In this work we present a new robotic system, Salto-1P, for exploring extreme jumping locomotion. Salto-1P weighs 0.098 kg, and has an active leg length of 14.4 cm. The robot is able to perform a standing vertical leap of 1.25 m, continuously hop to heights over 1 m, and jump over 2 m horizontally. Salto-1P uses aerodynamic thrusters and an inertial tail to control its attitude in the air. A linearized Raibert step controller was sufficient to enable unconstrained in-place hopping and forwards-backwards locomotion with external position feedback. We present studies of extreme jumping locomotion in which the robot spends just 7.7% of its time on the ground, experiencing accelerations of 14 times earth gravity in its stance phase. An experimentally collected dataset of 772 observed jumps was used to establish the range of achievable horizontal and vertical impulses for Salto-1P.

Commercialization of desktop milling machines has made rapid Printed Circuit Board (PCB) fabrication accessible. Unfortunately, PCB design for embedded and robotic systems is still a tedious and time consuming activity. In this paper, we present a technique, Just In Time Printed Circuit Board (JITPCB) for designing PCB systems at speeds commensurate with the capability of desktop PCB milling machines. We propose designing boards by writing software circuit generators that wire together and lay out circuit components in a hierarchical and reusable fashion. We have developed a declarative design mechanism allowing users to specify desired input and output peripherals and well as application code. Given this input, our system produces a complete working circuit board design, along with necessary initialization and networking code. Our system is an open framework that allows users to create a set of highly reusable parametric hardware/software modules. We demonstrate our approach by showing some common robotics applications designed with JITPCB to show its utility and generality.

New work in robotics targets the development of controllable agile motions such as leaping. In this work, we examine animal and robotic systems on the metric of jumping agility and find that animals can outperform the most agile robots by a factor of two. These specially adapted animals use a jumping strategy we term power modulation to generate more peak power for jumping than otherwise possible. A novel eight-bar revolute mechanism designed with a new linkage synthesis approach encodes the properties for power modulation as well as constraints which assure rotation-free jumping motion. We fabricate an 85 gram prototype and demonstrate that it can perform a range of jumps while constrained by a linear slide. The prototype can deliver 3.63 times more peak jumping power than the maximum its motor can produce. A simulation matched to the physical parameters of the prototype predicts that the robot can attain an agility exceeding that of the most agile animals if the actuator power is increased to 15W.

The X2-VelociRoACH is a 54 gram experimental legged robot which was developed to test hypotheses about running with unnaturally high stride frequencies. It is capable of running at stride frequencies up to 45 Hz, and velocities up to 4.9 m/s, making it the fastest legged robot relative to size. The top speed of the robot was limited by structural failure. We present new methods and materials to make more robust folded robotic structures. High-frequency running experiments with the robot shows that the power required to cycle its running appendages increase cubically with the stride rate. Our findings show that although it is possible to further increase the maximum velocity of a legged robot with the simple strategy of increasing stride frequency, considerations must be made for the energetic demands of high stride rates.

As we seek to develop more maneuverable legged robots, we need to understand the dynamics of legged turning in an approachable fashion. In this work, we analyze the dynamic turning motion of a dynamic hexapedal millirobot. We explore a family of phase locked turning gaits where all legs of the robot move at the same speed. These gaits are highly periodic, allowing the vertical height and roll angle of the robot to be approximated by single harmonic sinusoidal functions. We demonstrate that oscillations in height and roll angle determine the robot's turning behavior. The phase between these oscillations (and therefore the turning behavior) was modulated by the phase between the left and right sets of legs. A simple model using compliant leg forces was shown to match turning behavior for a range of 5Hz turning gaits. Based on the finding that roll oscillations are major determinants of turning behavior, we modified the robot to create a new high speed turning gait (forward velocity: 0.4 m/s, turn rate 206°/s).

