A review: On path planning strategies for navigation of mobile robot

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

As path planning is an NP-hard problem it can be solved by multi-objective algorithms. In this article, we propose a multi-objective path planning algorithm which consists of three steps: (1) the first step consists of optimizing a path by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm, it minimizes the path distance and smooths the path. (2) the second step, all optimal and feasible points generated by PSO–GWO algorithm are integrated with Local Search technique to convert any infeasible point into feasible point solution, the last step (3) depends on collision avoidance and detection algorithm, where mobile robot detects the presence of an obstacle in its sensing circle and then avoid them using collision avoidance algorithm. The proposed method is further improved by adding the mutation operators by evolutionary, it further solves path safety, length, and smooths it further for a mobile robot. Different simulations have been performed under numerous environments to test the feasibility of the proposed algorithm and it is shown the algorithm produces a more feasible path with a short distance and thus proves that it overcomes the shortcomings of other conventional techniques.

Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed path planning algorithm consists of three modules: in the first module, the path planning algorithm forms an optimised path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimises distance and follows path smoothness criteria; in the second module, any infeasible points generated by the proposed PSO-MFB Algorithm are detected by a novel Local Search (LS) algorithm and integrated with the PSO-MFB algorithm to be converted into feasible solutions; the third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. Simulations have been carried out that indicated that this method generates a feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Comparisons with previous examples in the literature are also included in the results.

Multi-Objective Path Planning of an Autonomous Mobile Robot in Static and Dynamic Environments using a Hybrid PSO-MFB Optimisation Algorithm.

Nonlinear PID Controller Design for a 6-DOF UAV Quadrotor System

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Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization

This paper proposes a new method to tune a fractional-order Proportional–Integral–Derivative controller (also called \(\mathrm {P}\mathrm {I}^{\uplambda }\mathrm {D}^{\upmu }\) ) for a Twin Rotor Aerodynamic System, which is a nonlinear highly coupled Multi-Input–Multi-Output system. The five parameters of the controller are tuned using optimization to minimize a performance index which is a weighted sum of absolute values of four frequency domain specifications: gain crossover frequency, phase margin, ISO damping property (to be robust against process gain variation), and magnitude peak value at the resonant frequency, where the latter is the new control design specification that is suggested by this paper. The performance of the proposed controller is compared with that of an integer-order Proportional–Integral–Derivative (PID) controller. Simulation results shows that the fractional-order controller outperforms its integer-order counterpart in minimizing the performance index, which results in satisfying the required design specification more accurately. This is demonstrated by first testing the performance of the of the closed-loop system where the fractional-order controller gives better performance than the integer-order controller and second by testing the robustness of the system by changing one of the process parameters, where the fractional-order controller is much more robust, unlike the integer-order controller where the closed-loop system becomes unstable.

A New Method to Tune a Fractional-Order PID Controller for a Twin Rotor Aerodynamic System

paper describes the problem of offline autonomous mobile robot path planning, which is consist of generating optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An improved algorithm for solving the problem of path planning using Artificial Bee Colony algorithm is presented. This nature- inspired metaheuristic algorithm, which imitates the foraging behavior of bees around their hive, is used to find the optimal path from a starting point to a target point. The proposed algorithm is demonstrated by simulations in three different environments. A comparative study is evaluated between the developed algorithm, the original ABC and other two state-of- the-art algorithms. This study shows that the proposed method is effective and gets trajectories with satisfactory results.

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Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

يقدم هذا البحث , وصفا لمشكلة تخطيط مسارالروبوتات المتنقلة في الزمن الحقيقي والمتمثلة بايجاد مسار امثل للروبوت من نقطة بداية والى نقطة نهاية على خريطة مسطحة بشكل مساحة ثنائية الابعاد. قدم خوارزمية محسنة لطريقة النهم للبكتريا لحل مسألت تخطيط المسار. ان هذه الخوارزمية المستوحاة من الطبيعة والتي تقلد عملية بحث عن المؤن لبكتريا مسماة بال ( أي كولاي) استخدمت لايجاد مسار من نقطة بداية الى نقطة نهاية. الخوارزمية المقترحة تم عرضها واثباتها عن طريق محاكاة لنوعين من البيئات المختلفة الثابتة والمتحركة. اجريت مقارنة بين الطريقة المطورة و طريقتين اخريين حديثة. هذا البحث اظهر فاعلية هذه الطريقة المقترحة في ايجاد مسارات بشكل مرض.

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Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

In this paper, a new structure of a Fractional Order State Feedback Controller (FOSFC) is designed to solve the problem of trajectory tracking of a Differential Drive Robot (DDR), which is a nonlinear Multi-Input Multi-Output (MIMO) system. One of the main features of this paper is that it considers both the kinematic and dynamic models of the robot. For comparison purposes, a conventional Integer Order State Feedback Controller (IOSFC) is designed for the same system. The parameters of both controllers are tuned (using the fmincon Matlab function) to minimize the Integral of Time multiplied by Squared Error (ITSE) performance index when the DDR tracks a circular reference trajectory. The FOSFC gives a better value for this performance index than that of the IOSFC, which means that the FOSFC enhances the performance of the system with respect to this performance index. While the stability of the closed loop system is verified using Lyapunovs direct method for the IOSFC, a new approach is presented in this paper to analyze the stability of the closed loop system for the designed FOSFC. Simulation results demonstrate that the FOSFC outperforms the IOSFC for two other performance indices, namely, the Integral of Absolute Error (IAE) and the Integral of Squared Error (ISE).

Nizar Hadi Abbas

Papers

Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization

A New Method to Tune a Fractional-Order PID Controller for a Twin Rotor Aerodynamic System

Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

Design and Stability Analysis of a Fractional Order State Feedback Controller for Trajectory Tracking of a Differential Drive Robot