Running with compliant curved legs involves the progression of the center of pressure, the changes of both the leg's stiffness and effective rest length, and the shift of the location of the maximum stress point along the leg. These phenomena are product of the geometric and material properties of these legs, and the rolling motion produced during stance. We examine these aspects with several reduced-order dynamical models to relate the leg's design parameters (such as normalized foot radius, leg's effective stiffness, location of the maximum stress point and leg shape) to running performance (such as robustness and efficiency). By using these models, we show that running with compliant curved legs can be more efficient, robust with fast recovery behavior from perturbations than running with compliant straight legs. Moreover, the running performance can be further improved by tuning these design parameters in the context of running with rolling. The results shown in this work may serve as potential guidance for future compliant curved leg designs that may further improve the running performance.
A novel path-planning algorithm is proposed for a tracked mobile robot to traverse uneven terrains, which can efficiently search for stability sub-optimal paths. This algorithm consists of combining two RRT-like algorithms (the Transition-based RRT (T-RRT) and the Dynamic-Domain RRT (DD-RRT) algorithms) bidirectionally and of representing the robot-terrain interaction with the robot's quasi-static tip-over stability measure (assuming that the robot traverses uneven terrains at low speed for safety). The robot's stability is computed by first estimating the robot's pose, which in turn is interpreted as a contact problem, formulated as a linear complementarity problem (LCP), and solved using the Lemke's method (which guarantees a fast convergence). The present work compares the performance of the proposed algorithm to other RRT-like algorithms (in terms of planning time, rate of success in finding solutions and the associated cost values) over various uneven terrains and shows that the proposed algorithm can be advantageous over its counterparts in various aspects of the planning performance.
A skid-steering mobile robot steers by creating a moment that is larger than the frictional moment which results in a lateral slippage also known as skidding. This moment is in turn generated by a difference of the forces originated from the two sides of the robot. Tracking a given trajectory using this type of steering mechanism is not easy since it requires to relate skidding to steering. A necessary condition for the stability of skid-steering mobile robots is that the longitudinal component of the instantaneous center of rotation (ICR) resides within the robot dimension. In the present work, we propose a novel trajectory-tracking control design using a backstepping technique that guarantees the Lyapunov stability and that satisfies this necessary condition by relating the longitudinal component of the "desired ICR" to the curvature of a given trajectory and the reference linear speed. Finally, we compare the performance of the proposed controller to that of other existing controllers for skid-steering mobile robots and show the robustness of the proposed controller even in the presence of modeled sensory noise and control time delay in simulation. 53rd IEEE Conference on Decision and Control
The problem of improving the stability of a mobile manipulator over a sloped terrain is addressed in the present work. Such an improvement is achieved by finding the location of the manipulator's center of mass that maximizes the overall quasi-static stability, defined here as the force-angle stability, using a stochastic optimization approach known as the Covariance Matrix Adaptation. The tracking of both trajectories for the robot base and for the manipulator is achieved by using an inverse-kinematics controller in simulation.
A novel dynamic model-based trajectory tracking control law is proposed for a four-wheel differentially driven mobile robot using a backstepping technique that guarantees the Lyapunov stability. The present work improves the work of Caracciolo et al.[1], a dynamic feedback linearization approach, by reducing the number of required assumptions and the number of state terms. We also thoroughly investigate on a gain tuning procedure which is often overlooked for nonlinear controllers. Finally, the performance of the proposed controller is compared with the dynamic feedback linearization approach via simulation results which indicate that our controller is robust even in the presence of measurement noise and control time delay.
Most of the existing path planners for traversing over rough terrains use the single-valued probabilistic properties of the terrain with the extension of considering the robot's dimensions to build the cost function. The present work proposes a path planner for a tracked mobile robot to traverse over rough terrains using the robot's tip-over stability as its cost function. The contacts that the robot makes with the terrain determine the pose of the robot and in turn its tip-over stability. The estimation of the robot's pose is formulated as a linear complementary problem (LCP) and solved using the Lemke's method. We show some examples on searching paths that optimize for various cost functions over a randomly generated rough terrain. We also validate the performance of our pose estimator by comparing their results to those obtained from a dynamic simulator (MSC Adams).
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