In this paper, we present a control theoretic formulation for composing human control inputs with an automatic controller for shared control of a quadruped rescue robot. The formulation utilizes a model predictive controller to guide human controlled leg positions to satisfy state constraints that correspond to static stability for the robot. A hybrid control architecture that incorporates the model predictive controller is developed to implement a gait that guarantees stable foot-placements for the robot. The algorithm is applied to a simulation of a quadruped rescue robot with human input provided through haptic joysticks.
Pneumatic actuators possess several attractive qualities: high power and force density, potentially adaptable compliance, and clean, safe, and low cost actuation. However, control of pneumatic actuators has proven difficult, limited by inherent compliance of the actuator, nonlinear and discontinuous third order dynamics, and friction. Stiction and compliance lead to a sandwiched nonlinearity that causes stick-slip and can cause significant tracking error and even instability. A broadly applicable method of friction compensation is addition of a feedforward term updated from a friction estimate at each time step. Since pneumatic dynamics are slow, achievable compensation can be insufficient. In this work, friction is estimated over a prediction horizon and then input into a model-based predictive controller as an offset term, so that compensation is planned and optimal over the prediction horizon. The controller is tested in simulation. Results are compared to control using instantaneous compensation and are characterized based on performance.
Teleoperation system using past image records (SPIR) is a system that shows the virtual trailing view using recorded images from previous times. In this system, the operator can control the robot intuitively by seeing the trailing view and the superimposed robot computer graphics (CG) model only using only a front facing camera on the robot. The preexisting time follower's vision was developed for wheeled vehicles. Legs may be more capable of dealing with uneven terrain and would also be capable of performing useful manipulation tasks. A legged robot with pneumatic actuators could also be capable of higher forces and thus be able to perform additional tasks. To operate a legged robot using SPIR, we have to consider many things, such as leg joint angles, contact of the feet with the ground, swing motion of the camera, and so forth. In this study, we propose a new user interface of SPIR for legged robot.
Pneumatic systems possess inherent compliance and potentially variable stiffness that make them an appealing actuator choice for tracking applications where contact and interaction are likely. However, good control of pneumatic systems is impeded by discontinuous and nonlinear dynamics, especially compliance and friction. The most successful previous solutions have either applied high-gain PD or sliding mode control. These achieve tracking control for compliant systems by transforming them into stiffer ones. Model predictive control can better balance precision tracking with compliance (low output impedance), so that the system is safer in case of collision disturbance. It can be coupled with a predictive observer that estimates friction as a known disturbance. The estimate is incorporated into the optimization, improving friction compensation for pneumatics, which has slow dynamics that do not react quickly enough with traditional feedforward compensation. Finally, predictive control enables constrained finite-time optimization, driving the system closer to its peak performance capability.
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