## 3 Literature Survey
[PERSON] et al. [31] realized the optimization of an energy model for a floor-cleaning robot that works on a differential drive algorithm dependent upon self-organized behavior. The capacity of customizable robots to vary their size necessitates the implementation of a dynamic estimating energy model, which makes the analysis energy-aware for full coverage planning of paths with these robots extremely fascinating. This study shows the consumption of energy can be measured by journey lengths, taking into consideration how differential drive functions of the floor cleaning frame dynamic patterns operate to complete actions of translation, correction of orientation, and transformation during robot navigation from the origin route-point to the final target route-point. [PERSON] et al. [32] designed an indoor carriage robotic for persons having physical disabilities, wherein it is essential to precisely trace the intended route. Monitoring is severely constrained by the robot's parameter uncertainty and disruptions. A brand-new, reliable tracking controller is proposed that must take into consideration both route monitoring optimization and energy usage reduction. The findings demonstrate that this innovative method can effectively decrease errors in monitoring by 0.2 m to 0.006 m while ensuring the least amount of consumed energy. [PERSON] et al. [33] propose a numerical optimal control strategy that uses non-linear robot physics along its movement and considers minimizing energy and end-duration optimization. By explicitly deriving this strategy based on optimality criteria, the optimal control issue is transformed through an issue of boundary values that might be addressed using state-of-the-art numerical solvers. This technique has been examined in several applications, and the outcomes demonstrate its effective application using a non-holonomic robotic architecture. [PERSON] et al. [34] explore ways in which platform-level enhancements impact the throughput and energy of computations, along with ways to exploit this decision to lesser energy consumed by the computation part without significantly affecting control and throughput performances. This technique employs an offline assessment step of the perceptual algorithm to provide throughput vs. energy graphs for a variety of processing frequencies and different sequencing of its perceiving codes on the central processing unit (CPU) and graphics processing unit (GPU). Based on the results of experiments, this approach might save up to 20% of the energy used while only losing 1% of the overall efficiency of the control system. When the environment is not very complicated, and the optimization terrain is not overly rough, PSO can perform effectively for relatively straightforward navigation tasks. Other approaches, including A* search, genetic algorithms, simultaneous localization and mapping (slam), and reinforcement learning methods, can perform more effectively in environments that are more complicated and difficult. Whenever used to solve particular issues, PSO can swiftly converge to a solution. However, this feature can vary according to the dimensionality, structure, and PSO algorithm parameters of the problem. The factors used, including the number of particles, inertia weight, and cognitive and social aspects, determine the robustness of PSO. Inappropriate tuning of parameters can result in late or early convergence or in the inability to identify a satisfactory solution. Potential field approaches, including rapidly exploring random trees (RRT), have been specially developedto tackle real-time navigation issues and can outperform PSO in such situations. Here, we are considering a situation where obstacles are static, and properties of the environment are also static; thus, PSO will be better than other techniques because of its higher rate of convergence. There are many techniques discussed in the literature survey that are useful for some specific situations.