Arboreal environments challenge current robots but are deftly traversed by many familiar animals such as squirrels. We present a small, 450 g robot “Pinto” developed for tree-jumping, a behavior seen in squirrels but rarely in legged robots: jumping from the ground onto a vertical tree trunk. We develop a powerful and lightweight latched series-elastic actuator using a twisted string and carbon fiber springs. We consider the effects of scaling down conventional quadrupeds and experimentally show how storing energy in a parallel-elastic fashion using a latch increases jump energy compared to series-elastic or springless strategies. By switching between series and parallel-elastic modes with our latched 5-bar leg mechanism, Pinto executes energetic jumps as well as maintains continuous control during shorter bounding motions. We also develop sprung 2-DoF arms equipped with spined grippers to grasp tree bark for high-speed perching following a jump.

Soldiers in the field often need to cross negative obstacles, such as rivers or canyons, to reach goals or safety. Military gap crossing involves on-site temporary bridges construction. However, this procedure is conducted with dangerous, time and labor intensive operations, and specialized machinery. We envision a scalable robotic solution inspired by advancements in force-controlled and Cable-Driven Parallel Robots (CDPRs); this solution can address the challenges inherent in this transportation problem, achieving fast, efficient, and safe deployment and field operations. We introduce the embodied vision in Co3MaNDR, a solution to the military gap crossing problem, a distributed robot consisting of several modules simultaneously pulling on a central payload, controlling the cables’ tensions to achieve complex objectives, such as precise trajectory tracking or force amplification. Hardware experiments demonstrate teleoperation of a payload, trajectory following, and the sensing and amplification of operators’ applied physical forces during slow operations. An operator was shown to manipulate a 27.2 kg (60 lb) payload with an average force utilization of 14.5% of its weight. Results indicate that the system can be scaled up to heavier payloads without compromising performance or introducing superfluous complexity. This research lays a foundation to expand CDPR technology to uncoordinated and unstable mobile platforms in unknown environments.

Legged robots leverage ground contacts and the reaction forces they provide to achieve agile locomotion. However, uncertainty coupled with contact discontinuities can lead to failure, especially in real-world environments with unexpected height variations such as rocky hills or curbs. To enable dynamic traversal of extreme terrain, this work introduces 1) a proprioception-based gait planner for estimating unknown hybrid events due to elevation changes and responding by modifying contact schedules and planned footholds online, and 2) a two-degree-of-freedom tail for improving contact-independent control and a corresponding decoupled control scheme for better versatility and efficiency. Simulation results show that the gait planner significantly improves stability under unforeseen terrain height changes compared to methods that assume fixed contact schedules and footholds. Further, tests have shown that the tail is particularly effective at maintaining stability when encountering a terrain change with an initial angular disturbance. The results show that these approaches work synergistically to stabilize locomotion with elevation changes up to 1.5 times the leg length and tilted initial states.

Entanglements like vines and branches in natural settings or cords and pipes in human spaces prevent mobile robots from accessing many environments. Legged robots should be effective in these settings, and more so than wheeled or tracked platforms, but naive controllers quickly become entangled and stuck. In this paper we present a method for proprioception aimed specifically at the task of sensing entanglements of a robot's legs as well as a reaction strategy to disentangle legs during their swing phase as they advance to their next foothold. We demonstrate our proprioception and reaction strategy enables traversal of entanglements of many stiffnesses and geometries succeeding in 14 out of 16 trials in laboratory tests, as well as a natural outdoor environment.

We present simplified 2D dynamic models of the 3D, passive dynamic inspired walking gait of a physical quasi-passive walking robot. Quasi-passive walkers are robots that integrate passive walking principles and some form of actuation. Our ultimate goal is to better understand the dynamics of actuated walking in order to create miniature, untethered, bipedal walking robots. At these smaller scales there is limited space and power available, and so in this work we leverage the passive dynamics of walking to reduce the burden on the actuators and controllers. Prior quasi-passive walkers are much larger than our intended scale, have more complicated mechanical designs, and require more precise feedback control and/or learning algorithms. By leveraging the passive 3D dynamics, carefully designing the spherical feet, and changing the actuation scheme, we are able to produce a very simple 3D bipedal walking model that has a total of 5 rigid bodies and a single actuator per leg. Additionally, the model requires no feedback as each actuator is controlled by an open-loop sinusoidal profile. We validate this model in 2D simulations in which we measure the stability properties while varying the leg length/amplitude ratio, the frequency of actuation, and the spherical foot profile. These results are also validated experimentally on a 3D walking robot (15cm leg length) that implements the modeled walking dynamics. Finally, we experimentally investigate the ability to control the heading of the robot by changing the open-loop control parameters of the robot.

We develop a drift-free roll and pitch attitude estimation scheme for monopedal jumping robots. The estimator uses only onboard rate gyroscopes and encoders and does not rely on external sensing or processing. It is capable of recovering from attitude estimate disturbances and, together with onboard velocity estimation, enables fully autonomous stable hopping control. The estimator performs well on a small untethered robot capable of large jumps and extreme stance accelerations. We demonstrate that the robot can follow a rectangular path using onboard dead-reckoning with less than 2 meters of drift over 200 seconds and 300 jumps covering 60 m. We also demonstrate that the robot can operate untethered outdoors under human wireless joystick direction.

We developed a deadbeat foot placement hopping controller for an untethered monopedal robot, Salto-1P. The controller uses a third order Taylor series approximation to an offline dynamic model and performs well on the physical platform. The robot demonstrated precise foot placement even on trajectories with aggressive changes in speed, direction, and height: in a random walk, its error standard deviation was 0.10 m. We establish how foot placement precision is tightly limited by attitude control accuracy, requiring attitude error less than 0.7 degrees for some tasks. We also show how foot placement precision degrades linearly as hopping height increases. These precision results apply to the large class of controllers that prescribe touchdown angle to control running velocity.

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.

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.