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Assuming you have modified the power supply, it is simply a case of connecting a long tether between the power supply and the screw terminals on the snake. Snake robots have been created with traditional rigid structures. Hirose and Yamada 2 studied the kinematics of snake motion and developed multiple snake-like robots with active or passive joints and wheels. Choset's research group 3–6 developed a rigid modular snake actuated by servo motors mounted in series, which is capable of multiple gaits, including climbing up inside a wall opening and raising its head to observe the environment behind an obstacle. Gravdahl's research group developed an underwater snake robot, propelling with eel-like locomotion. Crespi and Ijspeert 7, 8 developed a rigid snake robot actuated with DC motors. This robot can conduct planar locomotion with central pattern generator on land and swim in water. Transeth et al. 9–11 developed a snake robot “Aiko,” which can do obstacle-aided locomotion in addition to frequently used locomotion methods like lateral undulation and sidewinding gaits. To increase adaptive functionality to complex terrain, Kano et al. 12 developed a decentralized control scheme, which enables the robotic snake to generate reasonable locomotion depend on surrounding environment. a modular system architecture comprising identical modules with integrated 3D soft actuation, valving, and electronics; To explore the proposed 3D soft robotic snake's ability to operate on nonplanar environments, we developed a custom locomotion sequence based on the climbing motion of real snakes, which allows our robot to climb up a step (as shown in Supplementary Video S1). At least three modules are needed on the ground for the robot to perform snake-like lateral undulation locomotion and power the robot to move forward. The step climbing motion will result in intermediate states with several modules that cannot touch the ground when climbing up high steps due to the restriction of the module length. Thus, to gain higher thrust and better balance, we added one more module to the robot, and created this gait for a 5-module version of the robotic snake, without loss of generality. As a result, this version offers greater balance for some of the modules to be lifted off the ground.
Due to the on-board curvature sensing and actuation, WPI SRS-4 is able to achieve autonomous navigation, which is the first for a soft mobile robot. We combine iterative learning control ( Moore and Xu, 2000) with the on-board curvature sensors to enable the robot to correct its serpentine gait to follow consistent desired trajectories. In addition, we propose an “adaptive bounding box” method for motion planning of the SRS-4. This technique encapsulates the footprint of the snake robot within a dynamic area, allowing for efficient planning using motion primitives and a Bidirectional A* algorithm. The experimental result verify the usability of these two algorithms and present methods that enable the adaptation of existing algorithms for soft robotics. Unfortunately this proved ineffective (see in my YouTube video at 3:38) as the scales still skimmed over the surface of the carpet instead of catching on the fibres and increasing the friction. Engineering and Mechanical Engineering Departments, Worcester Polytechnic Institute, Worcester, MA, United States One additional direction we would like to investigate is the use of the WPI SRS for manipulation. The compliant continuum structure of this robot makes it useful for wrapping around objects and moving them. This would particularly be a challenge because most current work in this field assumes that a manipulator has a static base, or mobile bases that have certain simply modeled kinematic behaviors. We would also like to examine the number of modules used in the WPI SRS and study its scalability. Increasing the number of modules may improve performance on certain tasks, but this may also increase computation time and energy costs. Certain tasks may have an ideal number of modules. For example, a manipulation task may need more modules for redundancy, while a locomotion task may need fewer.Iterative Learning Control (ILC) is a method for control of periodic systems ( Moore and Xu, 2000). The serpentine locomotion of our snake robot is a good example of this, with motion stemming from the repetition of a single gait cycle across modules with a phase difference. Compared with other methods, ILC has minimal computational requirements while maintaining the dynamic behavior of the serpentine gait. All the environmental obstacles can be represented as circles, while the boundaries of the environment are impassible walls as well. Luo M, Pan Y, Skorina EH, et al. Slithering towards autonomy: a self-contained soft robotic snake platform with integrated curvature sensing. Bioinspir Biomim 2015;10:055001. Crossref, Medline , Google Scholar you don't need to use small ball bearings for the wheels, I just had a lot laying around. Alternatively you could use LEGO wheels or other toy wheels. Under ideal circumstances, a SRS using Equation (1) with ϕ = 0 should travel in a straight line. However, we have observed in our previous work ( Luo et al., 2015a) that such a gait will cause the snake to veer off slightly to one direction. This is a result of differences in the behavior of the actuators that make up the snake. These slight differences, resulting from variations in fabrication, air flow rate, and weight distribution between modules result in non-straight trajectories even though all gait parameters are identical. We propose to solve these differences using Iterative Learning Control.
