## 3 Literature Survey  
In recent decades, many researchers have focused on optimizing energy and time constraints for the optimal navigation of MR using different techniques. [PERSON] et al. [25] describe the MR control technique by keeping travel time, energy consumption, and path length as a base of study. The controlling method directs how the robot moves in line with the planned module's pathway determination, while the correctness of the route's modeling is crucial. A criterion is developed to control the navigation of MR. A QBot2e MR is used for the experiment, which works on the principle of differential drive dynamics. This work shows an optimization of energy and time constraints with the shortest path length taken by MR during the experiment. [PERSON] et al. [26] explain how we can optimize the energy for a wheeled MR during navigation. This work used a vision mapping technique and some major navigation algorithms, including A-star and RRT, for analysis. Using the foundation of forward movement modeling and odometry observations, the navigational outcomes are analyzed. An RGBW camera is utilized for making obstacles containing mapping. Also, binary occupation mapping was created to estimate the traffic path. This paper demonstrates that the RRT-star technique needs the least energy during navigation from the starting point to the target point. [PERSON] et al. [27] examine recent research on energy-efficient techniques for autonomous wheeled MRs to navigate various stages. To determine research gaps, the chosen papers in this study have been separated into fields for planning and controlling motions. Through multiple simulations and assessments, this paper demonstrates the immediate impact of the motion control step on the overall consumption of energy employing a manufacturing self-guided vehicle (SGV). The findings suggest that the quantity of energy utilized might differ depending on how the SGV responds to unanticipated obstacles. This study explains that, while creating an action plan, energy limits need to be taken into consideration. [PERSON] et al. [28] propose a new modeling of energy approach for MRs is suggested to increase the efficiency of energy use for an MR. Modeling of the energy utilization system for MR is done based on three features, including the motion system, the control system, and the sensor system. Mathematical formulas are used to further explain the association among all three of the features. Then, the mathematical framework is subsequently utilized in a four-wheeled Mecanum MR, where it is practically evaluated. The outcomes of this study indicated that the suggested energy modeling might be utilized to forecast the consumption of energy for MR mobility activities. [PERSON] et al. [29] present a strategy to find the best-suited path for MR on terrains with the lowest energy requirements. This research examines the issue of estimating the most effective routes for an MR to take on various terrains, whereas the associated expense of a route can be expressed as the energy required to compensate for resistance and gravitational. Certain upper- and lower-bound findings about the cumulative length of ideal pathways on landscapes in this paradigm are used. An effective approximation technique is described in this article that estimates a route between two supplied places where the price falls under a user-defined relative deviation proportion. [PERSON] et al. [30] describe a new approach for optimal movement planning with the objective of reducing the requirements of energy for a wheeled MR within applications involving robotics. Firstly, a model is generated and might be employed to estimate the amount of energy required to overcome friction resistance and convert the energy into kinetic energy. A proper coming velocity and time of the mentioned directions are chosen for the least amount of energy to deliver an easy trajectory throughout the created track. This simulation can be used as a foundation to optimize the usage of energy for MR by applying efficient planning of motion.