Typical obstacles are walls, rocks, water plants and other nearby robots for a group of small scale fish robots and submersibles that have been constructed in our lab. Sonar sensors are not employed to make the robot structure simple enough. All of circuits, sensors and processor cards are contained in a box of 9 x 7 x 4 cm dimension except motors, fins and external covers. Therefore, image processing results are applied to avoid collisions. However, it is useful only when the obstacles are located far enough to give images processing time for detecting them. Otherwise, acceleration sensors are used to detect collision immediately after it happens. Two of 2-axes acceleration sensors are employed to measure the three components of collision angles, collision magnitudes, and the angles of robot propulsion. These data are integrated to calculate the amount of propulsion direction change. The angle of a collision incident upon an obstacle is the fundamental value to obtain a direction change needed to design a following path. But there is a significant amount of noise due to a caudal fin motor. Because caudal fin provides the main propulsion for a fish robot, there is a periodic swinging noise at the head of a robot. This noise provides a random acceleration effect on the measured acceleration data at the collision. We propose an algorithm which shows that the MEMS-type accelerometers are very effective to provide information for direction changes in spite of the intrinsic noise after the small scale fish robots have made obstacle collision.
For the satisfying performance of a control system, design of a controller for the system which meets the required specifications, and the role of its supporting hardware that keeps functioning are equally important. Therefore it is inevitable to keep track of accurate and reliable sensor readings for good controller performance. Among the hardware of a control system sensors are most vulnerable to malfunction. Thus it is necessary to provide physical and analytical redundancy for the sensor measurement outputs to increase reliability against sensor failures. In general the task of detection, identification, and accommodation of sensor failures is widely pursued for this need. In case of sensor faults, they are detected by examining the sensor output values and the correlated or relevant values of the system. And then the types of the faults are identified by the analysis of symptoms of faults. If necessary self-validating sensor values are synthesized according to the types of faults, and then they are used for the controller instead of the raw data. In this paper, a self-validating sensor is applied to the control of a flexible link system with the sensor fault problems in the light sensor module for exact positioning to show the applicability. It is shown that the digital controller can provide a satisfactory loop performance even when the sensor faults occur.
Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.
Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But the main purposes of the noncontact sensors are rather narrowly confined within object detection and distance measurement. For the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. To resolve these problems in object recognition, an array of the sensor has been used. To improve the spatial resolution, more number of sensors are used in essence throughout the various devices of the sensor arrays. Under the disguise of a fixed number of the sensors, the array can be shifted mechanically in several steps. In this paper we propose a practical sensor resolution enhancement method using an electronic circuit accompanying the sensor array. The circuit changes the transmitter output voltage in several steps. Using the known sensor characteristics, a set of different return echo signals provide enhanced spatial resolution. The improvement is obtained with neither the cost of the increased number of the sensors nor extra mechanical devices.
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