Abstract--Coverage motion planning is essential to a wide range of robotic tasks. Unlike conventional motion planning problems, which reason over temporal sequences of states, coverage motion planning requires reasoning over the spatial distribution of entire trajectories, making standard motion planning methods limited in computational efficiency and less amenable to modern parallelization frameworks. In this work, we formulate the coverage motion planning problem as a statistical inference problem from the perspective of flow matching, a generative modeling technique that has gained significant attention in recent years. The proposed formulation unifies commonly used statistical discrepancy measures, such as Kullback-Leibler divergence and Sinkhorn divergence, with a standard linear quadratic regulator problem. This paper focuses on the advantages of this formulation in terms of scalability through parallelization, highlighting its computational benefits compared to conventional methods based on waypoint tracking.

Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous motion planning algorithms. However, existing frameworks often struggle to deliver the level of predictability and repeatability demanded by high-stakes applications -- ranging from ensuring safety in industrial environments to the creation of high-quality motion datasets for robot learning. Complementing existing tools, we introduce SRMP (Search-based Robot Motion Planning), a new software framework tailored for robotic manipulation. SRMP distinguishes itself by generating consistent and reliable trajectories, and is the first software tool to offer motion planning algorithms for multi-robot manipulation tasks. SRMP easily integrates with major simulators, including MuJoCo, Sapien, Genesis, and PyBullet via a Python and C++ API. SRMP includes a dedicated MoveIt! plugin that enables immediate deployment on robot hardware and seamless integration with existing pipelines. Through extensive evaluations, we demonstrate in this paper that SRMP not only meets the rigorous demands of industrial and safety-critical applications but also sets a new standard for consistency in motion planning across diverse robotic systems. Visit srmp.readthedocs.io for SRMP documentation and tutorials.

Randomized methods such as PRM and RRT are widely used in motion planning. However, in some cases, their running-time suffers from inherent instability, leading to ``catastrophic'' performance even for relatively simple instances. We apply stochastic restart techniques, some of them new, for speeding up Las Vegas algorithms, that provide dramatic speedups in practice (a factor of $3$ [or larger] in many cases). Our experiments demonstrate that the new algorithms have faster runtimes, shorter paths, and greater gains from multi-threading (when compared with straightforward parallel implementation). We prove the optimality of the new variants. Our implementation is open source, available on github, and is easy to deploy and use.

We propose a constrained Affine Geometric Heat Flow (AGHF) method that evolves so as to suppress the dynamics gaps associated with inadmissible control directions. AGHF provides a unified framework applicable to a wide range of motion planning problems, including both holonomic and non-holonomic systems. However, to generate admissible trajectories, it requires assigning infinite penalties to inadmissible control directions. This design choice, while theoretically valid, often leads to high computational cost or numerical instability when the penalty becomes excessively large. To overcome this limitation, we extend AGHF in an Augmented Lagrangian method approach by introducing a dual trajectory related to dynamics gaps in inadmissible control directions. This method solves the constrained variational problem as an extended parabolic partial differential equation defined over both the state and dual trajectorys, ensuring the admissibility of the resulting trajectory. We demonstrate the effectiveness of our algorithm through simulation examples.

--We present a tool A UTONA Vthat automates the mapping, localization, and path-planning tasks for autonomous navigation of robots. The modular architecture allows easy integration of various algorithms for these tasks for comparison. We present the generated maps and path-plans by A UTONA Vin indoor simulation scenarios. I NTRODUCTION Autonomous navigation in robotics is of central importance in search and rescue operations [1], warehouse automation [2], surveillance in hazardous environments [3] etc. Perception, mapping, localization, path-planning, and control are the key tasks necessary for autonomous navigation. We focus on designing a generic navigation framework for autonomous robots while the main concern remains in addressing the motion planning problem given the robot dynamics.

This paper proposes a bidirectional rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. The proposed algorithm, called HyRRT-Connect, propagates in both forward and backward directions in hybrid time until an overlap between the forward and backward propagation results is detected. Then, HyRRT-Connect constructs a motion plan through the reversal and concatenation of functions defined on hybrid time domains, ensuring that the motion plan satisfies the given hybrid dynamics. To address the potential discontinuity along the flow caused by tolerating some distance between the forward and backward partial motion plans, we reconstruct the backward partial motion plan by a forward-in-hybrid-time simulation from the final state of the forward partial motion plan. effectively eliminating the discontinuity. The proposed algorithm is applied to an actuated bouncing ball system and a walking robot example to highlight its computational improvement.

