Immunised Navigational Controller for Mobile Robot Navigation

Mathematical Problems in Engineering

et al. 2023

Self Cite

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively.

Section: Introductionmentioning

confidence: 99%

Trajectory Planning and Collision Control of a Mobile Robot: A Penalty‐Based PSO Approach

Pandey

Kumbhar

Mathematical Problems in Engineering

et al. 2023

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“…Zhou et al 17 have proposed a bio-inspired plant growth algorithm for path planning. Additionally, Parhi et al [18][19][20][21] have proposed an immunized navigational controller, neural network, hybridized fuzzy logic. Furthermore, Liu et al 22 have presented improved ant colony optimization for mobile robot path planning with grid method.…”

Section: Introductionmentioning

confidence: 99%

Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

Kumar

Proceedings of the Institution of Mechanical Engineers, Part B:

Muni

2022

Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length.

“…In global path planning, the environmental information is fully known to robot, whereas in local path planning, environment is fully unknown. In the period of AI, there has been much research to optimize the path-planning problem with the habit of traditional, non-traditional and many natureinspired metaheuristic intelligent techniques, such as Fuzzy logic (Patle et al, 2019), Neural network (Pandey et al, 2016), Particle swarm optimization (Zhang et al, 2019), Firefly algorithm (Patle et al, 2017), Cuckoo search algorithm (Mohanty and Parhi, 2016), Immunized navigational controller (Parhi et al, 2012), Artificial neural network (Parhi et al, 2014, Ant colony optimization (Rashid et al, 2016), Genetic algorithm (Nazarahari et al, 2019), Whale optimization technique (Mirjalili, 2016). Apart from these AI techniques, many hybrid techniques have also been used, such as hybridization of particle swarm optimization and gravitational search algorithm (Das et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

Kumar

Muni

et al. 2020

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Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.