The authors would like to express their gratitude to Mr. Yaojie He for his assistance in performing the experiments reported in this paper. This work was supported in part by the National Natural Science Foundation of China (No. 61673117).

1. Construction of the Machine

<ol>
	<li>Assemble the chassis as illustrated, securing mechanical components using appropriate fasteners. (Figure 1) 
	NOTE: The chassis, which comprises the baseboard, motor, wheels, etc., is the primary component of the robot responsible for its motion. Thus, during assembly, keep the bracket straight.</li>
	<li>Tin the wire lead and both the positive and negative electrodes. Solder two wire leads onto the two ends of the motor, connecting the red lead to the positive electrode and the black lead to the negative electrode.</li>
	<li>Assemble the shaft sleeve, the motors and the wheels.
	<ol>
		<li>Connect the motor to the shaft sleeve and secure it with a screw.</li>
		<li>Insert the shaft sleeve into the center of the wheel hub.</li>
		<li>Install the completed structure onto the chassis.</li>
	</ol>
	</li>
	<li>Drill two holes, 3 mm in diameter, in the center of chassis, to allow for installation of the motor driving module. Connect the motor to the motor driving module.</li>
	<li>Drill one hole 1 cm from both the left and right edges of the chassis for installation of the bracket for the infrared sensors on the bottom.</li>
	<li>Install two fasteners onto the center of the two sides of the chassis. 
	NOTE: To ensure normal operation of the infrared sensors, ensure that the connecting piece is perpendicular to the chassis.</li>
	<li>Drill a hole, 18 mm in diameter, through each of the two structural components for the installation of sensors. (Figure 2A)</li>
	<li>Install the motor drive to the underside of chassis. (Figure 2B) Install one infrared sensor pointing at each of the four directions, respectively, of the chassis. (Figure 2C)</li>
	<li>Install the steering gear in symmetry. Because of the large torque generated by the operation of the steering gear, ensure that the bolts are installed in a manner that provides a firm and impervious joint.</li>
	<li>Install four infrared sensors on the center of the machine.</li>
	<li>Place the 14.8 V power supply in the center of the machine, and affix the Microcontroller Unit (MCU) to the battery pack.</li>
	<li>Affix four range sensors to the upper part of machine. Adjust the angle between each sensor and the ground to 60&#176;, to guarantee detection accuracy relative to the working table.</li>
	<li>Install the dual-axis tilt sensor, which is used to detect cases when the machine fails to reach its target in the working area.</li>
	<li>Use a screwdriver to attach the robot arm to the front of the machine. (Figure 3)</li>
</ol>
2. Debugging the Steering Engine and Driver Module
<ol>
	<li>Double-click to open the debugging software (e.g., Robot Servo Terminal2010). Connect computer to the debug board with a Universal Serial Bus (USB) converting cable. (Figure 4)</li>
	<li>Set the steering engine&#39;s baud rate to 9600 bits/s, the rate limitation to 521 rad/min, the angular limitation to 300&#176; and voltage limitation to 9.6 V in the working interface.</li>
	<li>Set the working mode of the robot steering gear to &#34;steering engine mode.&#34;</li>
	<li>Apply asynchronous half-duplex communication as the connection between the controller and steering engines. This way, the controller can control more than 255 steering engines from a single Universal Asynchronous Receiver/Transmitter (UART) interface. (Figure 5) 
	Caution: There may be, at most, 6 steering engines connected to a single wire. Too many steering engines will lead to overheating and large voltage drop, resulting in unusual behavior such as resetting and abnormal data communication, etc. (Figure 6)</li>
	<li>Apply asynchronous half-duplex communication as the connection between the controller and the motor driving module. (Figure 7)</li>
