On Saturday, April 22, Dr. Zhenyu Gan presented his research on legged robots at the Museum of Science and Technology (MOST) for middle and high school students. Dr. Gan talked about the studies of diverse animal gaits and presented state of the art research on legged robots. He also discussed their potential applications in the near future.
Jing Cheng presented his research on iterative learning control on ECS research day. Jiayu Ding, Yasser Gurmallah A Alqaham, Deze Liu attended the poster sessions.
Abstract— It is often overlooked by roboticists when designing locomotion controllers for their legged machines, that energy consumption plays an important role in selecting the best gaits for locomotion at high speeds or over long distances. The purpose of this study is to examine four similar asymmetrical quadrupedal gaits that are frequently observed in legged animals in nature. To understand how a specific footfall pattern will change the energetics of a legged system, we first developed a full body model of a quadrupedal robot called A1. And for each gait we created a hybrid system with desired footfall sequence and rigid impacts. In order to find the most energy efficient gait, we used optimal control methods to formulate the problem as a trajectory optimization problem with proper constraints and objective function. This problem was implemented and solved in a nonlinear programming framework called FROST. Based on the optimized trajectories for each gait, we investigated the values of cost of transport and the work done by all joints. Moreover, we analyzed the exchange of angular momentum in different components of the system during the whole stride cycle. According to the simulation results, bounding with two flight phases is likely to be the most energy efficient gait for A1 across a wide range of speed.
Abstract— Symmetry manifests itself in legged locomotion in a variety of ways. No matter where a legged system begins to move periodically, the torso and limbs coordinate with each other’s movements in a similar manner. Also, in many gaits observed in nature, the legs on both sides of the torso move in exactly the same way, sometimes they are just half a period out of phase. Furthermore, when some animals move forward and backward, their movements are strikingly similar as if the time had been reversed. This work aims to generalize these phenomena and propose formal definitions of symmetries in legged locomotion using group theory terminology. Symmetries in some common quadrupedal gaits such as pronking, bounding, half-bounding, and galloping have been discussed. Moreover, a spring-mass model has been used to demonstrate how breaking symmetries can alter gaits in a legged system. Studying the symmetries may provide insight into which gaits may be suitable for a particular robotic design, or may enable roboticists to design more agile and efficient robot controllers by using certain gaits.
Gan oversees the Dynamic Locomotion and Robotics (DLAR) Lab, where he builds simplistic models for legged locomotion. The lab is part of the University’s galvanic response to the rise of autonomous systems. Recent additions include the Autonomous Systems Policy Institute and the Artificial Intelligence, Autonomous Systems and Human-Technology Frontier research group.
Gan’s research draws on science, engineering and technology. Using motion-capture data to isolate the movements of animals (mainly dogs and horses), his team develops simple spring-mass models. These models imitate different gait patterns, producing locomotion through a sequence of foot contacts with the ground.
Abstract: precise trajectory tracking for legged robots can be challenging due to their high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances from the environment. A commonly adopted solution to overcome these challenges is to use optimization-based algorithms and approximate the system with a simplified, reduced-order model. Additionally, deep neural networks are becoming a more promising option for achieving agile and robust legged locomotion. These approaches, however, either require large amounts of onboard calculations or the collection of millions of data points from a single robot. To address these problems and improve tracking performance, this paper proposes a method based on iterative learning control. This method lets a robot learn from its own mistakes by exploiting the repetitive nature of legged locomotion within only a few trials. Then, a torque library is created as a lookup table so that the robot does not need to repeat calculations or learn the same skill over and over again. This process resembles how animals learn their muscle memories in nature. The proposed method is tested on the A1 robot in a simulated environment, and it allows the robot to pronk at different speeds while precisely following the reference trajectories without heavy calculations.
planning, feedback control, and feedforward control are highlighted in green,
blue, and orange regions respectfully. The detailed calculations of all modules
are listed with their corresponding equation numbers in the paper.
Our lab collaborated with Technology Alliance of Central New York (TACNY) and rolled out the first summer program for students entering 6th-9th grades, STEM Trekkers in 2022. The program is free and open to any student with interest in science, technology, engineering, and math. We offer a comprehensive, unique, and rigorous STEM interactive educational experience that piques students’ interest in STEM and seek to highlight the crossover between industry and education in our community.
The theme for 2022 workshop was Robotics Engineering, Design and Coding. 40 students attended the workshop and they were given 20 quadrupedal robot kits (Bittle from Petoi). During the two-day workshop at SU, they learned how to design a robot, build the robot, program the robot using a graphical interface called TinkerGen, and control the robot to shake head, to walk in different directions. We also had a soccer competition on the 2nd day to let the students test and compete with their robots.
Addtionally, they had a tour to our lab at SU and the local robotic company Ramboll Inc. located in Liverpool, NY. The workshop was a great success this year and we plan to hold this program again next year. Please contact us through TACNY’s website if you want to sign up for the event of next year. https://www.tacny.org/trekkers/
The Dynamic Walking 2022 conference was held at the University of Wisconsin-Madison, from June 13th to June 16th.
The recording of the talk can be found here: https://mediaspace.wisc.edu/media/DW22_Ding%2C+Jiayu+-+June+14th+2022%2C+12A01A10+pm/1_oo1ew2jk
The paper entitled ‘A Template Model Explains Jerboa Gait Transitions Across a Broad Range of Speeds’ can be reached and cited through https://www.frontiersin.org/articles/10.3389/fbioe.2022.804826/full.
Exemplary code and other supplementary resources have been uploaded to UM Deep Blue Data repository https://doi.org/10.7302/ewaa-qm16.