Author: kodlab

Legged robots are by nature strongly non-linear, high-dimensional systems whose full complexity permits neither tractable mathematical analysis nor comprehensive numerical study. In consequence, a growing body of literature interrogates simplified “template” [1], [2] models—to date almost exclusively confined to sagittal- or horizontal-plane motion—with the aim of gaining insight into the design or control of the far messier reality. In this paper we introduce a simple bounding-in-place (“BIP”) model as a candidate frontal plane template for straight-ahead level ground running and explore its use in formulating hypotheses about whether and why rolling motion is important in legged locomotion. Numerical study of left-right compliance asymmetry in the BIP model suggests that compliance ratios yielding lowest steady state roll suffer far longer disturbance recovery transients than those promoting greater steady state roll. We offer preliminary experimental data obtained from video motion capture data of the frontal plane disturbance recovery patterns of a RHex-like hexapod suggesting a correspondence to the conclusions of the numerical study. Fig. 1. EduBot [19], a RHex-like [20] hexapedal robot.

Jonathan Clark was supported by the IC Postdoctoral Fellow Program under grant number HM158204-1-2030. Samuel Burden was supported by the SUNFEST REU program at the University of Pennsylvania. This work was also partially supported by the NSF FIBR grant #0425878. For more information: Kod*Lab

Recent developments in dynamic legged locomotion have focused on encoding a substantial component of leg intelligence into passive compliant mechanisms. One of the limitations of this approach is reduced adaptability: the final leg mechanism usually performs optimally for a small range of conditions (i.e. a certain robot weight, terrain, speed, gait, and so forth). For many situations in which a small locomotion system experiences a change in any of these conditions, it is desirable to have a variable stiffness leg to tune the natural frequency of the system for effective gait control. In this paper, we present an overview of variable stiffness leg spring designs, and introduce a new approach specifically for autonomous dynamic legged locomotion. We introduce a simple leg model that captures the spatial compliance of the tunable leg in three dimensions. Lastly, we present the design and manufacture of the multi-directional variable stiffness legs, and experimentally validate their correspondence to the proposed model.

We present our approach to undergraduate engineering education, “A contexualized, social, self-paced, engineering education for life-long learners” through a look at a new two course introductory sequence for the freshman year. As the centerpiece of these courses, we use a smaller version of our advanced research platform, RHex, to integrate introductory programming material with electrical and systems engineering theory. We move away from the traditional “filing cabinet” test and drill approach (fill minds up with facts and test)as students start to become life-long learners, creators, and innovators through exposure to research level problems. The initial assessments of our approach have been very positive. Treating students as junior-researchers and exposing them to cutting edge robotics yielded a 98% attendance rate, 95% of students choosing to take a optional follow-on course, and 90% saying they would recommend the course to another student. We describe as well our students’ development of leadership and communications skills through a required ervice-learning component.Working with high-schoolers from the School District of Philadelphia the undergraduates in the course in turn teach material similar to what they have learned in class

This paper reviews a template for dynamical climbing originating in biology, explores its stability properties in a numerical model, and presents empirical data from a physical prototype as evidence of the feasibility of adapting the dynamics of the template to robot that runs vertically upward.

The recently proposed pendulous climbing model abstracts remarkable similarities in dynamic wall scaling behavior exhibited by radically different animal species. The present paper’s first contribution summarizes a numerical study of this model to hypothesize that these animals’ apparently wasteful commitments to lateral oscillations may be justified by a significant gain in the dynamical stability and, hence, the robustness of their resulting climbing capability.

The paper’s second contribution documents the design and offers preliminary empirical data arising from a physical instantiation of this model. Notwithstanding the substantial differences between the proposed bio-inspired template and this physical manifestation, initial data suggest the mechanical climber may be capable of reproducing both the motions and ground reaction forces characteristic of dynamical climbing animals. Even without proper tuning, the robot’s steady state trajectories manifest a substantial exchange of kinetic and potential energy, resulting in vertical speeds of 0.30 m/s (0.75 bl/s) and claiming its place as the first bio-inspired dynamical legged climbing platform.

We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot’s body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences.

Simple mathematical models or ‘templates’ of locomotion have been effective tools in understanding how animals move and have inspired and guided the design of robots that emulate those behaviors. This paper describes a recently proposed biologically-based template for dynamic vertical climbing, and evaluates the feasibility of adapting it to build a vertical ‘running’ robot.

