A one decade survey of autonomous mobile robot systems

The goal of the navigation process is to find the optimal path for the mobile robot and control its motion on that path without any oscillation. This work aims to find the optimal paths for multi-mobile robots working in the same static environment. To achieve this goal, the proposed quarter orbits particle swarm optimization (QOPSO) algorithm, which is an enhancement of the cell decomposition algorithm, will be used. The main advantage of using the QOPSO algorithm is to generate the shortest path and avoid collision with static obstacles. Moreover, to direct the motion of the three mobile robots on the desired predefined paths, a proposed inverse differential kinematic neural network trajectory tracking (IDKNNTT) controller based on a modified Elman recurrent neural network (MERNN) will be used. This proposed controller is used to control the nonlinear kinematics mobile robots' system to smoothly and quickly generate the left and right wheels' velocities of the multi mobile robots, which are used to control the orientation and position of each mobile robot. Furthermore, using the proposed controller minimizes the tracking error in the X-axis and the Y-axis positions, approximately zeroes the orientation error, and provides no oscillation in the responses. In particular, the controller guarantees that all the mobile robots will follow their desired paths quickly and correctly. Finally, we validate the numerical simulation results of the proposed control strategy by comparing them to those of other types of controllers in terms of the maximum error enhancement in the X-position and the Y-position. Particularly, when the proposed controller was compared to the convolutional neural network trajectory tracking (CNNTT) controller, the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 75.5 % and enhances the tracking error rate on the Y-axis by 21.2 %. In addition, the proposed controller was compared to the MIMO-PID-MENN controller, and the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 33.3 % and on the Y-axis by 40.6 %.

“…The structure of the MERNN consists of four layers: The 𝑒𝜃 𝑀𝑅 (𝑘) of the three mobile robots can be calculated using Eq. 6, as follows [2]:…”

Section: Control Strategy Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Path Planning Algorithm and Control Strategy Design for MultiMobile Robots based on a Modified Elman Recurrent Neural Network

2022

“…Hence, path planning is one of the most important techniques for mobile robot autonomy. In particular, path planning's major goal is to create an ideal and viable path for a mobile robot to follow in order to get to the target places as rapidly as possible, utilizing optimal trajectories [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.

“…Generally, algorithms of motion planning determine a valid configuration to create a collision-free path for the mobile robot. This becomes difficult as the extent of the configuration space increases [3]. The increase in applications related to intelligent mobile robots reflects the importance of path planning in many fields of robotics.…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

2022

During the previous years, the number of researchers who discussed the field of path planning algorithms has increased. In this paper, a proposed hybrid technique for path planning based on the benefits of sampling-based algorithms and heuristic path planning algorithms was enhanced. Rapidly-exploring Random Tree star (RRT*) was used to generate the candidate nodes that represent a collision-free path. These candidate nodes were applied to the Particle Swarm Optimization (PSO) algorithm to generate the shortest and the smoothest path planning (the optimal path planning) for the mobile robot. The RRT*PSO algorithm was applied in two scenarios (static and dynamic environments) with a workspace of [500×500] cm using MATLAB 2021a. In the static environment, a workspace full of obstacles was chosen to find the shortest collision-free path. In addition, a reference path equation was found to calculate the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels. In this work, the suggested hybrid RRT*PSO algorithm improves path length 34.27% compared to the A* algorithm and the fuzzy analytic hierarchy process (A*-FAHP hybrid) algorithm and 0.35% compared to the Self-adaptive evolutionary game-based particle swarm optimization (SAEGBPSO) algorithm. In the dynamic environment, an algorithm named (contour path down and contour path up) was suggested to avoid collisions with dynamic obstacles. By using this suggested algorithm, the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels were calculated to obtain a smooth path followed and free-navigation with dynamic obstacles in an environment.