Leveraging Equivariances and Symmetries in the Control Barrier Function Synthesis

The synthesis of Control Barrier Functions (CBFs) often involves demanding computations or a meticulous construction. However, structural properties of the system dynamics and constraints have the potential to mitigate these challenges. In this paper, we explore how equivariances in the dynamics, loosely speaking a form of symmetry, can be leveraged in the CBF synthesis. Although CBFs are generally not inherently symmetric, we show how equivariances in the dynamics and symmetries in the constraints induce symmetries in CBFs derived through reachability analysis. This insight allows us to infer their CBF values across the entire domain from their values on a subset, leading to significant computational savings. Interestingly, equivariances can be even leveraged to the CBF synthesis for non-symmetric constraints. Specifically, we show how a partially known CBF can be leveraged together with equivariances to construct a CBF for various new constraints. Throughout the paper, we provide examples illustrating the theoretical findings. Furthermore, a numerical study investigates the computational gains from invoking equivariances into the CBF synthesis.

Generating and Optimizing Topologically Distinct Guesses for Mobile Manipulator Path Planning

Optimal path planning often suffers from getting stuck in a local optimum. This is often the case for mobile manipulators due to nonconvexities induced by obstacles and robot kinematics. This paper attempts to circumvent this issue by proposing a pipeline to obtain multiple distinct local optima. By evaluating and selecting the optimum among multiple distinct local optima, it is likely to obtain a closer approximation of the global optimum. We demonstrate this capability in optimal path planning of nonholonomic mobile manipulators in the presence of obstacles and subject to end effector path constraints. The nonholomicity, obstacles, and end effector path constraints often cause direct optimal path planning approaches to get stuck in local optima. We demonstrate that our pipeline is able to circumvent this issue and produce a final local optimum that is close to the global optimum.