The first load-bearing and energetically autonomous exoskeleton, called the Berkeley Lower Extremity Exoskeleton (BLEEX) walks at the average speed of two miles per hour while carrying 75 pounds of load. The project, funded in 2000 by the Defense Advanced Research Project Agency (DARPA) tackled four fundamental technologies: the exoskeleton architectural design, a control algorithm, a body LAN to host the control algorithm, and an on-board power unit to power the actuators, sensors and the computers. This article gives an overview of the BLEEX project.

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The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.

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In this paper, we present a method to calculate the intended motion of joints in the human body by analysing EMG signals. Those signals are emitted by the muscles attached to the adjoining bones during their activation. With the resulting intended motion, a leg orthosis can be controlled in realtime to support disabled people while walking or climbing stairs and help patients suffering from the effects of a stroke in their rehabilitation efforts. To allow a variety of different motions, a human body model with physical properties is developed and synchronized with data recorded from the pose sensors. Computing the intended motion is performed by converting calibrated EMG signals to muscle forces which animate the model. The algorithm was evaluated with experiments showing the calculated intended motion while climbing one step of a stair. The algorithm and the experimental results are both shown.

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Energetic autonomy of a hydraulic-based mobile field robot requires a power source capable of both electrical and hydraulic power generation. While the hydraulic power is used for locomotion, the electric power is used for the computer, sensors and other peripherals. An internal combustion engine was used as the prime mover due to the high energy density of gasoline. The primary specification for this hybrid hydraulic-electric power unit (HEPU) is that it must output constant pressure hydraulic power and constant voltage electric power. An on-board computer uses a pressure sensor and a speed sensor to regulate the pressure and voltage by modulating a hydraulic solenoid valve and an engine throttle. The speed regulation also results in a system noise with predictable frequency band which allows for optimal muffler design. A novel characteristic of this power source is its cooling system in which hydraulic fluid is used to cool the engine cylinders. Several hydraulic-electric power units were built and successfully demonstrated on the Berkeley Lower Extremity Exoskeleton (BLEEX) shown on bleex.me.berkeley.edu/bleex.htm. A prototype power unit weighs 27 Kg, outputs 2.3 kW (3.0 hp) hydraulic power at 6.9 MPa (1000 psi), and 220 W of electric power at 15 VDC.

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A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects’ arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and, from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while after learning, hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that, besides learning a model of the force field, the nervous system also chose to exploit the space to minimize the effects of the force field on the realization of the endpoint trajectory plan. We discuss applications of these results in the light of current theories of robotic control, including inverse kinematics and optimal control.

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