Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

Kumar, Saroj; Parhi, Dayal R.; Pandey, Krishna Kant; Muni, Manoj Kumar

doi:10.1017/s0263574721000114

Cited by 14 publications

(8 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a leader-follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles. Kumar S. [17] proposed a hybrid approach of intelligent water drop and genetic algorithm technique and executed it in order to obtain global optimal path by replacing local optimal points. Many studies have proved the effectiveness and advantages of flocking control for multi-robot formation, coordination control, and obstacle avoidance control [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

View full text Add to dashboard Cite

Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

View full text Add to dashboard Cite

show abstract

“…Muni et al [25][26][27] have developed fuzzy and water cycle approaches for navigational control of a humanoid robots. Kumar et al [28] have proposed a hybrid model for trajectory planning of mobile robots. Montiel et al [29] have explained bacterial foraging behavior for path planning of robots.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Kumar

Parhi

2022

Robotica

Self Cite

View full text Add to dashboard Cite

Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

show abstract

“…Pandey et al [44] have proposed ANFIS controller for navigational control of mobile robot. Kumar et al [45] have explained IWD-GA hybrid algorithm for optimal path search and target detection of mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.

show abstract

Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

Cited by 14 publications

References 53 publications

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots

Contact Info

Product

Resources

About