Implementation of Takagi Sugeno method as fuzzy logic control with multiple sensors on fire fighter robot prototype navigation

Autonomous mobile robot will become an interest by many researchers. Due to needs of an effective navigational system for the robot, many approach was purposed to control the reliability of the system in order to achieve an optimum control system respond. Nowadays, AI was used as one of the approached to enhance the decision making by the robot itself without depending on the user. In this development, multiple sensors were implemented to increase the detection accuracy of the prototype during navigate on constantly changing environment. By using multiple sensors (ultrasonic sensor) it means that, it will have more input information so, in order to manage all of this data information, intelligent control strategy was required to achieve an optimum output in term of efficiency and accuracy. On this development, previous prototype was upgraded into more intelligent system. Fuzzy Logic was introduced to this development by using a Takagi Sugeno inference method. In this project Fuzzy control was designed with aid of computing software (MATLAB) and by using a Arduino MEGA controller Fuzzy logic control was apply to realize the real-time simulation for the robot navigation. The fire detection unit by using IR sensor module was purposed to make sure it can scan the IR wave generated by the flame. Differential steering method was selected for semi-autonomous robot driving system and it was powered by 2 DC motor for the robot navigation. All the function of the robot is tested in order to evaluate the capability of the system on the robot by referred to the project scope. On this development, the effectiveness of the system was vary with the number of Fuzzy Rule. The effectiveness of the Fuzzy control was observed by the time taken for the prototype completed the route and the time respond for the prototype to avoid obstacle during the navigation inside the test rig

Simulation and Design of an Intelligent Mobile Robot for Fire Fighting

The application of traditional frangible glass panel and automatic system such as smoke detector or heat detector requires a human response to realise the existence of fire and to perceive and determine its severity. However, fire-fighting system such as sprinkler causes the damage of the property and also injury and panic due to the water sprayed out when the system trigged. Thus to avoid such incident, a mobile robot with fire detection capability and fire fighting system to perform fire-fighting purpose is a new technology to reduce subsequent damage and secure life before the fire engine to attend. A navigation system base on fuzzy logic controller (FLC) is developed for the mobile robot in an ambiguous situation for fire fighting purpose. A method of Path Recognition Algorithms (PRA) providing robots the autonomous ability to judge purpose of action likes human base on the input sensors from the environment. Multiple fuzzy behaviours by fuzzy logic method have been developed to allow the robot over come some of the possible obstacles and resistances for the robot to navigate in unknown environment. Behaviours have been integrated with arbitration strategy to determine the appropriate behaviours by priority method with preset data. An ultrasonic sensor and an infrared thermal sensor were mounted on a 360 degree rotated stepper motor to scan distance between the robot and its immediate obstacles and fire source around its environment. A fuzzy base computer animation in virtual reality is developed to simulate a simple in-door environment for fire fighting purpose and a systematic implementation of real-time simulation of the mobile robot is presented. However traveling in such region to the target location, there exist some unknown obstacles for the mobile robot especially in real-world environment with unknown map and unpredictable obstacle location, thus the control algorithms must be able to promptly react upon the unpredictable. The simulation result of this study indicated that application of FLC in mobile robot could be a suitable system for fire detection and fire fighting task in an unknown environment. More comprehensive study in behaviour coordination will be the major focus to ensure smoother robot navigation and more effective fire detection capability. Keywords: (Fuzzy logic control, mobile robot, part recognition algorithms

Neuro-fuzzy techniques to optimize an FPGA embedded controller for robot navigation

This paper describes how low-cost embedded controllers for robot navigation can be obtained by using a small number of if-then rules (exploiting the connection in cascade of rule bases) that apply Takagi-Sugeno fuzzy inference method and employ fuzzy sets represented by normalized triangular functions. The rules comprise heuristic and fuzzy knowledge together with numerical data obtained from a geometric analysis of the control problem that considers the kinematic and dynamic constraints of the robot. Numerical data allow tuning the fuzzy symbols used in the rules to optimize the controller performance. From the implementation point of view, very few computational and memory resources are required: standard logical, addition, and multiplication operations and a few data that can be represented by integer values. This is illustrated with the design of a controller for the safe navigation of an autonomous car-like robot among possible obstacles toward a goal configuration. Implementation results of an FPGA embedded system based on a general-purpose soft processor confirm that percentage reduction in clock cycles is drastic thanks to applying the proposed neuro-fuzzy techniques. Simulation and experimental results obtained with the robot confirm the efficiency of the controller designed. Design methodology has been supported by the CAD tools of the environment Xfuzzy 3 and by the Embedded System Tools from Xilinx. © 2014 Elsevier B.V.Peer Reviewe

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

BEHAVIOR BASED CONTROL AND FUZZY Q-LEARNING FOR AUTONOMOUS FIVE LEGS ROBOT NAVIGATION

This paper presents collaboration of behavior based control and fuzzy Q-learning for five legs robot navigation systems. There are many fuzzy Q-learning algorithms that have been proposed to yield individual behavior like obstacle avoidance, find target and so on. However, for complicated tasks, it is needed to combine all behaviors in one control schema using behavior based control. Based this fact, this paper proposes a control schema that incorporate fuzzy q-learning in behavior based schema to overcome complicated tasks in navigation systems of autonomous five legs robot. In the proposed schema, there are two behaviors which is learned by fuzzy q-learning. Other behaviors is constructed in design step. All behaviors are coordinated by hierarchical hybrid coordination node. Simulation results demonstrate that the robot with proposed schema is able to learn the right policy, to avoid obstacle and to find the target. However, Fuzzy q-learning failed to give right policy for the robot to avoid collision in the corner location. Keywords : behavior based control, fuzzy q-learnin

Wavefront Propagation and Fuzzy Based Autonomous Navigation

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments

Supervised Control of a Flying Performing Robot using its Intrinsic Sound

We present the current results of our ongoing research in achieving efficient control of a flying robot for a wide variety of possible applications. A lightweight small indoor helicopter has been equipped with an embedded system and relatively simple sensors to achieve autonomous stable flight. The controllers have been tuned using genetic algorithms to further enhance flight stability. A number of additional sensors would need to be attached to the helicopter to enable it to sense more of its environment such as its current location or the location of obstacles like the walls of the room it is flying in. The lightweight nature of the helicopter very much restricts the amount of sensors that can be attached to it. We propose utilising the intrinsic sound signatures of the helicopter to locate it and to extract features about its current state, using another supervising robot. The analysis of this information is then sent back to the helicopter using an uplink to enable the helicopter to further stabilise its flight and correct its position and flight path without the need for additional sensors

A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments

Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies