An Extended Coronavirus Optimization Algorithm for Trajectory Planning of a Model-Based Racing Track

In light of the latest pandemic, many researchers proposed a nature-inspired Coronavirus algorithm to aid in tackling different optimization problems. Many meta-heuristic techniques were conducted to handle the trajectory planning of vehicles on track; however, no implementation or comparison is formulated using the aforementioned new technique. This paper proposes a modified version of the Coronavirus optimization algorithm to aid the drivers offline prior to entering the track. To ensure this algorithm's capability, a comparative study is conducted between the extended Coronavirus Optimization Algorithm (CVOA) and other meta-heuristic techniques such as: Genetic Algorithm (GA) and Grey Wolf Optimization Algorithm (GWO). Using MATLAB, two different tracks were visualized and the aforementioned techniques were employed to generate a feasible trajectory plan. All equations are represented using a real vehicle model to verify the algorithms' outputs with real case scenarios.