	<li>Set the ID number of the two driving modules and the four steering engines. ID3 and ID4 are left blank for future updating purposes. (Figure 8) 
	NOTE: ID1: leftward driving module; ID2: rightward driving module; ID5: left-front steering engine; ID6: right-front steering engine; ID7: left-rear steering engine; ID8: right-rear steering engine.</li>
	<li>Cascade the steering engines one by one and connect the cascade to the controller.</li>
	<li>Connect the sensors to their respective controller interfaces. It should be noted that the sensor whose connector bears a triangular mark is the ground (GND). 
	NOTE: AD1: front infrared photoelectric sensor on underside; AD2: right infrared photoelectric sensor on underside; AD3: rear infrared photoelectric sensor on underside; AD4: left infrared sensor on underside; AD5: front infrared distance measuring sensor; AD6: right infrared distance measuring sensor; AD7: rear infrared measuring sensor; AD8: left infrared distance measuring sensor; AD9: left-front anti-fall infrared photoelectric sensor; AD10: right-front anti-fall infrared photoelectric sensor; AD11: right-rear anti-fall infrared photoelectric sensor; AD12: left-rear anti-fall infrared photoelectric sensor.</li>
</ol>
3. Debugging the Sensors
<ol>
	<li>Rotate the regulating knob on the tail of the infrared sensors to adjust the detection range of the sensors. When the robot is positioned in the center of the working table, the logic level of the top four infrared sensors is 1. When the machine moves to the edge of the working table, the logic level of the infrared sensor on the corresponding side will be 0. (Figure 9A) 
	NOTE: The robot can determine its location in the working table by analyzing the logic level of the infrared sensors. For example, if the logic levels of the left and front sensors are 0, the robot must be in the upper-left region of the working table.</li>
	<li>Compare the measured values of the distance sensor to their baseline values for calibration. (Figure 9B) 
	NOTE: The distance sensor is an analog sensor. As the distance varies, the sensor&#39;s signal strength feedback and corresponding measured values will also vary. The measured values will be relayed to the host machine via digital sensors so that the robot can identify changes in its surroundings.</li>
	<li>Debug the tilt-angle sensor.
	<ol>
		<li>Position the tilt-angle sensor horizontally and record its measured values.</li>
		<li>Incline the sensor toward two different directions and record its measured values. If the measured values are within the error range, the sensor can be regarded as being in regular operation.</li>
	</ol>
	</li>
</ol>
4. Control Scheme
<ol>
	<li>Construct the simulation model of the DC motor, based on the DC motor voltage balance equation, flux linkage equation and torque balance equation.
	<ol>
		<li>Establish voltage balance equation given by 
		<img alt="Equation 1" src="/files/ftp_upload/56422/56422eq1.jpg" /> 
		where ud is direct axis voltage, uq is quadrature axis voltage. Rd and Rq denote direct axis resistance and quadrature axis resistance respectively. <img alt="Equation 2" src="/files/ftp_upload/56422/56422eq2.jpg" />,<img alt="Equation 3" src="/files/ftp_upload/56422/56422eq3.jpg" />,<img alt="Equation 4" src="/files/ftp_upload/56422/56422eq4.jpg" />,<img alt="Equation 5" src="/files/ftp_upload/56422/56422eq5.jpg" />, represent direct axis current, direct axis current, direct axis flux and quadrature axis flux.</li>
		<li>Establish flux linkage equation given by 
		<img alt="Equation 6" src="/files/ftp_upload/56422/56422eq6.jpg" /> 
		where <img alt="Equation 7" src="/files/ftp_upload/56422/56422eq7.jpg" /> and <img alt="Equation 8" src="/files/ftp_upload/56422/56422eq8.jpg" /> denote the coefficient of direct axis self-inductance and quadrature axis self-inductance respectively. <img alt="Equation 9" src="/files/ftp_upload/56422/56422eq9.jpg" />and <img alt="Equation 10" src="/files/ftp_upload/56422/56422eq10.jpg" /> are coefficient of mutual inductance. <img alt="Equation 11" src="/files/ftp_upload/56422/56422eq11.jpg" />, <img alt="Equation 12" src="/files/ftp_upload/56422/56422eq12.jpg" /> represent electromagnetic torque and load torque.</li>
