# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## 1 Introduction  
The advancements in the field of robotics have a long history that dates back to the 1960s, when the first industrial robot was developed. Since then, many types of robots, such as humanoid robots, unmanned aerial vehicles (UAVs), unmanned water vehicles (UWVs), bio-inspired robots, sci-fi robots, and unmanned ground vehicles (UGVs) more commonly known as mobile robots, found their applications in various fields [1; 2; 3; 4]. One of the key areas where robots are extensively used is exploration and monitoring. For the purpose, mobile robots are mainly utilized due to their affordability and maneuverability. Realistically, all mobile robots are supposed to maneuver in their surroundings in the presence of obstacles. Therefore, path planning (PP) is considered the most critical task that defines the success or failure of the whole process. Robots are usually equipped with multiple input sensors and decision-making control systems in order to traverse through a trajectory, which can be foreknown or determined online. It is desired thatrobots be capable enough to avoid complex obstacles while online or offline. The degree of severity is multiplied many times for unknown cluttered environments [5; 6]. The environment is perceived through different sensors, such as ultrasonic sensors, laser range finders, and cameras. To avoid the obstacles, the most common approach is to control the heading angle of the mobile robot in order to steer away. There are many algorithms that help robots to perform such actions; some are preferred over others based upon their performances. In the field of PP, some researchers have focused on the development of intelligent mobility strategies that enable mobile robots to explore both static and dynamic environments independently [7]. There can exist multiple paths that lead to the target. The selection of an optimal path is usually dependent upon the satisfaction of some constraints or rules, such as shortest distance, least energy consumption, and minimum time. Among these, the cost function based upon displacement is considered the most important due to its effectiveness in almost all situations [8]. Therefore, the overall optimization problem can be posed as the determination of the path that ensures the shortest distance from the start to the end point. Rapidly-exploring random trees, probabilistic roadmaps [9], genetic algorithms [10; 11; 12], neural networks, simulated annealing [13], particle swarm optimization [14; 15], ant colony optimization [16], and bacterial foraging optimization [17; 18] to name a few, belong to the wide class of heuristic and meta-heuristic algorithms [19]. These methods have their own advantages and disadvantages. For over a decade, mathematical programming, cell decomposition, roadmap, and the artificial potential field (APF) have been considered as handy tools to perform motion planning. APF is still considered as one of the most common and reliable planning techniques [20]; however, it provides only one trajectory that may not be optimal necessarily. APF received much attention after the work of [PERSON] et al., who worked upon obstacle avoidance in an effective manner [21]. It has also been shown in [22] that potential fields can be utilized for controlling robot swarm formation, collision prevention, and swarm motion. The artificial field generated around the mobile robot comprises two main forces: the attractive potential field force that attracts the robot to the goal point, and the repulsive potential field force that repels the robot away from any obstacle's field. The total potential field of APF is generated when both forces are combined (attractive + repulsive) [23]. APF is considered an effective navigation approach, but it suffers from a local minima problem. When the amounts of attractive and repulsive forces acting on robot become equal, its movement is ceased unless some external effect is applied.