Dynamic turning and running of a hexapod robot using a separated and laterally arranged two-leg model

We report on the development of separated and laterally arranged two-leg (SLTL) models with/without differentiated leg properties and their use as the dynamic running and turning templates for a hexapod robot. The laterally arranged two-leg morphology enables differential driving for turning. The differentiable leg settings, such as stiffness, enables the model to adopt unbalanced leg arrangements of empirical legged gaits, such as a tripod gait, into consideration. The fixed-point motion of the model was utilized as the main methodology to plan dynamic running and turning, in which the plot of one-step distance versus period was constructed for the legs' operation point selection and matching. The proposed methodology was experimentally validated using four indices: turning curvature, flight phase, motion stability, and energy efficiency. The experimental results show that the running robot using the SLTL model with differentiated leg stiffness has better energy efficiency than one without by 4%, while the latter model has identical performance to the original spring-loaded inverted pendulum model with rolling contact. As for turning, the robot using the SLTL models with/without differentiated leg stiffness can preserve dynamic turning in all experiments with turning curvatures up to0.28m-1and0.30m-1, respectively,33%and43%more than the robot using the original model-less phase-shift turning strategy (0.21 m-1). Using the proposed model-based strategy, the flight phase of the robot turning in all curvatures (including straight running) maintains around 20%, the root-mean-squared (RMS) values of pitch and roll remains less than3 deg, and the specific resistance (SR) is bounded between0.64 and 0.73. By contrast, the robot using the phase-shifting turning strategy can maintain dynamic motion up to a turning curvature of0.21 m-1. A further increase in phase shifting not only does not increase the turning curvature but also changes the robot motion from running to walking. In this case, no flight phase exists, theSRjumps up significantly, and RMS values of pitch and roll also increase dramatically. In short, the experimental validation confirms the effectiveness of the proposed methodology for initiating the dynamic running and turning of the robot.

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.