		<li>Establish torque balance equation calculated by <img alt="Equation 13" src="/files/ftp_upload/56422/56422eq13.jpg" />.</li>
		<li>Build simulation model of the DC motor. (Figure 10)</li>
	</ol>
	</li>
	<li>Apply double closed-loop control of the DC motor. Utilize the output of the speed regulator as the input to the current regulator to regulate the motor&#39;s torque and current. 
	Note: Diagram of the structure of the current regulation system. (Figure 11) 
	The transfer function of the PI current regulator is shown as <img alt="Equation 14" src="/files/ftp_upload/56422/56422eq14.jpg" />, where <img alt="Equation 15" src="/files/ftp_upload/56422/56422eq15.jpg" />is the proportional coefficient of the current regulator and <img alt="Equation 16" src="/files/ftp_upload/56422/56422eq16.jpg" /> is the lead time constant of the current regulator. It can be obtained by the scale coefficient <img alt="Equation 17" src="/files/ftp_upload/56422/56422eq17.jpg" />, and the integral coefficient <img alt="Equation 18" src="/files/ftp_upload/56422/56422eq18.jpg" />.
	<ol>
		<li>Apply double closed-loop control of the DC motor. (Figure 12)</li>
	</ol>
	</li>
	<li>Apply four-quadrant motion control of the DC motor. (Figure 13)
	<ol>
		<li>Utilize an H-bridge driving circuit to achieve four-quadrant motion of the DC motor by modulating the on-off of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET). (Figure 14) 
		Note: When Q1 and Q4 are turned on and the motor is in the forward electric state and the first quadrant running state. When Q3 and Q4 are turned on, the motor is in the energy braking state and the second quadrant running state. When Q2 and Q3 are turned on, the motor is in the reverse electric state and the third quadrant motion state. When Q1 and Q2 are turned on, the motor is in the reverse energy braking state and the fourth quadrant motion state.</li>
	</ol>
	</li>
	<li>Apply pulse width modulation (PWM) to regulate the speed of the DC motor. Modulate the DC voltage pulse width (duty cycle) applied to the motor armature by controlling the on-off of the electric switch when the voltage of DC motor power supply remains essentially unchanged, thus modulating the average value and the rotation speed input to armature voltage of the motor.</li>
</ol>
5. Write the Program
<ol>
	<li>Use the USB download line to import a Binary (BIN) file generated by KEIL5 into the controller.</li>
	<li>Select the program to be executed.</li>
</ol>
6. Application Scenario
<ol>
	<li>Apply color recognition to categorize cargo in a factory. (Figure 15)
	<ol>
		<li>Use an optical camera to collect images and verify the scanned color using the number of the bounced two-dimensional array.</li>
		<li>Lift the object with the mechanical arms.</li>
		<li>Issue a command to transport the object to the designated location using the camera and driving motor of the robot.</li>
	</ol>
	</li>
	<li>Search quickly to clear the designated areas. (Figure 16)
	<ol>
		<li>Use the four optical sensors on the robot to detect the locations of surrounding obstacles.</li>
		<li>Command the steering engine to lift the mechanical shovel and clear obstacles in the designated areas.</li>
		<li>Use the genetic algorithm to determine the most effective search path.</li>
	</ol>
	</li>
	<li>Use self-recognition to prevent falling from the workbench to separate workers from the machine working area and ensure worker safety.
	<ol>
		<li>Modify signals based on the difference in altitude between the four upper optical sensors, which recognize the workbench and the ground.</li>
		<li>Analyze the mutable signals to determine the location of the edges of the workbench.</li>
		<li>Command the machine to avoid the edges of the workbench.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

