, a robot path-planning problem is tested, and our algorithm still achieves superior results.

Shuffled teaching learning-based algorithm for solving robot path planning problem

To evade the big and destructive obstacles in the real world scenario, such as bomb blast, nuclear activities, and fire breakdowns, robots are necessary. Robot path planning (RPP) problem is one of the interesting NP-hard problems in the world of robotics. The RPP problem can be dealt with, using swarm intelligence (SI) based optimisation algorithms. Teaching learning based optimisation (TLBO) algorithm is a very efficient and reliable swarm intelligence based algorithm in the history of optimisation. This paper proposed a hybridised version of TLBO with shuffled frog leaping algorithm (SFLA) to improve the efficiency in terms of exploitation and to overcome the slow convergence rate. The proposed variant is named as shuffled teaching learning-based optimisation (STLBO) algorithm. For checking the efficiency and accuracy of the proposed STLBO, it is applied to 12 continuous benchmark functions and compared with different nature inspired algorithms (NIA). To check the robustness of the propounded STLBO, it is implemented to solve the problem of RPP. Through simulation results and statistical analyses, the effectiveness of the proposed STLBO is proved.

An Advanced Quantum Optimization Algorithm for Robot Path Planning

In this paper, a new robot path planning algorithm based on Quantum-inspired Evolutionary Algorithm (QEA) is proposed. QEA is an advanced evolutionary computing scheme with the quantum computing features such as qubits and superposition. It is suitable for solving large scale optimization problems. The proposed QEA algorithm works in the discretized environment, and approximates the optimal robot planing path in a highly computationally efficient fashion. The simulation results indicate that the proposed QEA algorithm is suitable for both complex static and dynamic environment and considerably outperforms the conventional genetic algorithm (GA) for solving the robot path planning problem. Our algorithm runs in only about 2[Formula: see text]s, which demonstrates that it can well tackle the optimization problem in robot path planning.