Motion planning for aerial manipulators in constrained environments has typically been limited to known environments or simplified to that of multi-rotors, which leads to poor adaptability and overly conservative trajectories. This paper presents RINGO: Real-time Navigation with a Guiding Trajectory, a novel planning framework that enables aerial manipulators to navigate unknown environments in real time. The proposed method simultaneously considers the positions of both the multi-rotor and the end-effector. A pre-obtained multi-rotor trajectory serves as a guiding reference, allowing the end-effector to generate a smooth, collision-free, and workspace-compatible trajectory. Leveraging the convex hull property of B-spline curves, we theoretically guarantee that the trajectory remains within the reachable workspace. To the best of our knowledge, this is the first work that enables real-time navigation of aerial manipulators in unknown environments. The simulation and experimental results show the effectiveness of the proposed method. The proposed method generates less conservative trajectories than approaches that consider only the multi-rotor.

This paper describes two C++/Open Motion Planning Library implementations of the recently developed motion planning algorithms HyRRT arXiv:2210.15082v1 [cs.RO] and HySST arXiv:2305.18649v1 [cs.RO]. Specifically, cHyRRT, an implementation of the HyRRT algorithm, is capable of generating a solution to a motion planning problem for hybrid systems with probabilistically completeness, while cHySST, an implementation of the asymptotically near-optimal HySST algorithm, is capable of computing a trajectory to solve the optimal motion planning problem for hybrid systems. cHyRRT is suitable for motion planning problems where an optimal solution is not required, whereas cHySST is suitable for such problems that prefer optimal solutions, within all feasible solutions. The structure, components, and usage of the two tools are described. Examples are included to illustrate the main capabilities of the toolbox.

With the development of science and technology, mobile robots are playing a significant important role in the new round of world revolution. Further, mobile robots might assist or replace human beings in a great number of areas. To increase the degree of automation for mobile robots, advanced motion planners need to be integrated into them to cope with various environments. Complex maze environments are common in the potential application scenarios of different mobile robots. This article proposes a novel motion planner named the rapidly exploring random tree based Gaussian process motion planner 2, which aims to tackle the motion planning problem for mobile robots in complex maze environments. To be more specific, the proposed motion planner successfully combines the advantages of a trajectory optimisation motion planning algorithm named the Gaussian process motion planner 2 and a sampling-based motion planning algorithm named the rapidly exploring random tree. To validate the performance and practicability of the proposed motion planner, we have tested it in several simulations in the Matrix laboratory and applied it on a marine mobile robot in a virtual scenario in the Robotic operating system.

During recent years, unmanned surface vehicles are extensively utilised in a variety of maritime applications such as the exploration of unknown areas, autonomous transportation, offshore patrol and others. In such maritime applications, unmanned surface vehicles executing relevant missions that might collide with potential static obstacles such as islands and reefs and dynamic obstacles such as other moving unmanned surface vehicles. To successfully accomplish these missions, motion planning algorithms that can generate smooth and collision-free trajectories to avoid both these static and dynamic obstacles in an efficient manner are essential. In this article, we propose a novel motion planning algorithm named the Dynamic Gaussian process motion planner 2, which successfully extends the application scope of the Gaussian process motion planner 2 into complex and dynamic environments with both static and dynamic obstacles. First, we introduce an approach to generate safe areas for dynamic obstacles using modified multivariate Gaussian distributions. Second, we introduce an approach to integrate real-time status information of dynamic obstacles into the modified multivariate Gaussian distributions. Therefore, the multivariate Gaussian distributions with real-time statuses of dynamic obstacles can be innovatively added into the optimisation process of factor graph to generate an optimised trajectory. The proposed Dynamic Gaussian process motion planner 2 algorithm has been validated in a series of benchmark simulations in the Matrix laboratory and a dynamic obstacle avoidance mission in a high-fidelity maritime environment in the Robotic operating system to demonstrate its functionality and practicability.