In this paper, we designed a type of intelligent robot that can be built autonomously. We implemented the proposed intelligent search algorithm and autonomous recognition by integrating several software programs with hardware. In the protocol, we introduced basic approaches for configuring the hardware and debugging the intelligent robot, which may help users design a suitable mechanical structure of their own robot. However, during actual operation, it is necessary to pay attention to stability of the structure, its ope...

Robots are complex, intelligent machines that combine knowledge of several disciplines, including mechanics, electronics, control, computers, sensors and artificial intelligence 1,2. Increasingly, robots are assisting or even replacing humans in the workplace, especially in industrial production, due to the advantages robots possess in performing repetitive or dangerous tasks. The design of the intelligent robot protocol in the current study is based on a closed-loop control strategy, specifically path planning based on a genetic algorithm. Furthermore, the functional modules have been strictly divided3,4, which may provide a solid foundation for future optimization work, so that the robots have a strong capacity for upgrades.
The modular implementation of the robotic platform is based primarily on the following methods: multi-dimensional combination control strategy in motor control module5,6, and intelligent exploration based on a genetic algorithm in the optimization algorithm module.
We use double closed-loop control of the DC motor and four-quadrant motor operation in the motor control module. Double closed-loop speed control means that the output of the speed regulator serves as the input of the current regulator, allowing it to control the current and torque of the motor. The advantage of this system is that the torque of the motor can be controlled in real-time based on the difference between the given speed and the actual speed. When the difference between given and actual speeds is relatively large, the motor torque increases and the speed changes faster to drive the motor speed toward the given value as quickly as possible, which makes for rapid speed regulation7,8,9. Conversely, when the speed is relatively close to the given value, it can automatically reduce the torque of the motor to avoid excessive speed, allowing the speed to achieve the given value relatively quickly with no error6,10. Since the equivalent time constant of the electric current loop is relatively small, the four-quadrant motor11,12 can respond more quickly to suppress the impact of interference when the system is subject to external interference. This allows it to improve the stability and anti-jamming ability of the system.
We choose a genetic intelligent optimization algorithm with the highest efficiency based on the results of a simulation run in MATLAB. A genetic algorithm is a stochastic parallel search algorithm based on the theory of natural selection in genetics. It constitutes an efficient method for finding the global optimal solution in the absence of any initial information. It regards the solution set of the problem as a population, thereby increasing the quality of the solution via continuous selection, crossover, mutation and other genetic operations. With regard to path planning by intelligent robots, difficulty arises as a result of insufficient initial information, complicated environments and nonlinearity. Genetic algorithms are better able to solve the problem of path planning because they possess global optimization ability, strong adaptability and robustness in solving nonlinear problems; there are no specific restrictions on the problem; the calculation process is simple; and there are no special requirements for the search space 13,14.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L1-1</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L2-1</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L3-1</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L4-1</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L5-1</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>L5-2</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3A</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3B</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3C</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3F</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3G</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3H</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>U3J</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>I3</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>I5</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>I7</td>
			<td></td>
		</tr>
		<tr>
			<td>structural parts</td>
			<td>UPTECMONYH HAR</td>
			<td>CGJ</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LM1</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LM2</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LM3</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LM4</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LX1</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LX2</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LX3</td>
			<td></td>
		</tr>
		<tr>
			<td>link component</td>
			<td>UPTECMONYH HAR</td>
			<td>LX4</td>
			<td></td>
		</tr>
		<tr>
			<td>Steering gear structure component</td>
			<td>UPTECMONYH HAR</td>
			<td>KD</td>
			<td></td>
		</tr>
		<tr>
			<td>Steering gear structure component</td>
			<td>UPTECMONYH HAR</td>
			<td>DP</td>
			<td></td>
		</tr>
		<tr>
			<td>Infrared sensor</td>
			<td>UPTECMONYH HAR</td>
			<td>E18-B0</td>
			<td>Digital sensor</td>
		</tr>
		<tr>
			<td>Infrared&#160;Range&#160;Finder</td>
			<td>SHARP</td>
			<td>GP2D12</td>
			<td></td>
		</tr>
		<tr>
			<td>Gray level sensor</td>
			<td>SHARP</td>
			<td>GP2Y0A02YK0F</td>
			<td></td>
		</tr>
		<tr>
			<td>proMOTION&#160;CDS</td>
			<td>SHARP</td>
			<td>CDS&#160;5516</td>
			<td>The robot steering gear</td>
		</tr>
		<tr>
			<td>motor drive module</td>
			<td>Risym</td>
			<td>HG7881</td>
			<td></td>
		</tr>
		<tr>
			<td>solder wire</td>
			<td>ELECALL</td>
			<td>63A</td>
			<td></td>
		</tr>
		<tr>
			<td>terminal</td>
			<td>Bright wire</td>
			<td>5264</td>
			<td></td>
		</tr>
		<tr>
			<td>motor</td>
			<td>BX motor</td>
			<td>60JX</td>
			<td></td>
		</tr>
		<tr>
			<td>camera</td>
			<td>Logitech</td>
			<td>C270</td>
			<td></td>
		</tr>
		<tr>
			<td>Drilling machine</td>
			<td>XIN XIANG</td>
			<td>16MM</td>
			<td>Please be careful</td>
		</tr>
		<tr>
			<td>Soldering station</td>
			<td>YIHUA</td>
			<td>8786D</td>
			<td>Be&#160;careful&#160;to&#160;be&#160;burn</td>
		</tr>
		<tr>
			<td>screwdriver</td>
			<td>EXPLOIT</td>
			<td>043003</td>
			<td></td>
		</tr>
		<tr>
			<td>Tweezers</td>
			<td>R`DEER</td>
			<td>RST-12</td>
			<td></td>
		</tr>
	</tbody>
</table>

