I. INTRODUCTIONNowadays, sustainability becomes a general requirement in engineering and sciences, including in path planning technologies. Path planning is very important for an object, which needs to generate motion, such as a vehicle robot, an arm robot manipulator, a missile, a ship, a submarine, and an aircraft. It is a process to design a feasible path that becomes a trace for vehicles or robots.One of the advantages of using a Bezier curve for path planning is its adaptability to change the control points. It has been widely used as a manufacturing path in additive manufacturing [1,2,3], vehicle route path of an aircraft [4], and vessel path motion [5]. The goal of sustainability in the manufacturing system is still in the way of the process. In the future, additive manufacturing will be developed into sustainable manufacturing via path planning technologies [1]. Thus, research on the path planning methodologies that address a sustainability goal is highly important.Sustainable manufacturing refers to applying an approach for manufacturing that has minimal harm to environments and mitigated bad impacts on human health and nature [6]. For the robots and vehicle systems, which commonly operate in obstacle environments, it is necessary to achieve the safety goal by generating a collision-free path. Collision avoidance strategies become an important issue in path planning. The concept of collision avoidance dynamic critical area for vessel path planning was introduced in [5]. Collision avoidance for human-robot interactions in adaptive manufacturing was presented in [3]. Saeed et al.[7] proposed a boundary node method to solve mobile robot path planning in static obstacle environments. Kala et al. [8] applied multi-neuron heuristic search to solve the path planning in the static obstacle environment.The GA as one of the evolutionary methods has been applied to solve the point-to-point path planning. Linquan et al.[9] proposed to use GA for path planning of robot soccer systems in a dynamic environment. Choi et al. [10] presented two-path planning algorithms based on Bezier curves for autonomous vehicles with waypoints and corridor constraints. Lil et al. 11] assumed the existence of an obstacle-avoiding polyline path and replaced the polyline path with a G 2 cubic spline curve to avoid the obstacles.