Optimal Control of Robotic Systems and Biased Riemannian Splines

In this paper, we study mechanical optimal control problems on a given Riemannian manifold $(Q,g)$ in which the cost is defined by a general cometric $\tilde{g}$. This investigation is motivated by our studies in robotics, in which we observed that the mathematically natural choice of cometric $\tilde{g} = g^{*}$ -- the dual of $g$ -- does not always capture the true cost of the motion. We then, first, discuss how to encode the system's torque-based actuators configuration into a cometric $\tilde{g}$. Second, we provide and prove our main theorem, which characterizes the optimal solutions of the problem associated to general triples $(Q, g, \tilde{g})$ in terms of a 4th order differential equation. We also identify a tensor appearing in this equation as the geometric source of "biasing" of the solutions away from ordinary Riemannian splines and geodesics for $(Q, g)$. Finally, we provide illustrative examples and practical demonstration of the biased splines as providing the true optimizers in a concrete robotics system

Geometric Gait Optimization for Inertia-Dominated Systems With Nonzero Net Momentum

Inertia-dominated mechanical systems can achieve net displacement by 1) periodically changing their shape (known as kinematic gait) and 2) adjusting their inertia distribution to utilize the existing nonzero net momentum (known as momentum gait). Therefore, finding the gait that most effectively utilizes the two types of locomotion in terms of the magnitude of the net momentum is a significant topic in the study of locomotion. For kinematic locomotion with zero net momentum, the geometry of optimal gaits is expressed as the equilibria of system constraint curvature flux through the surface bounded by the gait, and the cost associated with executing the gait in the metric space. In this paper, we identify the geometry of optimal gaits with nonzero net momentum effects by lifting the gait description to a time-parameterized curve in shape-time space. We also propose the variational gait optimization algorithm corresponding to the lifted geometric structure, and identify two distinct patterns in the optimal motion, determined by whether or not the kinematic and momentum gaits are concentric. The examples of systems with and without fluid-added mass demonstrate that the proposed algorithm can efficiently solve forward and turning locomotion gaits in the presence of nonzero net momentum. At any given momentum and effort limit, the proposed optimal gait that takes into account both momentum and kinematic effects outperforms the reference gaits that each only considers one of these effects.Comment: 8 pages, 9 figures, accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 202

Towards Geometric Motion Planning for High-Dimensional Systems: Gait-Based Coordinate Optimization and Local Metrics

Geometric motion planning offers effective and interpretable gait analysis and optimization tools for locomoting systems. However, due to the curse of dimensionality in coordinate optimization, a key component of geometric motion planning, it is almost infeasible to apply current geometric motion planning to high-dimensional systems. In this paper, we propose a gait-based coordinate optimization method that overcomes the curse of dimensionality. We also identify a unified geometric representation of locomotion by generalizing various nonholonomic constraints into local metrics. By combining these two approaches, we take a step towards geometric motion planning for high-dimensional systems. We test our method in two classes of high-dimensional systems - low Reynolds number swimmers and free-falling Cassie - with up to 11-dimensional shape variables. The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model. Furthermore, we provide a geometric optimality interpretation of the optimal gait.Comment: 7 pages, 6 figures, submitted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024

Optimal Gait Families using Lagrange Multiplier Method

The robotic locomotion community is interested in optimal gaits for control. Based on the optimization criterion, however, there could be a number of possible optimal gaits. For example, the optimal gait for maximizing displacement with respect to cost is quite different from the maximum displacement optimal gait. Beyond these two general optimal gaits, we believe that the optimal gait should deal with various situations for high-resolution of motion planning, e.g., steering the robot or moving in "baby steps." As the step size or steering ratio increases or decreases, the optimal gaits will slightly vary by the geometric relationship and they will form the families of gaits. In this paper, we explored the geometrical framework across these optimal gaits having different step sizes in the family via the Lagrange multiplier method. Based on the structure, we suggest an optimal locus generator that solves all related optimal gaits in the family instead of optimizing each gait respectively. By applying the optimal locus generator to two simplified swimmers in drag-dominated environments, we verify the behavior of the optimal locus generator.Comment: 6 page

Plant design for deterministic control of STEMS and tale-springs

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (leaf 54).In this thesis, the limits of conventional linear actuators for long stroke applications are discussed, and tape-spring based actuators such as the STEM are introduced as an alternative solution. While the literature contains several assessments of self-deploying tape-springs, little exists in the area of closed loop deterministic control of such mechanisms. This thesis adapts the existing models of tape springs to form a framework for the study of closed loop controllable tape springs. Included is an evaluation of the validity of the prevailing first order model for a coiled tape-spring. Lastly, several avenues for future research are suggested.by Ross L. Hatton.S.B

Geometric Mechanics of Contact-Switching Systems

Discrete and periodic contact switching is a key characteristic of steady state legged locomotion. This paper introduces a framework for modeling and analyzing this contact-switching behavior through the framework of geometric mechanics on a toy robot model that can make continuous limb swings and discrete contact switches. The kinematics of this model forms a hybrid shape space and by extending the generalized Stokes' theorem to compute discrete curvature functions called stratified panels, we determine average locomotion generated by gaits spanning multiple contact modes. Using this tool, we also demonstrate the ability to optimize gaits based on system's locomotion constraints and perform gait reduction on a complex gait spanning multiple contact modes to highlight the scalability to multilegged systems.Comment: 6 pages, 7 figures, and link to associated video: https://drive.google.com/file/d/12Sgl0R1oDLDWRrqlwwAt3JR2Gc3rEB4T/view?usp=sharin