16 accepted papers
Classical methods in robot motion planning, such as sampling-based and optimization-based methods, often struggle with scalability towards higher-dimensional state spaces and complex environments. Diffusion models, known for their capability to learn complex, high-dimensional and multi-modal data di…
We present a complete framework for fast motion planning of non-holonomic autonomous mobile robots in highly complex but structured environments. Conventional grid-based planners struggle with scalability, while many kinematically-feasible planners impose a significant computational burden due to th…
Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formu
Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combi
This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of lines-of-sight from a source point to all other grid cells in a single pass with no preprocessing and independently of the number and shape of obstacles. It…
This letter introduces an efficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(n)$</tex-math></inline-formula> compute and memory complexity algorithm for globally optimal path planning on 2D Cart
We present PV-OSIMr, an efficient algorithm for computing the Delassus matrix (also known as the inverse operational space inertia matrix) for a kinematic tree, with the lowest order computational complexity known in literature. PV-OSIMr is derived by optimizing the recently proposed PV-OSIM algorit
This paper presents a robustified time-optimal motion planning approach for navigating an Autonomous Mobile Robot (AMR) from an initial state to a terminal state without colliding with obstacles, even when subjected to disturbances, which are modeled as random process noise and measurement noise. Th…
Fast and safe manipulation of flexible objects with a robot manipulator necessitates measures to cope with vibrations. Existing approaches either increase the task execution time or require complex models and/or additional instrumentation to measure vibrations. This paper develops a model-based meth…
Trajectory optimization is a powerful tool for robot motion planning and control. State-of-the-art general-purpose nonlinear programming solvers are versatile, handle constraints effectively and provide a high numerical robustness, but they are slow because they do not fully exploit the optimal cont…
The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal control problem (OCP). The convex-over-nonlinear structu
We present Tasho (Task specification for receding horizon control), an open-source Python toolbox that facilitates systematic programming of optimal control problem (OCP)-based robot motion skills. Separation-of-concerns is followed while designing the components of a motion skill, which promotes th…
Extensive work has been done on efficiently resolving hierarchical robot task specifications that minimize the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -2 norm of linear constraint violations, but not for <i xmlns:mml="http://www.w3.org/1998/
Constraint-based robot programming allows for implementing sensor-based skills that react to disturbances on the one hand, and composable skills that dynamically create and reconfigure complex robot behaviors on the other hand. In this letter, we address a class of problems where composing appropria
Variation in task requirements and operation in uncertain environments require robots that can continuously adapt their motions during execution. To do this successfully, the original motion characteristics must be preserved. This paper presents an approach for on-line adaptation of end-effector tra
This paper introduces a constraint-based skill framework for programming robot applications. Existing skill frameworks allow application developers to reuse skills and compose them sequentially or in parallel. However, they typically assume that the skills are running independently and in a nominal…