# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 1 Introduction
### Short Literature Review
#### 1.1.2 Optimal Algorithms
Transition-based RRT (T-RRT) [30] is another improved version using the stochastic optimization method. It means that after rapidly growing the random trees, it uses the transition test to accept or reject a new potential state and also combines with the self-tuning parameter to control the RRT's exploration. This algorithm is simulatedto create maps in rough terrain applications. Another path planning algorithm [21] was tested in a 2D environment with the use of distance maps using wave expansion and the depth-first search method. The collision points were indicated and collected in the database, and the goal was to avoid collision to save the manipulation steps. Even though there are large variants of RRT* algorithms available, sometimes it fails to generate the path in more constrained environments where the sampling value is higher than the gap between the obstacles. We propose a novel variant of RRT*, the adaptive RRT*, which is modified by adjusting the sampling value to accommodate the constraint environments [17]. Previous research has shown that when RRT* fails to generate a path in more constraint environments the novel adaptive RRT* algorithm changes the sampling value based on the constraints present in the environment, and thus fetches a collision-free path if one exists. Robots working with humans are required to be aware of their surroundings and they are actively involved in collision checking using the _Moveult_ Library integrated with the Robot Operating System (ROS) [41]. ROSs are used to set up motion planning queries and check for the configuration errors and target being outside of reach in a 3D environment, but they are not directly involved with online path planners [41]. Much research has investigated the comparison between different path planning algorithms tested for industrial robots [42; 43; 44]. Some of the tested algorithms are the sequential algorithm (SEQ), the simultaneous algorithm (A*), the simultaneous algorithm with uniform cost (UC), the simultaneous algorithm with greedy cost (G), and the genetic algorithm (GA). The environment is created with different numbers of obstacles like 1, 2, and 3, and the path length and computational time are measured and compared across all the algorithms [44]. The adapted RRT algorithm is a modified version of RRT by adding the heuristic algorithms. This is tested with autonomous robots. Due to the heuristics algorithm addition, this overcomes the shortcoming of probabilistic completeness, but has the limitation that it needs the predetermined knowledge of intermediate stations for the autonomous robots [46]. Many researchers are exploring metaheuristic algorithms for mobile robot path planning, as they have demonstrated their effectiveness in addressing complex optimization problems [47]. An improved lazy PRM algorithm was implemented online for welding applications [48], but here, the online version is used to detect the obstacles and the environment. Another study showed the comparison between the PRM or RRT and genetic algorithm methods, and the result showed that in terms of computational time, sampling-based algorithms are finding the path in a shorter time compared to computational intelligence methods, such as GAs, but in terms of the path length, computational intelligence algorithms are superior to sampling-based algorithms [49].