A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

Abstract

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

Optimal tracking of a mobile robot with path determined by Random Particle Optimization and genetic algorithms

“…This section considers a linear model for the robot and presents results of the computed command vector, optimal control and resulting trajectories (Eqs. [10][11][12].…”

“…The system follows a command vector (10) from which the optimal control is found as (11) Finally, the optimal trajectory from the discretized state transition equation is thus readily found from (12) III.…”

“…The objective of optimal control is to enable an actuating strategy to achieve best performance typically in the sense of minimum time of mission, minimum energy required, minimum fuel used, or minimum departure from a desired prescribed trajectory. Earlier methods for optimal control include sliding mode control and the artificial potential approach [9,11]. The Linear Quadratic regulator formulation [1][2][3] has been used for linear systems with quadratic performance indicators to obtain optimal control strategies and have also been used for nonlinear analysis [10] which is difficult conceptually and numerically.…”

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

“…They have compared this proposed algorithm to ACO algorithm and stated that the proposed algorithm provides better results (in terms of path length and computational cost) compared to ACO algorithm. Mohajer et al [152] have presented a new Random Particle Optimization Algorithm (RPOA), which is inspired by the bacterial foraging technique, and used for local path planning for mobile robots in the dynamic and unknown environments. The proposed algorithm randomly searches the feasible path in the environment and avoids the moving obstacles by using the sensors.…”

Dynamic robot path planning using an enhanced simulated annealing approach

“…The method is demonstrated for a mobile rover in a completely unknown terrain. A recent work in mobile path planning is by Mohajer et al [14], in which an online random particle optimization algorithm is proposed with consideration of dynamic environments. Another recent work on this topic is by Nakhaeinia and Karasfi [15], in which a behavior-based approach is developed through using fuzzy logic for collision avoidance of mobile robots in unknown and dynamic environments.…”