Mobile Robot Dynamic Path Planning Based on Self-Adaptive Harmony Search Algorithm and Morphin Algorithm

Quan, Yongbin; Ouyang, Haibin; Zhang, Chunliang; Li, Steven; Gao, Liqun

doi:10.1109/access.2021.3098706

Cited by 22 publications

(7 citation statements)

References 29 publications

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“…Hovgard et al [23] developed an optimization approach for reducing energy consumption in multi-robot systems by determining the best execution time and order of robot motions through motion parameter modification. To acquire the optimized path planning, several heuristic-based algorithms such as neural network (NN) [24], fuzzy logic (FL) [25], and nature-inspired algorithms, including GA [26], PSO [27], and ACO [28], as well as certain Artificial Potential Field Algorithm (APFA) [29,30] and some other hybrid models [31,32], are also applied. However, many of these studies do not take energy efficiency into account.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

et al. 2022

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

et al. 2022

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“…The Adaptive and Sound Searching Algorithm can only obtain the initial optimal path in a static environment. The Morphin algorithm was introduced to achieve real-time obstacle avoidance for moving obstacles [15], but it does not consider the impact of different shapes and motion states of dynamic obstacles on USV motion. To solve the problem that global path planning cannot avoid dynamic obstacles, an improved APF algorithm was used for local path planning, and a dynamic warning mechanism was adopted to optimize the step size, forming an improved ACO-APF hybrid algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

Chen,

Feng,

Bao

et al. 2024

JMSE

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Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.

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“…The suggested method is better than the fixed population size firefly algorithm in terms of how stable it is, how quickly it converges, and how long it takes to calculate. The Morphin algorithm [18] was developed to swiftly dodge moving obstacles. Simulation results show that the proposed method performs well for planning an initial, static optimal path.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Robot Path Planning Using an Improved Hunter Prey Optimization Algorithm

2023

IJIES

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This work considers the best path planning algorithm for a mobile robot that travels independently in an unknowable environment. To get around the limitations of unstable searches in the conventional Hunter-Prey Optimization Algorithm (HPO), the improved HPO optimization algorithm is used and introduces a new control parameter referred to as randomization adjustment in order to avoid stagnation and early convergence. The absence of the transfer parameter from exploration and exploitation is a significant flaw in the HPO algorithm, which results in an unstable search and additional time waste. Another new parameter called the changing parameter (CP) is used to address this flaw. It is used in an environment with erratic static and dynamic obstacles and a static and dynamic target. Finding a collision-free path that is also the objectively shortest path and the smoothest path can solve the pathplanning problem. The proposed algorithm attempts to mimic the real world by taking into account the actual size of the mobile robot, a kinematic model, and the robot's specifications. The proposed algorithm is evaluated by comparing it on 30 dimensions using 13 benchmark test functions. The performance of the proposed algorithm is evaluated against the results of five swarm optimization algorithms. According to the results of the standard deviation tests, the proposed algorithm performs the best in 92% of the 13 test functions. Furthermore, the average outcomes for three complex maps (10×10) m in size demonstrate the potency of this approach for robot paths from the starting point to the target. The average distance over ten runs for maps 1, 2, and 3 is 12.6436 meters, 12.4961 meters, and 17.6547 meters, respectively. It demonstrated how quickly and easily it could avoid both stationary and moving obstacles.

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Mobile Robot Dynamic Path Planning Based on Self-Adaptive Harmony Search Algorithm and Morphin Algorithm

Cited by 22 publications

References 29 publications

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

Multi-Objective Robot Path Planning Using an Improved Hunter Prey Optimization Algorithm

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