Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach

Zou, Awei; Wang, Lei; Liu, Weimin; Cai, Jingcao; Wang, Hai; Tan, Tielong

doi:10.1007/s11227-022-04998-z

Cited by 23 publications

(10 citation statements)

References 34 publications

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“…Covariance matrix adaptation evolution and proximal policy optimization Avoid obstacles [33] Collision-free paths for individual robots Improved PSO algorithm and evolutionary operators Arrival time, secure route construction, and energy usage [34] Mobile robot route planning Multi-goal Genetic Algorithm (MOGA) Safety, distance, smoothness, traveling time, and collision-free path [35] Mayfly optimization algorithm for robot route planning Improved mayfly optimization algorithm based on qlearning Global search capabilities and avoidance of local optima [36] Path planning for multiple robots Enhanced artificial bee colony algorithm and ABCL method Optimal collision-free courses for multiple robots Suresh, et al [34] proposed the Mobile Robot route Search powered by a Multi-goal Genetic Algorithm (MRPS-MOGA), a new method that uses a genetic algorithm with various goal functions to solve mobile robot route planning issues. The primary purpose of MRPS-MOGA is to determine the most efficient route by taking into account five specific criteria: safety, distance, smoothness, trip time, and avoidance of collisions.…”

Section: Related Workmentioning

confidence: 99%

“…Zou, et al [35] have discussed issues in the fundamental Mayfly Optimization Algorithm (MOA) for robot route planning, such as sluggish convergence, low precision, instability, and applicability limited to static situations. A fusion technique was suggested that merges an enhanced Mayfly Optimization technique with the Dynamic Window Approach.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

TANG,

ZHAO

2024

ijacsa

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Path planning is a prominent and essential part of mobile robot navigation in robotics. It allows robots to determine the optimal path from a given beginning point to a desired end goal. Additionally, it enables robots to navigate around obstacles, recognize secure pathways, and select the optimal route to follow, considering multiple aspects. The Whale Optimization Algorithm (WOA) is a frequently adopted approach to planning mobile robot paths. However, conventional WOA suffers from drawbacks such as a sluggish convergence rate, inefficiency, and local optimization traps. This study presents a novel methodology integrating WOA with Lévy flight and Differential Evolution (DE) to plan robot paths. As WOA evolves, the Levy flight promotes worldwide search capabilities. On the other hand, DE enhances WOA's ability to perform local searches and exploitation while also maintaining a variety of solutions to avoid getting stuck in local optima. The simulation results demonstrate that the proposed approach offers greater planning efficiency and enhanced route quality.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

TANG,

ZHAO

2024

ijacsa

View full text Add to dashboard Cite

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“…The Q-learning algorithm and Deep Q-Network (DQN) algorithm are two well-known techniques in the field of artificial intelligence. Zou et al [12] integrated the Q-learning algorithm with the Dynamic Window approach and the Mayfly Optimization algorithm to improve the algorithm's global search capability and adaptability to complex environments. H. Quan et al [13] enhanced the experience replay and sampling mechanisms within the DQN algorithm to augment its adaptability in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots

Chen,

Wang,

Deng

et al. 2024

Sensors

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Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots’ maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm’s performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot’s mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants.

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“…Hossain [12] et al guided the robot toward the desired goal by finding an appropriate distance and designed a hybrid algorithm of DWA and the following gap method to generate collision-free running trajectories for mobile agents. Zou [13] et al extracted global path nodes as sub-target points according to the mayfly optimization algorithm and combined the DWA algorithm for path planning, which effectively improved real-time obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Efficient obstacle avoidance and path planning for mobile agent through complicated environments

Yu,

Chen,

Zeng

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The Dynamic Window Approach (DWA) suffers from unreasonable planning or is prone to obstacle avoidance failure when the mobile agent operates in a complex environment with dense obstacle distribution. An improved DWA path planning algorithm (IDWA) is proposed to address the above problems. Firstly, the heading angle function is modified to improve navigation ability for mobile agent reaching at the target point while ensuring operational safety. Secondly, the obstacle function is optimized to avoid the agent going around the obstacle's edge. Finally, a complex scenario with multiple obstacles of different passing difficulties is constructed to verify the feasibility of the proposed method. Simulation experiments show that IDWA significantly improves the agent's passage efficiency and local search ability, and the path planning is more reasonable with smaller path deviation and heading deviation.

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Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach

Cited by 23 publications

References 34 publications

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots

Efficient obstacle avoidance and path planning for mobile agent through complicated environments

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