trajectory simulation

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This work addresses the “execution gap” between high-level semantic tasks and executable robot motions by introducing Motion Statecharts—a symbolic, executable motion representation that supports concurrency and hierarchical nesting. Coupled with a unified differentiable kinematic world model, this framework enables end-to-end mapping from semantic task specifications to low-level motion control. Smooth and dynamically feasible trajectories are generated through a linear model predictive control (lMPC)-driven task-function approach incorporating snap (jerk derivative) constraints. The proposed system has been successfully deployed across eight heterogeneous robotic platforms, demonstrating strong cross-platform generalization and real-world efficacy. The accompanying software framework, Giskard, has been publicly released.

To address the slow initial solution generation and challenging online optimization in robotic end-effector trajectory tracking, this paper proposes an anytime planning framework capable of interruption and continuous refinement. Methodologically, it integrates kinematic modeling, heuristic sampling, and real-time replanning to reformulate two mainstream algorithms—A* and RRT*. Its key contributions are: (1) the first adaptation of graph-search algorithms to an anytime paradigm; and (2) a guided-path-based directional deviation sampling strategy that jointly optimizes initial solution speed and progressive accuracy improvement. Experimental evaluation across three benchmark scenarios demonstrates an average 3.2× reduction in time-to-first-solution, a 37% decrease in trajectory tracking error, and significantly enhanced convergence stability.

This work proposes an online trajectory generation method based on piecewise quintic/quartic splines to address the challenge of converting arbitrary geometric paths into kinematically feasible and collision-free trajectories in dynamic environments. The approach explicitly enforces jerk constraints and supports real-time replanning under high-frequency goal updates. By integrating dynamic environment perception and a responsive adaptation mechanism, it guarantees collision avoidance within finite time while permitting bounded deviations from the original path. Both simulation and real-world experiments demonstrate that the method outperforms existing approaches in trajectory smoothness, computational efficiency, and real-time performance, achieving stable operation in human-in-the-loop dynamic scenarios with target update rates up to 1 kHz.

This work addresses the problem of generating physically consistent motion trajectories and inferring executable action sequences from state-only demonstrations—i.e., without action labels. To this end, we propose DynaFlow, the first method embedding a differentiable physics simulator into a flow-matching framework to enable end-to-end mapping from observed state sequences to physically feasible trajectories and their corresponding actions. Its core innovation lies in incorporating differentiable dynamics constraints directly into the generative process, ensuring inherent physical plausibility while enabling implicit action inversion and long-horizon open-loop motion generation. By jointly optimizing both the latent state-space trajectory and the generative model, DynaFlow successfully reproduces diverse gaits on the Go1 quadrupedal robot, transforming kinematically infeasible demonstrations into dynamically executable behaviors. Experimental validation on real hardware demonstrates its effectiveness, robustness, and generalization capability across locomotion tasks.

This study addresses the absence of a unified and widely accepted formalism for specifying robotic tasks, which hinders non-experts from defining single- or multi-robot missions in complex, dynamic environments. For the first time, it systematically compares four prominent task specification paradigms—Behavior Trees, Finite State Machines, Hierarchical Task Networks (HTN), and Business Process Model and Notation (BPMN)—from the perspective of task-level description. The evaluation focuses on expressiveness, control structures, tooling support, and integration with human workflows. Through expert validation, the work clarifies the strengths, limitations, and suitable application contexts of each approach, offering researchers and practitioners a principled basis for method selection to enhance the robustness and adaptability of robotic task systems.

This study addresses the safety and precision challenges in automated slewing control of knuckle-boom cranes caused by payload oscillations. The authors propose an open-loop slewing trajectory generation method driven purely by behavioral input–output data, circumventing the need for explicit system modeling. Leveraging Willems’ behavioral theory and its extended formulations, the approach enables a non-parametric characterization of the underactuated system’s dynamics and generates smooth, optimal trajectories via convex optimization. Compared to conventional model-based strategies, the proposed method substantially reduces reliance on expert knowledge and large datasets. Experimental results demonstrate a 35% reduction in payload swing, a 43% decrease in tracking error, and a 50% shortening of execution time, highlighting its efficacy and practicality in real-world crane automation.

This work addresses the challenge of coordinating multi-object task scheduling and motion planning in shared workspaces, where both temporal-spatial and resource constraints must be jointly satisfied to ensure safe and efficient execution. The paper proposes an incremental closed-loop framework that, for the first time, integrates off-the-shelf schedulers with sampling-based motion planners through a symbolic spatiotemporal abstraction. In this framework, the scheduler generates candidate plans, which are then verified by the motion planner for continuous-motion feasibility; the planner returns symbolic conflict information—such as spatial interference or required temporal adjustments—to guide the scheduler’s iterative refinement. Evaluated on logistics and job-shop benchmarks, the approach significantly improves the feasibility and efficiency of multi-agent cooperative planning, achieving effective synergy between discrete task scheduling and continuous motion execution.

This work addresses the challenge of generating constrained, interpretable, and domain-compliant trajectory patterns for moving objects in real-world dynamic environments. It proposes a hybrid qualitative-quantitative approach based on Answer Set Programming (ASP), which traverses the environmental graph structure and integrates geometric constraint reasoning with stable model semantics to enumerate geometrically feasible motion behaviors. To the best of our knowledge, this is the first application of ASP to generate diverse trajectory patterns that are verifiable, traceable, and seamlessly incorporate domain knowledge with environmental topology. Experiments on the large-scale Argoverse 2 autonomous driving benchmark demonstrate that the generated trajectories exhibit high interpretability and practical applicability, effectively overcoming the limited explainability inherent in purely data-driven methods.