This article presents the design, fabrication, verification, and real-time simulation of a modular three-dimensional (3D) soft robotic snake based on the two-dimensional pneumatic snake robot we introduced previously. 1 With three-degree-of-freedom (DoF) modules, the proposed snake robot can perform 3D locomotion instead of simply planar motion. Three different locomotion methods, lateral undulation, sidewinding locomotion, and step climbing, are presented and tested on different surfaces and with different numbers of modules. A simulation environment was developed in parallel using NVidia Flex, which is a particle-based simulation technique developed by NVidia Gameworks. Because each servo is a little further along the sine wave than the previous servo we must shift each successive motor in the sine wave code. This is done using the following line. The shift variable can then be seen in action in the for-loop above. float pi=3.14159; Ijspeert AJ, Crespi A. Online trajectory generation in an amphibious snake robot using a lamprey-like central pattern generator model. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 262–268, IEEE, 2007. Crossref , Google Scholar Adding the vibration motor is very similar to the LEDs. It does not require to be connected to the 5V power supply on the arduino board, but gets it’s power from the Pin it connects to. We are connecting the vibration motor to Pin 10. It does not matter what pin you connect the vibration motor to, but we wanted to physically separate it from the LED groups for less confusion. Vikas V, Cohen E, Grassi R, et al. Design and locomotion control of a soft robot using friction manipulation and motor–tendon actuation. IEEE Trans Robot 2016;32:949–959. Crossref , Google Scholar
3. Locomotion Dynamics of a Modular Soft Robotic Snake
FIG. 6. Top-left: trajectory of the soft robotic snake CoM when locomotion frequency is 1.50 Hz. Top-middle: trajectory of the soft robotic snake CoM when locomotion frequency is 1.75 Hz. Bottom-left: trajectory of the soft robotic snake CoM when locomotion frequency is 2.00 Hz. Bottom-middle: soft robotic snake performing lateral undulation locomotion from right side to left side in real world. Right: error between simulation result and real-world experiment result with relate to distance traveled. CoM, central of mass. When attaching wiring to the LEDs, make sure that the wiring attaches to the long or short side of each LED. You cannot connect different sides of an LED together on the same circuit, they will not be able to turn on.
The vibration motor will need to be as long as the snake itself so it will be able to rattle the tail. The LEDs groups will each need to be put on their own strands so that the lights can be distributed down the snake body. Previous studies had mainly looked at snake movements on flat surfaces, but rarely in 3D terrain, except for on trees. Li said these did not necessarily account for real-life large obstacles such as pieces of rubble and debris that search and rescue robots would have to scale. We used the following code to get the LED blink. We first turn the LED on, then pause for one second to leave the LED on. We then turn the LED off and pause again for one second, so that the LED stays off. This then causes the blinking action to happen. Traditional SnakeBots move by changing the shape of their body, similar to actual snakes. Many variants have been created which use wheels or treads for movement. No SnakeBots have been developed yet that can completely mimic the locomotion of real snakes, but researchers have been able to produce new ways of moving that do not occur in nature.We demonstrate and verify different locomotion gaits and methods with the 3D soft robotic snake prototype, including Marvi H, Gong C, Gravish N, et al. Sidewinding with minimal slip: snake and robot ascent of sandy slopes. Science 2014;346:224–229. Crossref, Medline , Google Scholar a b Marvi, Hamidreza (2014-10-10). "Sidewinding with minimal slip: Snake and robot ascent of sandy slopes". Science. 346 (6206): 224–229. arXiv: 1410.2945. Bibcode: 2014Sci...346..224M. doi: 10.1126/science.1255718. PMID 25301625. S2CID 23364137 . Retrieved 2016-05-04. We found that, practically it is beneficial to keep the input pressure and gait frequency constant during locomotion, and thus, the distance from the Centroid trajectory to the left and right boundaries of the adaptive bounding box is a function of the steering offset in the gait algorithm.
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