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Abstract

Potential field algorithm introduced by Khatib is well-known in path planning for robots. The algorithm is very simple yet provides real-time path planning and effective to avoid robot's collision with obstacles. The purpose of the paper is to implement and modify this algorithm for quadrotor path planning. The conventional potential method is firstly applied to introduce challenging problems, such as not reachable goals due to local minima solutions or nearby obstacles (GNRON). This will be solved later by proposed modified algorithms. The first proposed modification is by adding virtual force to the repulsive potential force to prevent local minima solutions. Meanwhile, the second one is to prevent GNRON issue by adding virtual force and considering quadrotor's distance to goal point on the repulsive potential force. The simulation result shows that the second modification is best applied to environment with GNRON issue whereas the first one is suitable only for environment with local minima traps. The first modification is able to reach goals in six random tests with local minima environment. Meanwhile, the second one is able to reach goals in six random tests with local minima environment, six random tests with GNRON environment, and six random tests with both local minima and GNRON environment.

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The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path.

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Proportional-Integral-Derivative (PID) control is one of the famous controllers applied in industrial and robotics world. It is quite easy to understand and to be implemented. It is known that, with a proper combination of its gain parameters, a system with good performance can be obtained. This parameter tuning can be tricky since the number of the combination is almost infinite. Hence, methods to tune the parameter gains of proportional, integral and derivative is needed. Some optimization methods that can be used in tuning parameters are Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The paper will analyse the performance of those methods by applying them in PID Controller to control a Direct Current (DC) motor system by comparing the iteration time, the number of parameters, and augmented system performance. The comparison is done on MATLAB simulation by using the same computer. It results as DE as the best among them with the least number of parameters and best DC motor system performances.

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