Stewart DE, Trinkle JC. An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and coulomb friction. Int J Numer Methods Eng 1996;39:2673–2691. Crossref , Google Scholar Research in recreating locomotion via scales was done at Harvard University and demonstrated in this video. I was unable to devise a similar method to move the scales up and down on my robot and instead settled for attaching passive 3D printed scales to the underbelly. Solder the GND and 5V wires to a 3x7 hole perf board in the tail with a capacitor and screw terminals. The purpose of the capacitor is to remove any current draw spikes caused when starting up the servos, that can reset the Arduino Nano (if you don't have a capacitor you can probably get away without it, but it is better to be safe). Remember that the long prong of electrolytic capacitors need to be connected to the 5V line and the shorter prong to the GND line. Solder the the GND wire to the GND pin of the Nano and the 5V wire to the 5V pin. Note if you are using a different voltage, (see next section), say a Lipo battery with 7.4V, then wire the red wire to the Vin pin, NOT the 5V pin, doing so will destroy the pin. This instructable is technically a 2-in-1, in that I explain how to make 2 different versions of a robotic snake. If you are only interested in building one of the snakes ignore the instructions for the other snake. These 2 different snakes will be from here on referred to using the following phrases interchangeably:

If the steps got taller and more slippery, the snake would move more slowly and wriggle their front and rear body less to maintain stability. Ming Luo 1 Zhenyu Wan 2 Yinan Sun 2 Erik H. Skorina 2 Weijia Tao 2 Fuchen Chen 2 Lakshay Gopalka 2 Hao Yang 2 Cagdas D. Onal 3 * When setting up the project in previous steps, everything was attached pretty closely to the breadboard. If we want out snake to be the full length of the base, we will need to create wire extensions for various portions of the electrical. In this step we will be setting up a blinking LED, similar to this instructable tutorial. Though this is a simple step, it might be useful to learn more information about how the setup works, as we will not be going through specifics in this step. We conduct experiments to demonstrate undulatory locomotion on a paper surface with frequency ( ) of 1.5, 1.75, and 2 Hz. The phase delay is set to be , which results in 1.25 traveling curvature waves along the body. For comparison, the same tests are conducted in the real-time simulation for our soft robotic snake. In Figure 6, the trajectory of the soft robotic snake central of mass (CoM) are presented. The blue lines represent the result from simulation, while the red lines represent the result from real-world experiments. In a real-world experiment, the robotic snake can reach a velocity of 140.25 mm/s (0.275 body length/s) under 2 Hz.

ML: mechanical design and fabrication, algorithm developing, and testing. ZW and YS: fabrication, low-level control developing, and testing. ES, WT, and FC: mechanical design and fabrication. LG and HY: testing. CO: principal investigator and idea provider. All authors: contributed to the article and approved the submitted version. Funding

myServos[2*j].write(90+offset+amplitude*sin(Speed*rads+j*Wavelengths*shift-(Multiplier-1)*pi/4)); //moves servos in vertical plane Skorina EH, Onal CD. Soft hybrid wave spring actuators. Adv Intell Syst 2020;2:1900097. Crossref , Google Scholar Mobile robots are promising tools for emergency response. However, it is not easy for traditional rigid robots to explore narrow, complicated, unknown, or dangerous environments, especially where first responders cannot enter during search-and-rescue missions. A novel step climbing locomotion, which is developed in the real-time simulation conveniently and rapidly as compared with experimental iterations.

5. Motion Planning and Trajectory Tracking

The advantage of this approach is a computationally feasible level of abstraction for our soft snake robot to perform motion planning in an obstacle course, despite is relatively complex body deformation. We put this concept into a sampling based motion planning algorithm and bound the problem using the following assumptions: 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. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Material The robotic snake is theoretically supposed to move in a straight line with constant input and parameter setting. Because of the fabrication inaccuracy, uneven paper surface, and unexpected sliding friction, the robotic snake cannot move in a straight line. The simulation results present a similar tendency and velocity with the real-world experiments, while the trajectories in simulation results are closer to a straight line since there are no fabrication inaccuracies and the surface can be set to be perfectly even.

This line writes to each of the 10 servos a sine wave. The base line angle is 90 degrees, the offset variable will control if the snake goes forward (offset=0) or turns left or right (offset=10 or -10), see GIF above. The sine wave outputs a value between [-1,1], this value can be scaled up by multiplying by the amplitude. for(int j=0; j<10; j++){ Tolley MT, Shepherd RF, Karpelson M, et al. An untethered jumping soft robot. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 561–566, IEEE, 2014. Google Scholar 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.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.