We present the results a simulation study suggesting that appropriate mechanical and control alterations to the template result in fast stable climbing that preserves the characteristic body motions and foot forces found in the template model and in animals. These design changes should also allow the robot to operate with commercially available actuators and in the same power to weight range as other running and climbing robots.

Cheetahs and beetles run, dolphins and salmon swim, and bees and birds fly with grace and economy surpassing our technology. Evolution has shaped the breathtaking abilities of animals, leaving us the challenge of reconstructing their targets of control and mechanisms of dexterity. In this review we explore a corner of this fascinating world. We describe mathematical models for legged animal locomotion, focusing on rapidly running insects and highlighting past achievements and challenges that remain. Newtonian body–limb dynamics are most naturally formulated as piecewise-holonomic rigid body mechanical systems, whose constraints change as legs touch down or lift off. Central pattern generators and proprioceptive sensing require models of spiking neurons and simplified phase oscillator descriptions of ensembles of them. A full neuromechanical model of a running animal requires integration of these elements, along with proprioceptive feedback and models of goal-oriented sensing, planning, and learning. We outline relevant background material from biomechanics and neurobiology, explain key properties of the hybrid dynamical systems that underlie legged locomotion models, and provide numerous examples of such models, from the simplest, completely soluble “peg-leg walker” to complex neuromuscular subsystems that are yet to be assembled into models of behaving animals. This final integration in a tractable and illuminating model is an outstanding challenge.

We discuss the gait generation and control architecture of a bioinspired climbing robot that presently climbs a variety of vertical surfaces, including carpet, cork and a growing range of stucco-like surfaces in the quasi-static regime. The initial version of the robot utilizes a collection of gaits (cyclic feed-forward motion patterns) to locomote over these surfaces, with each gait tuned for a specific surface and set of operating conditions. The need for more flexibility in gait specification (e.g., adjusting number of feet on the ground), more intricate shaping of workspace motions (e.g., shaping the details of the foot attachment and detachment trajectories), and the need to encode gait “transitions” (e.g., tripod to pentapod gait structure) has led us to separate this trajectory generation scheme into the functional composition of a phase assigning transformation of the “clock space” (the six dimensional torus) followed by a map from phase into leg joints that decouples the geometric details of a particular gait. This decomposition also supports the introduction of sensory feedback to allow recovery from unexpected event and to adapt to changing surface geometries.

We report on a continuous-time rigid-body pose estimator for a walking hexapod robot. Assuming at least three legs remain in ground contact at all times, our algorithm uses the outputs of six leg-configuration sensor models together with a priori knowledge of the ground and robot kinematics to compute instantaneous estimates of the 6-degrees-of-freedom (6-DOF) body pose. We implement this estimation procedure on the robot RHex by means of a novel sensory system incorporating a model relating compliant leg member strain to leg configuration delivered to the onboard CPU over a customized cheap high-performance local wireless network. We evaluate the performance of this algorithm at widely varying body speeds and over dramatically different ground conditions by means of a 6-DOF vision-based ground-truth measurement system (GTMS). We also compare the odometry performance to that of sensorless schemes—both legged as well as on a wheeled version of the robot—using GTMS measurements of elapsed distance.

We review a large multidisciplinary effort to develop a family of autonomous robots capable of rapid, agile maneuvers in and around natural and artificial vertical terrains such as walls, cliffs, caves, trees and rubble. Our robot designs are inspired by (but not direct copies of) biological climbers such as cockroaches, geckos, and squirrels. We are incorporating advanced materials (e.g., synthetic gecko hairs) into these designs and fabricating them using state of the art rapid prototyping techniques (e.g., shape deposition manufacturing) that permit multiple iterations of design and testing with an effective integration path for the novel materials and components. We are developing novel motion control techniques to support dexterous climbing behaviors that are inspired by neuroethological studies of animals and descended from earlier frameworks that have proven analytically tractable and empirically sound. Our near term behavioral targets call for vertical climbing on soft (e.g., bark) or rough surfaces and for ascents on smooth, hard steep inclines (e.g., 60 degree slopes on metal or glass sheets) at one body length per second.