Oscar Montiel

Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.

Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms

A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.

An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications

A novel structure of type 2 fuzzy logic controller is presented. The method is highly efficient regarding computational time and implementation effort. Type-2 input membership functions were optimized using the Human Evolutionary Model (HEM) considering as the objective function the Integral of Squared Error at the controllers output. Statistical tests were achieved considering how the error at the controller’s output is diminished in presence of uncertainty, demonstrating that the proposed method outperforms an optimized traditional type-2 fuzzy controller for the same test conditions.

Embedding a high speed interval type-2 fuzzy controller for a real plant into an FPGA

The main goal of this paper is to show that interval type-2 fuzzy inference systems (IT2 FIS) can be used in applications that require high speed processing. This is an important issue since the use of IT2 FIS still being controversial for several reasons, one of the most important is related to the resulting shocking increase in computational complexity that type reducers, like the Karnik-Mendel (KM) iterative method, can cause even for small systems. Hence, comparing our results against a typical implementation of a IT2 FIS using a high level language implemented into a computer, we show that using a hardware implementation the the whole IT2 FIS (fuzzification, inference engine, type reducer and defuzzification) last only four clock cycles; a speed up of nearly 225,000 and 450,000 can be obtained for the Spartan 3 and Virtex 5 Field Programmable Gate Arrays (FPGAs), respectively. This proposal is suitable to be implemented in pipeline, so the complete IT2 process can be obtained in just one clock cycle with the consequently gain in speed of 900,000 and 2,400,000 for the aforementioned FPGAs. This paper also shows that the iterative KM method can be efficient if it is adequately implemented using the appropriate combination of hardware and software. Comparative experiments of control surfaces, and time response in the control of a real plant using the IT2 FIS implemented into a computer against the IT2 FIS into an FPGA are shown.

Performance of a Simple Tuned Fuzzy Controller and a PID Controller on a DC Motor

We are presenting the usefulness of an innovative method called simple tuning algorithm (STA) for tuning fuzzy controllers, it has only one variable to adjust to achieve the tuning goal, this in counterpart to other methods like the proportional integral derivative (PID) controller wish has three variables to adjust for the same goal. Comparative examples of the STA and the PID methods are presented in a speed control of a real DC gear motor application. The PID controller was tuned using the Ziegler-Nichols. In base of the obtained quantitative and qualitative measures and observations, we are concluding that the fuzzy controller performance outperformed the PID controller; moreover, the tuning process using the STA method was easier than using the Ziegler-Nichols method

Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles

The BPF proposal ensures a feasible, optimal and safe path for robot navigation.The results of BPF overcomes APF and other EAPF methods like those based in GAs.The BPF is quite faster in optimization leading to reduction in computation burden.The BPF running in parallel mode is the most suitable to fulfill local and global controllability.The BPF is capable to work in offline and online mode with static and dynamic obstacles. In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.

Simple tuned fuzzy controller embedded into an FPGA

It is presented a flexible architecture that allows to implement an embedded nonlinear fuzzy controller into an FPGA which can be easily tuned through the use of the Simple Tuning Algorithm (STA) without a controller reference model. The model was developed using VHDL programming, and it was tested in soft real time using Xilinx System Generator and Simulink before the final implementation into the FPGA. Experimental results show how the fuzzy controller works for different tuning values.

Modeling and Simulation of the Defuzzification Stage of a Type-2 Fuzzy Controller Using the Xilinx System Generator and Simulink

This paper is focused on the study, analysis and development of code for the defuzzification stage of type-2 fuzzy systems, through the average of two type-1 fuzzy systems. This proposal is based on the average method for systems where the type-2 membership functions of the inputs and output, have no uncertainty in the mean or center. The codification is done using the hardware description language VHDL, and it was exported to Simulink through the Xilinx System Generator (XSG). Comparative tests were conducted between the type-2 fuzzy systems for different number of bits and noise levels.

Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field

In this paper, we introduce the concept of Parallel Evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the Evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation; making the PEAPF suitable to be used in real-time applications.

The evolutionary learning rule for system identification

In this paper, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the well-known statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a breeder genetic algorithm (BGA) is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.