Ibrahim Ibrahim

We present an effective solution for computing locally optimal Reeds-Shepp distances and paths for kinematically-constrained vehicles in environments represented as obstacle-rich 2D Cartesian occupancy grids, addressing the reliance of current methods on discretization and approximation techniques. Our solution leverages a visibility-based marching architecture with continuous analytical expressions for propagating the Reeds-Shepp distance function. We introduce a model to identify reachable and unreachable regions for Reeds-Shepp vehicles, accompanied by a comprehensive representation of the Reeds-Shepp distance function in both cases. Unlike existing approaches, our method computes locally optimal distances and smooth paths globally without discretizing vehicle orientations, motion primitives, or the PDE, and without gradient descent (GD) backtracking, ensuring both accuracy and computational efficiency. Extensive simulations in various environments demonstrate effective improvements over state-of-the-art methods, particularly in complex obstacle-rich scenarios. To facilitate adoption, we provide an open-source solver implemented in C++.

In this study, we present a simple and intuitive method for accelerating optimal Reeds-Shepp path computation. Our approach uses geometrical reasoning to analyze the behavior of optimal paths, resulting in a new partitioning of the state space and a further reduction in the minimal set of viable paths. We revisit and reimplement classic methodologies from the literature, which lack contemporary open-source implementations, to serve as benchmarks for evaluating our method. Additionally, we address the under-specified Reeds-Shepp planning problem where the final orientation is unspecified. We perform exhaustive experiments to validate our solutions. Compared to the modern C++ implementation of the original Reeds-Shepp solution in the Open Motion Planning Library, our method demonstrates a $15\times$ speedup, while classic methods achieve a $5.79\times$ speedup. Both approaches exhibit machine-precision differences in path lengths compared to the original solution. We release our proposed C++ implementations for both the accelerated and under-specified Reeds-Shepp problems as open-source code.

This letter introduces an efficient $\mathcal {O}(n)$ compute and memory complexity algorithm for globally optimal path planning on 2D Cartesian grids. Unlike existing marching methods that rely on approximate discretized solutions to the Eikonal equation, our approach achieves exact wavefront propagation by pivoting the analytic distance function based on visibility. The algorithm leverages a dynamic-programming subroutine to efficiently evaluate visibility queries. Through benchmarking against state-of-the-art any-angle path planners, we demonstrate that our method outperforms existing approaches in both speed and accuracy, particularly in cluttered environments. Notably, our method inherently provides globally optimal paths to all grid points, eliminating the need for additional gradient descent steps per path query. The same capability extends to multiple starting positions. We also provide a greedy version of our algorithm as well as open-source C++ implementation of our solver.

This paper introduces a novel approach for whole-body motion planning and dynamic occlusion avoidance. The proposed approach reformulates the visibility constraint as a likelihood maximization of visibility probability. In this formulation, we augment the primary cost function of a whole-body model predictive control scheme through a relaxed log barrier function yielding a relaxed log-likelihood maximization formulation of visibility probability. The visibility probability is computed through a probabilistic shadow field that quantifies point light source occlusions. We provide the necessary algorithms to obtain such a field for both 2D and 3D cases. We demonstrate 2D implementations of this field in simulation and 3D implementations through real-time hardware experiments. We show that due to the linear complexity of our shadow field algorithm to the map size, we can achieve high update rates, which facilitates onboard execution on mobile platforms with limited computational power. Lastly, we evaluate the performance of the proposed MPC reformulation in simulation for a quadrupedal mobile manipulator.