the modular design and production of an intelligent robot based on a closed-loop control strategy

Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach their targets. In recent years, reliance upon robots in both daily life and industry has increased. The protocol proposed in this paper describes the design and production of a handling robot with an intelligent search algorithm and an autonomous identification function.
First, the various working modules are mechanically assembled to complete the construction of the work platform and the installation of the robotic manipulator. Then, we design a closed-loop control system and a four-quadrant motor control strategy, with the aid of debugging software, as well as set steering gear identity (ID), baud rate and other working parameters to ensure that the robot achieves the desired dynamic performance and low energy consumption. Next, we debug the sensor to achieve multi-sensor fusion to accurately acquire environmental information. Finally, we implement the relevant algorithm, which can recognize the success of the robot&#39;s function for a given application.
The advantage of this approach is its reliability and flexibility, as the users can develop a variety of hardware construction programs and utilize the comprehensive debugger to implement an intelligent control strategy. This allows users to set personalized requirements based on their needs with high efficiency and robustness.

In the diagram of the double closed-loop motion control program, purple represents a given speed signal and yellow represents the value of the control system output. Figure 17 clearly shows that the double closed-loop control system is significantly more effective than an open-loop system. The actual overshoot of the output of the double closed-loop system is relatively small and the dynamic performance of the system is better. ( Figure 1...

Watch this Scientific Journal Video about The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy at JoVE.com

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.

Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach ...

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11.5K Views.  Chinese Academy of Sciences. We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.

Video: The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.

We use a closed-loop fly-machine interface to investigate general principles in neuronal control.

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine ...

Neuroscience

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

In recent years, soft robotics technologies have aroused increasing interest in the medical field due to their intrinsically safe interaction in unstructured environments. At the same time, new procedures and techniques have been developed to reduce the invasiveness of surgical operations. Minimally Invasive Surgery (MIS) has been successfully employed for abdominal interventions, however standard MIS procedures are mainly based on rigid or semi-rigid tools that limit the dexterity of the clinician. This paper presents a soft and high dexterous manipulator for MIS. The manipulator was inspired by the biological capabilities of the octopus arm, and is designed with a modular approach. Each module presents the same functional characteristics, thus achieving high dexterity and versatility when more modules are integrated. The paper details the design, fabrication process and the materials necessary for the development of a single unit, which is fabricated by casting silicone inside specific molds. The result consists in an elastomeric cylinder including three flexible pneumatic actuators that enable elongation and omni-directional bending of the unit. An external braided sheath improves the motion of the module. In the center of each module a granular jamming-based mechanism varies the stiffness of the structure during the tasks. Tests demonstrate that the module is able to bend up to 120&#176; and to elongate up to 66% of the initial length. The module generates a maximum force of 47 N, and its stiffness can increase up to 36%.

This paper describes the design and fabrication of a soft unit for surgical manipulators. The base module includes three flexible fluidic actuators to achieve omnidirectional bending and elongation, and a granular jamming-based mechanism to enable stiffness control. A complete mechanical characterization is also reported.

In recent years, soft robotics technologies have aroused increasing interest in the medical field due to their intrinsically safe interaction in ...

