Optimal Path Planning for an Autonomous Mobile Robot Using Dragonfly Algorithm

Abstract

Navigation, path generation and obstacle avoidance are considered as the key challenges in the area of autonomous mobile robots. In this article, a new meta-heuristic optimization technique called Dragonfly Algorithm (DA) is employed for the navigation of autonomous mobile robot in an unknown cluttered environment filled with several static obstacles. This new meta-heuristic Dragonfly algorithm is inspired from the static and dynamic swarming behaviours of dragonflies in nature. Two objective functions, target seeking and obstacle avoidance are formulated based on the distance between the robot, target and the obstacles and is optimized using the proposed DA for obtaining optimal path. After every iteration, based on the objective function values the robot proceeds towards the globally best agent in the swarm in a sequence of permutation which finally leads to the target. A variety of static environment is modelled and the algorithm is tested both through simulation and experimentally. The proposed algorithm shows that the robot reaches the target without colliding any obstacles while generating a smooth optimal trajectory.

Application of Improved Moth-Flame Optimization Algorithm for Robot Path Planning

“…In order to verify the feasibility and effectiveness of the IMFO algorithm for mobile robot path planning, this paper compares it with the PSO algorithm, the MFO algorithm, and the DA algorithm in the literature [6] in three environments. The maximum number of iterations for the four algorithms is set to 300 and the population size is set to 30.…”

“…The reference [5] applied the quantum evolutionary algorithm (QEA) to robot path planning and compared it with the genetic algorithm (GA), and the simulation experiments verified the effectiveness of QEA. The reference [6] studied the robot path planning problem under static obstacle space based on the dragonfly algorithm and achieved better results. Although the swarm intelligence algorithm has a good performance, it has problems such as slow convergence speed and easy to fall into local optimal solutions.…”

Kinematic Model of a Logistic Train with a Double Ackermann Steering System

“…Ackermann steering system involves one or two steering wheels at the front. A tractor with a differential drive is similar to a wheeled mobile robot (WMR), where the two rear wheels are driven independently [14,16]. The front passive wheel simply adapts to the direction of movement.…”

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

“…On the other hand, neural network and deep learning-based methods have been proposed recently such as radial basis function neural network (RBFNN) applied for trajectory tracking of industrial Manutec-r15 robot [21], while grid-based search on randomized maps has been adopted in [22]. Recently, a number of hybrid and nature-inspired algorithms were suggested such as particle swarm optimization-modified frequency bat (PSO-MFB) algorithm for multi-target path planning [23], firefly algorithm for trajectory planning in highly uncertain environment [24], dragonfly algorithm [25], a hybrid beetle antennae search (BAS) and artificial potential field (APF) algorithm [26].…”