Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.

In AI planning, it is common to distinguish between planning domains and problem instances, where a "domain" is generally understood as a set of related problem instances. This distinction is important, for example, in generalised planning, which aims to find a single, general plan or policy that solves all instances of a given domain. In PDDL, domains and problem instances are clearly separated: the domain defines the types, predicate symbols, and action schemata, while the problem instance specifies the concrete set of (typed) objects, the initial state, and the goal condition. In this paper, we show that it is quite easy to define a PDDL domain such that any propositional planning problem instance, from any domain, becomes an instance of this (lifted) "universal" domain. We construct different formulations of the universal domain, and discuss their implications for the complexity of lifted domain-dependent or generalised planning.

A dense SLAM system is essential for mobile robots, as it provides localization and allows navigation, path planning, obstacle avoidance, and decision-making in unstructured environments. Due to increasing computational demands the use of GPUs in dense SLAM is expanding. In this work, we present coVoxSLAM, a novel GPU-accelerated volumetric SLAM system that takes full advantage of the parallel processing power of the GPU to build globally consistent maps even in large-scale environments. It was deployed on different platforms (discrete and embedded GPU) and compared with the state of the art. The results obtained using public datasets show that coVoxSLAM delivers a significant performance improvement considering execution times while maintaining accurate localization. The presented system is available as open-source on GitHub https://github.com/lrse-uba/coVoxSLAM.

Narrow passage path planning is a prevalent problem from industrial to household sites, often facing difficulties in finding feasible paths or requiring excessive computational resources. Given that deep penetration into the environment can cause optimization failure, we propose a framework to ensure feasibility throughout the process using a series of subproblems tailored for narrow passage problem. We begin by decomposing the environment into convex objects and initializing collision constraints with a subset of these objects. By continuously interpolating the collision constraints through the process of sequentially introducing remaining objects, our proposed framework generates subproblems that guide the optimization toward solving the narrow passage problem. Several examples are presented to demonstrate how the proposed framework addresses narrow passage path planning problems.

Optimal path planning often suffers from getting stuck in a local optimum. This is often the case for mobile manipulators due to nonconvexities induced by obstacles and robot kinematics. This paper attempts to circumvent this issue by proposing a pipeline to obtain multiple distinct local optima. By evaluating and selecting the optimum among multiple distinct local optima, it is likely to obtain a closer approximation of the global optimum. We demonstrate this capability in optimal path planning of nonholonomic mobile manipulators in the presence of obstacles and subject to end effector path constraints. The nonholomicity, obstacles, and end effector path constraints often cause direct optimal path planning approaches to get stuck in local optima. We demonstrate that our pipeline is able to circumvent this issue and produce a final local optimum that is close to the global optimum.

In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framework for automated planning of explanations for robot navigation, where the preferences of different users regarding explanations are probabilistically modeled to tailor the stochasticity of the real-world human-robot interaction and the communication of decisions of the robot and its actions towards humans. This approach aims to enhance the transparency of robot path planning and adapt to diverse user explanation needs by anticipating the types of explanations that will satisfy individual users.

Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations. Most ZS-ICL methods use large language models (LLMs) to generate (input, label) pairs as pseudo-demonstrations and leverage historical pseudo-demonstrations to help solve the current problem. They assume that problems are from the same task and traverse them in a random order. However, in real-world scenarios, problems usually come from diverse tasks, and only a few belong to the same task. The random traversing order may generate unreliable pseudo-demonstrations and lead to error accumulation. To address this problem, we reformulate ZS-ICL as a planning problem and propose a Demonstration-aware Monte Carlo Tree Search (MCTS) approach (DAWN-ICL), which leverages MCTS to strategically plan the problem-solving trajectories for ZS-ICL. In addition, to achieve effective and efficient Q value estimation, we propose a novel demonstration-aware Q-value function and use it to enhance the selection phase and accelerate the expansion and simulation phases in MCTS. Extensive experiments demonstrate the effectiveness and efficiency of DAWN-ICL on in-domain and cross-domain scenarios, and it even outperforms ICL using human-annotated labels. The code is available at https://github.com/RUCAIBox/MCTS4ZSICL.

Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lower bound of the final path cost as informed heuristics for fast path-finding. These heuristics guide a search tree to explore the roadmap during runtime. The search tree examines the edges using a fuzzy collision checking concerning the dynamic environment. Finally, the heuristics tree exploits knowledge fed back from the fuzzy collision checking module and updates the lower bound for the path cost. As we demonstrate in real-world experiments, the closed-loop formed by these three components significantly accelerates the planning procedure. An additional backtracking step ensures the feasibility of the resulting paths. Experiments in simulation and the real world show that HIRO can find collision-free paths considerably faster than baseline methods with and without prior knowledge of the environment.

In this work, we present FRTree planner, a novel robot navigation framework that leverages a tree structure of free regions, specifically designed for navigation in cluttered and unknown environments with narrow passages. The framework continuously incorporates real-time perceptive information to identify distinct navigation options and dynamically expands the tree toward explorable and traversable directions. This dynamically constructed tree incrementally encodes the geometric and topological information of the collision-free space, enabling efficient selection of the intermediate goals, navigating around dead-end situations, and avoidance of dynamic obstacles without a prior map. Crucially, our method performs a comprehensive analysis of the geometric relationship between free regions and the robot during online replanning. In particular, the planner assesses the accessibility of candidate passages based on the robot's geometries, facilitating the effective selection of the most viable intermediate goals through accessible narrow passages while minimizing unnecessary detours. By combining the free region information with a bi-level trajectory optimization tailored for robots with specific geometries, our approach generates robust and adaptable obstacle avoidance strategies in confined spaces. Through extensive simulations and real-world experiments, FRTree demonstrates its superiority over benchmark methods in generating safe, efficient motion plans through highly cluttered and unknown terrains with narrow gaps.

Abstract-- Path planning in a changing environment is a challenging task in robotics, as moving objects impose timedependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given timedependent constraints while being goal-oriented. Modern robotic applications aim to place the robots into a collaborative environment rather than in a confined workstation, Figure 1: Planning with learned subgoals: with the estimated e.g.