Bioengineering

Designing and Implementing Nervous System Simulations on LEGO Robots

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step of the method is to develop a nervous system simulation of specific reflexive behaviors of an appropriate model organism; here we use the American Lobster. Exteroceptive reflexes mediated by decussating (crossing) neural connections can explain an animal's taxis towards or away from a stimulus as described by Braitenberg and are particularly well suited for investigation using the NXT platform.1 The nervous system simulation is programmed using LabVIEW software on the LEGO Mindstorms platform. Once the nervous system is tuned properly, behavioral experiments are run on the robot and on the animal under identical environmental conditions. By controlling the sensory milieu experienced by the specimens, differences in behavioral outputs can be observed. These differences may point to specific deficiencies in the nervous system model and serve to inform the iteration of the model for the particular behavior under study. This method allows for the experimental manipulation of electronic nervous systems and serves as a way to explore neuroscience hypotheses specifically regarding the neurophysiological basis of simple innate reflexive behaviors. The LEGO Mindstorms NXT kit provides an affordable and efficient platform on which to test preliminary biomimetic robot control schemes. The approach is also well suited for the high school classroom to serve as the foundation for a hands-on inquiry-based biorobotics curriculum.

An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step ...

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, irrigation, and mechanical weed control. Extending this research, we introduce here a novel methodology to manipulate the directional growth of plants via their natural mechanisms for signaling and hormone distribution. An effective methodology of robotic stimuli provision can open up possibilities for new experimentation with later developmental phases in plants, or for new biotechnology applications such as shaping plants for green walls. Interaction with plants presents several robotic challenges, including short-range sensing of small and variable plant organs, and the controlled actuation of plant responses that are impacted by the environment in addition to the provided stimuli. In order to steer plant growth, we develop a group of immobile robots with sensors to detect the proximity of growing tips, and with diodes to provide light stimuli that actuate phototropism. The robots are tested with the climbing common bean, Phaseolus vulgaris, in experiments having durations up to five weeks in a controlled environment. With robots sequentially emitting blue light-peak emission at wavelength 465 nm-plant growth is successfully steered through successive binary decisions along mechanical supports to reach target positions. Growth patterns are tested in a setup up to 180 cm in height, with plant stems grown up to roughly 250 cm in cumulative length over a period of approximately seven weeks. The robots coordinate themselves and operate fully autonomously. They detect approaching plant tips by infrared proximity sensors and communicate via radio to switch between blue light stimuli and dormant status, as required. Overall, the obtained results support the effectiveness of combining robot and plant experiment methodologies, for the study of potentially complex interactions between natural and engineered autonomous systems.

Distributed robot nodes provide sequences of blue light stimuli to steer the growth trajectories of climbing plants. By activating natural phototropism, the robots guide the plants through binary left-right decisions, growing them into predefined patterns that by contrast are not possible when the robots are dormant.

Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, ...

Operation of the Collaborative Composite Manufacturing (CCM) System

The automated tape placement and the automated fiber placement (AFP) machines provide a safer working environment and reduce the labor intensity of workers than the traditional manual fiber placement does. Thus, the production accuracy, repeatability and efficiency of composite manufacturing are significantly improved. However, the current AFP systems can only produce the composite components with large open surface or simple revolution parts, which cannot meet the growing interest in small complex or closed structures from industry.
In this research, by employing a 1-degree of freedom (DoF) rotational stage, a 6-RSS parallel robot, and a 6-DoF serial robot, the dexterity of the AFP system can be significantly improved for manufacturing complex composite parts. The rotational stage mounted on the parallel robot is utilized to hold the mandrel and the serial robot carries the placement head to mimic two human hands that have enough dexterity to lay the fiber to the mandrel with complex contour.
Although the CCM system increases the flexibility of composite manufacturing, it is quite time-consuming or even impossible to generate the feasible off-line path, which ensures uniform lay-up of subsequent fibers considering the constraints like singularities, collisions between the fiber placement head and mandrel, smooth fiber direction change and keeping the fiber placement head along the norm of the part&#39;s surface, etc. Moreover, due to the existing positioning error of the robots, the on-line path correction is needed. Therefore, the on-line pose correction algorithm is proposed to correct the paths of both parallel and serial robots, and to keep the relative path between the two robots unchanged through the visual feedback when the constraint or singularity problems in the off-line path planning occur. The experimental results demonstrate the designed CCM system can fulfill the movement needed for manufacturing a composite structure with Y-shape.