Legged robots come in a range of sizes and capabilities. By combining these robots into heterogeneous teams, joint locomotion and perception tasks can be achieved by utilizing the diversified features of each robot. In this work we present a framework for using a heterogeneous team of legged robots to detect slippery terrain. StarlETH, a large and highly capable quadruped uses the VelociRoACH as a novel remote probe to detect regions of slippery terrain. StarlETH localizes the team using internal state estimation. To classify slippage of the VelociRoACH, we develop several Support Vector Machines (SVM) based on data from both StarlETH and VelociRoACH. By combining the team's information about the motion of VelociRoACH, a classifier was built which could detect slippery spots with 92% (125/135) accuracy using only four features.

The VelociRoACH is a 10 cm long, 30 gram hexapedal millirobot capable of running at 2.7 m/s, making it the fastest legged robot built to date, relative to scale. We present the design by dynamic similarity technique and the locomotion adaptations which have allowed for this highly dynamic performance. In addition, we demonstrate that rotational dynamics become critical for stability as the scale of a robotic system is reduced. We present a new method of experimental dynamic tuning for legged millirobots, aimed at finding stable limit cycles with minimal rotational energy. By implementing an aerodynamic rotational damper, we further reduced the rotational energy in the system, and demonstrated that stable limit cycles with lower rotational energy are more robust to disturbances. This method increased the stability of the system without detracting from forward speed.

For maximum maneuverability, terrestrial robots need to be able to turn precisely, quickly, and with a small radius. Previous efforts at turning in legged robots primarily have used leg force or velocity modulation. We developed a palm-sized legged robot, called TAYLRoACH. The tailed robot was able to make rapid, precise turns using only the actuation of a tail appendage. By rapidly rotating the tail as the robot runs forward, the robot was able to make sudden 90° turns at 360 °s-1. Unlike other robots, this is done with almost no change in its running speed. We have also modeled the dynamics of this maneuver, to examine how features, such as tail length and mass, affect the robot's turning ability. This approach has produced turns with a radius of 0.4 body lengths at 3.2 body lengths per second running speed. Using gyro feedback and bang-bang control, we achieve an accuracy of ± 5° for a 60° turn.

Abstract — This paper presents a simple, data-driven tech-nique for identifying models for the dynamics of legged robots. Piecewise Affine (PWA) models are used to approximate the observed nonlinear system dynamics of a hexapedal millirobot. The high dimension of the state space (16) and very large number of state observations (∼100,000) motivated the use of statistical clustering methods to automatically choose the submodel regions. Comparisons of models with 1 to 50 PWA regions are analyzed with respect to state derivative prediction and forward simulation accuracy. Derivative prediction accu-racy was shown to reduce average in-axis absolute error by up to 52 % compared to a null estimator. Simulation results show tracking of state trajectories over one stride length, and the degradation of simulation prediction is analyzed across model complexity and time horizon. We describe metrics for comparing the performance of different model complexities across one-step and simulation predictions. I.

Minimally actuated millirobotic crawlers navigate unreliably over uneven terrain-even when designed with inherent stability-mostly because of manufacturing variabilities and a lack of good models for ground interaction. In this paper, we investigate the performance of a legged robot as it traverses three distinct rough terrains: tile, carpet, and gravel. Furthermore, we present an accurate, robust, low-lag, and efficient algorithm for terrain classification that uses vibration data from the on-board inertial measurement unit and motor control data from back-EMF sensing and magnetic encoders.

We present a rapid-manufacturable, hair-actuated contact sensor array designed for use on legged millirobots. The sensor is an array of sensitive contact switches. Each switch is activated by loading a hair mounted at the switch tip. The hair sensor array is sufficiently sensitive to detect the small contact forces experienced by a lightweight robot, with an average normal sensitivity of 0.8 grams/hair. The compliant polymer hairs detect both normal and shear contact, allowing the array to detect a variety of contact forces. By virtue of its design, the hair sensor array can be fabricated using a roll-to-roll layered process. We demonstrate a straightforward application of the sensor technology on a hexapedal millirobot, using the array to both estimate average ground speed and detect high-centering when running over obstacles. This application is of particular interest for milliscale robots operating in rough terrain, where the risk of entrapment is high. Our results indicate that the hair sensor array can estimate ground speed and detect high-centering when running over simple geometric obstacles.