Abstract-Ohio State and Stanford Universities are cooperating to construct and artificial quadruped. This quadruped has a rest leg length of 0.68m and weighs 70kg. The body is made of four single-leg modules. Each module houses an articulated 3-dof leg. The articulated leg structure uses a set of mechanical springs to store energy. The stiffness of the leg, from hip to foot, is highly nonlinear. The leg initially stiffens as the leg is compressed the first 1/3 of its range, then remains roughly constant for the remainder. This stiffening allows the leg to maintain a relatively constant working length. A cable is used to flex the knee of the quadruped. As the cable is pulled, the knee bends, tensioning the energy storage springs. This cable makes about a ¼ turn over a pulley which is concentric with the hip axis, ½ turn around a pulley at the ankle and then ¾ turn back around a second hip pulley in a direction opposite the first hip pulley. This arrangement decouples cable tension and hip torque. The non-anchored end of the cable is tensioned by a capstan. This capstan uses unique geometry to decrease holding torque and to release the cable instantly. The current top speed of this leg in a 2-dimensional test is 4.15m/s. This leg has been controlled using two control systems, both implemented on an embedded controller attached to the leg. An articulated leg presents control challenges not seen when trying to control a prismatic leg. The first controller tested is a heuristic algorithm whose parameters are updated in real time by the LevenbergMarquardt learning method. This controller is similar to the controllers used by Raibert[24], with modifications to allow for leg asymmetry. The second controller tested is a direct adaptive fuzzy controller. The fuzzy controller consists of a rule base, inference mechanism, fuzzification interface, and defuzzification interface. Fuzzification starts by mapping an input (body velocity, desired change in velocity, height) into one or more membership functions.The inference mechanism then determines the applicability of each rule to the current inputs. Defuzzification combines the recommendations of each rule in an output based upon rule certainties. The adaptation mechanism modifies the rule output centers to correct velocity errors. The adaptation mechanism gains are experimentally tuned. Once tuned, the controller quickly adapts to leg changes. Both control systems were tested with and without adaptation. Both systems more accurately tracked desired velocity with adaptation. Accurate, high resolution, high speed body attitude sensing is essential for successful quadruped operation.No existing solutions meet the project requirements. An adequate sensor system is being developed in cooperation with a commercial vendor. Initial results show that accurate position tracking is possible with currently available MEMS inertial sensors.
Ohio State (OSU) and Stanford Universities are cooperating to understand quadruped galloping through the design of a self-contained, biomimetic, galloping robot. The leg for this quadruped was first designed at Ohio State University (OSU). A second-generation leg, with the same functional geometry, has been designed and tested at Stanford University. The objective of these tests was to determine that the single leg would be capable of prolonged operation at a velocity of 5m/s, and that the control system, developed in simulation, would function under real-world conditions. The mechanical design of the quadruped is based on properties of biological quadruped animal legs. Important biomimetic design characteristics include minimal impact loss, elastic energy storage, and low inertia. The cable linkage, which works against a large spring to flex the knee, is uncoupled from the front-to-back hip actuator through a parallelogram-like cable mechanism. The controller developed for the leg is a direct adaptive fuzzy controller. The direct adaptive approach does not require system identification and can use heuristics to successfully control a complex system. With this controller we were able to successfully control a single 2dof leg constrained in yaw, pitch, roll and transverse translation. This controller was executed once each cycle at the top-of-flight. Parameters set by the controller were passed to PD controllers at each of the 2 joints: the hip and cable-actuated knee. The control of this leg required only 7 x 5 x 3 = 105 rules, each with a corresponding output for thigh and knee angle. This controller was implemented on an embedded processor attached to the leg. The leg reached a speed of 3m/s. Modifications to the leg will increase this speed. The controller successfully adapted to the leg.
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