Operation of the Collaborative Composite Manufacturing CCM System

A collaborative composite manufacturing system is developed for robotic lay-up of composite laminates using the prepreg tape. The proposed system allows the production of composite laminates with high levels of geometrical complexity. The issues in the path planning, coordination of the robots and control are addressed in the proposed method.

The automated tape placement and the automated fiber placement (AFP) machines provide a safer working environment and reduce the labor intensity of ...

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84&#176;. The manufacturing method is valid for the fast pneunet bending actuators in general and might, therefore, be interesting for newcomers to the field of actuator manufacturing. The control of the robot is achieved by means of a pneumatic control box that can provide arbitrary pressures and can be built by only using purchased components, a laser cutter, and a soldering iron. For the walking performance of the robot, the pressure-angle calibration plays a crucial role. Therefore, a semi-automated method for the pressure-angle calibration is presented. At high inclines (&#62; 70&#176;), the robot can no longer reliably fix itself to the walking plane. Therefore, the gait pattern is modified to ensure that the feet can be fixed on the walking plane.

This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with ...

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It is conducted by eliciting an illusion through use of a mirror as if the hemiplegic arm is moving in real-time while moving the healthy arm. It can facilitate brain neuroplasticity through activation of the sensorimotor cortex. However, conventional mirror therapy has a critical limitation in that the hemiplegic arm is not actually moving. Thus, we developed a real-time 2-axis mirror robot system as a simple add-on module for conventional mirror therapy using a closed feedback mechanism, which enables real-time movement of the hemiplegic arm. We used 3 Attitude and Heading Reference System sensors, 2 brushless DC motors for elbow and wrist joints, and exoskeletal frames. In a feasibility study on 6 healthy subjects, robotic mirror therapy was safe and feasible. We further selected tasks useful for activities of daily living training through feedback from rehabilitation doctors. A chronic stroke patient showed improvement in the Fugl-Meyer assessment scale and elbow flexor spasticity after a 2-week application of the mirror robot system. Robotic mirror therapy may enhance proprioceptive input to the sensory cortex, which is considered to be important in neuroplasticity and functional recovery of hemiplegic arms. The mirror robot system presented herein can be easily developed and utilized effectively to advance occupational therapy.

We developed a real-time mirror robot system for functional recovery of hemiplegic arms using automatic control technology, conducted a clinical study on healthy subjects, and determined tasks through feedback from rehabilitation doctors. This simple mirror robot can be applied effectively to occupational therapy in stroke patients with a hemiplegic arm.

Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It is ...

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Experimentation is crucial in engineering education. This work explores visualized experiments in online laboratories for teaching and learning and also research. Interactive and visualizing features, including theory-guided algorithm implementation, web-based algorithm design, customizable monitoring interface, and three-dimensional (3-D) virtual test rigs are discussed. To illustrate the features and functionalities of the proposed laboratories, three examples, including the first-order system exploration using a circuit-based system with electrical elements, web-based control algorithm design for virtual and remote experimentation, are provided. Using user-designed control algorithms, not only can simulations be conducted, but real-time experiments can also be conducted once the designed control algorithms have been compiled into executable control algorithms. The proposed online laboratory also provides a customizable monitoring interface, with which users can customize their user interface using provided widgets such as the textbox, chart, 3-D, and camera widget. Teachers can use the system for online demonstration in the classroom, students for after-class experimentation, and researchers to verify control strategies.

This work describes an online experimentation system that provides visualized experiments, including the visualization of theories, concepts, and formulas, visualizing the experimental process with three-dimensional (3-D) virtual test rigs, and visualizing the control and monitoring system using widgets such as charts and cameras.

Experimentation is crucial in engineering education. This work explores visualized experiments in online laboratories for teaching and learning and also ...

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks.

In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.

Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation ...

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...