24,160 research outputs found

    Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

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    Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system

    Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle

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    NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems

    Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

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    A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

    Wavefront Propagation and Fuzzy Based Autonomous Navigation

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    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

    Single Input Fuzzy Logic Controller For Yaw Control Of Underwater Remotely Operated Crawler

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    Underwater Remotely Operated Crawler (ROC) is a class of the Unmanned Underwater Vehicle (UUV) that is tethered, unoccupied, manoeuvres on the seabed and remotely operated by a pilot from a platform. Underwater characteristic parameters such as added mass, buoyancy, hydrodynamic forces, underwater currents, including pressure could considerably affect and reduce the mobility of the ROC. The challenges faced by the ROCs are that the needs to reduce the overshoot in the system response, including, the time response and settling time. For yaw control (a motion around the z-axis), an occurrence of an overshoot in the system response is highly intolerable. Reducing the overshoot in the ROC trajectory is crucial since there are many challenging underwater natures and underwater vehicle control problems while studies on finding the solutions are still ongoing to find an improvement. Conventional Proportional-Integral-Derivative (PID) controller is not robust to be applied in the ROC due to the non-linear dynamic model of the ROC and underwater conditions. Besides that, by reducing the overshoot, the ROC mobility will be much more efficient and provided a reliable platform for underwater data mining. This study is focused to give an optimum performance of yaw control without overshoot in the system response and faster time response. This research begins by designing an underwater ROC as the researchā€™s platform. Then, the designed ROC is simulated by using SolidWorks software obtain the analysis of structural integrity and hydrodynamic properties. System identification technique is conducted to obtain the empirical modelling design of the fabricated ROC which equipped with Inertial Measurement Unit (IMU) sensor. The fuzzy logic controller (FLC) is designed based on 5 by 5 rule matrix which has to deal with fuzzification, rule base, inference mechanism and defuzzification operations. A simplification of the FLC is proposed and the method is called Single Input Fuzzy Logic Controller (SIFLC). The simplification is achieved by applying the ā€œsigned distance methodā€ where the SIFLC reduces the two-input FLC to a single input FLC. In other words, SIFLC is based on the signed distance method which eventually reduces the controller as single input-single output (SISO) controller. A PID controller is designed for the purpose of benchmarking with the FLC and SIFLC. SIFLC has the capability to adapt the non-linear underwater parameters (currents, waves and etc.). This research has discussed and compared the performance of PID, FLC and SIFLC. The algorithm is verified in MATLAB/Simulink software. Based on the results, SIFLC provides more robust and reliable control system. Based on the computation results, SIFLC reduces the percentage of overshoot (%OS) of the system and achieve 0.121%, while other controllers (PID and FLC) 4.4% and 1.7% respectively. Even that so, this does not mean that PID and FLC are not reliable but due to the presence of %OS
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