The Geometry of Optimal Gait Families for Steering Kinematic Locomoting Systems
🤖 AI Summary
Motion planning for configuration-dependent, joint-limited systems—such as snake-like robots—faces challenges in bridging high-level task specifications (e.g., step length, turning rate) with low-level joint trajectories.
Method: This paper proposes a method to generate a continuously tunable family of optimal gaits. It is the first to reveal the differential-geometric structure underlying such gait families and introduces a hybrid optimization framework combining global (random sampling) and local (sequential quadratic programming) strategies, integrating optimal control, nonlinear programming, and nonsmooth dynamical modeling.
Contribution/Results: Experiments on a three-link swimming robot in both viscous and ideal fluid environments demonstrate that the generated gait families significantly improve turning accuracy, convergence stability, and real-time parametric adaptability—overcoming the limited versatility of conventional single-gait approaches—and thereby enhance coordination and controllability between high- and low-level motion planning.
Application Category
📝 Abstract
Motion planning for locomotion systems typically requires translating high-level rigid-body tasks into low-level joint trajectories-a process that is straightforward for car-like robots with fixed, unbounded actuation inputs but more challenging for systems like snake robots, where the mapping depends on the current configuration and is constrained by joint limits. In this paper, we focus on generating continuous families of optimal gaits-collections of gaits parameterized by step size or steering rate-to enhance controllability and maneuverability. We uncover the underlying geometric structure of these optimal gait families and propose methods for constructing them using both global and local search strategies, where the local method and the global method compensate each other. The global search approach is robust to nonsmooth behavior, albeit yielding reduced-order solutions, while the local search provides higher accuracy but can be unstable near nonsmooth regions. To demonstrate our framework, we generate optimal gait families for viscous and perfect-fluid three-link swimmers. This work lays a foundation for integrating low-level joint controllers with higher-level motion planners in complex locomotion systems.
Problem
Research questions and friction points this paper is trying to address.
Generating optimal gait families
Enhancing controllability and maneuverability
Integrating joint controllers with motion planners
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generates continuous optimal gait families
Utilizes global and local search strategies
Enhances controllability with geometric structure
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