Wilm Decré

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++.

This article proposes intent-based decision-making approaches for operator assistance in manipulation tasks, where the intent of the human operator is defined by the target of an ongoing reaching motion. These approaches start from a motion model database, which contains multiple motion models learned using Trajectory parameterized Probabilistic Principal Component Analysis. Often, such a database is constructed offline using supervised learning. Instead, this article proposes a semi-supervised and unsupervised online learning approach, able to encode new motion styles observed during execution. Then, the intended target of an ongoing reaching motion is predicted based on this motion model database, for which three probabilistic intent estimation approaches are proposed: 1) classification intent estimation; 2) prediction intent estimation; and 3) mixture intent estimation. In addition, a decision-making approach is introduced which determines whether the human operator will deviate from the task’s high-level plan based on the target probabilities obtained from intent estimation, such that an early intervention in the human’s task execution can be provided. This decision-making approach directly considers the target probabilities in its decision-making process, instead of only considering the most probable target, which makes it more adaptable to different situations and better at dealing with large uncertainties. The proposed methods were validated on the publicly available HA4M dataset.

This work presents Impact, a modular toolchain for the description, simulation, and deployment of Optimal Control Problems (OCP) and Model Predictive Control (MPC) strategies. Developed in Python with MATLAB bindings, Impact enables users to intuitively define system models, cost functions, and constraints using symbolic expressions. The toolchain integrates multiple transcription methods and nonlinear programming solvers, supporting rapid prototyping and seamless transition to deployment. Various artifacts can be automatically exported for simulation or embedded implementation. The capabilities and usability of the framework are illustrated through a nonlinear cart–pendulum example, highlighting its flexibility and efficiency in solving constrained control problems.

This paper proposes a novel and efficient optimization-based method for generating near time-optimal trajectories for holonomic vehicles navigating through complex but structured environments. The approach aims to solve the problem of motion planning for planar motion systems using magnetic levitation that can be used in assembly lines, automated laboratories or clean-rooms. In these applications, time-optimal trajectories that can be computed in real-time are required to increase productivity and allow the vehicles to be reactive if needed. The presented approach encodes the environment representation using free-space corridors and represents the motion of the vehicle through such a corridor using a motion primitive. These primitives are selected heuristically and define the trajectory with a limited number of degrees of freedom, which are determined in an optimization problem. As a result, the method achieves significantly lower computation times compared to the state-of-the-art, most notably solving a full Optimal Control Problem (OCP), OMG-tools or VP-STO without significantly compromising optimality within a fixed corridor sequence. The approach is benchmarked extensively in simulation and is validated on a real-world Beckhoff XPlanar system

This article describes a solution for pick-and-place tasks that do not require high precision throughout the execution, using a robotized gantry crane. Two common issues that occur when using a crane are addressed, and solutions are provided. First, the lack of rotational controllability is overcome by designing a gripper that passively aligns itself with the handle of the payload using a simple, robust control strategy. Second, the active workspace is expanded by using controlled, dynamic motions, based on a variable-length pendulum model. Thus, the workspace is no longer limited to positions directly accessible from above, as is the case with quasi-static control methods. The robustness and effectiveness of the proposed solution was validated by a grasping, and a shelf insertion experiment. The robot was able to grasp the payload during all 48 trials. Failures were detected and recovery strategies were implemented. The handle could also be reliably ungrasped. During the shelf insertion, imperfect trajectory tracking caused a significant error during the unobservable part of the trajectory. Nevertheless, the actual placement position was always close to the desired position.

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.