Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally efficient, but achieving both can be challenging due to their conflicting nature. This paper proposes Direction Informed Trees (DIT*), a sampling-based planner that focuses on optimizing the search direction for each edge, resulting in goal bias during exploration. We define edges as generalized vectors and integrate similarity indexes to establish a directional filter that selects the nearest neighbors and estimates direction costs. The estimated direction cost heuristics are utilized in edge evaluation. This strategy allows the exploration to share directional information efficiently. DIT* convergence faster than existing single-query, sampling-based planners on tested problems in$\mathbb{R}^{4}$to$\mathbb{R}^{16}$and has been demonstrated in real-world environments with various planning tasks. A video showcasing our experimental results is available at: https://youtu.be/2SX6QT2NOek.

Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and adaptive grasp transitions remains a challenge, particularly when dealing with external forces and complex motion constraints. Existing grasp transition strategies often fail to account for varying external forces and do not optimize motion performance effectively. In this work, we propose an Imitation-Guided Bimanual Planning Framework that integrates efficient grasp transition strategies and motion performance optimization to enhance stability and dexterity in robotic manipulation. Our approach introduces Strategies for Sampling Stable Intersections in Grasp Manifolds for seamless transitions between uni-manual and bi-manual grasps, reducing computational costs and regrasping inefficiencies. Additionally, a Hierarchical Dual-Stage Motion Architecture combines an Imitation Learning-based Global Path Generator with a Quadratic Programming-driven Local Planner to ensure real-time motion feasibility, obstacle avoidance, and superior manipulability. The proposed method is evaluated through a series of force-intensive tasks, demonstrating significant improvements in grasp transition efficiency and motion performance. A video demonstrating our simulation results can be viewed at https://youtu.be/3DhbUsv4eDo.

Robotic motion planning faces formidable challenges in constrained environments, particularly in rapidly searching for feasible solutions and converging towards optimal. This study introduces Multi-Sets Tree (MST*), a sampling-based planner designed to accelerate path searching and solution optimization. MST* integrates estimated guided incremental local densification (GuILD) sets that are based on prior estimated solution costs before finding the initial solution. For path optimization, MST* integrates novel beacon selectors to define problem subsets, thereby guiding exploration and effectively exploiting high-potential areas. This multi-set strategy ensures balanced exploration and exploitation, enabling MST* to handle sparse free space. Moreover, MST* utilizes adaptive sampling techniques via Lebesgue’s measure of domain subsets for rapid search. MST* improves search efficiency and path optimality, particularly in constrained high-dimensional environments. It extends the informed sampling concept by refining the search region and batch sampling. Experimental results demonstrate that MST* outperforms single-query planners across ℝ4to ℝ16benchmarks and in real-world robotic navigation tasks. A video showcasing our experimental results is available at: https://youtu.be/obftvS0a41M.

This paper introduces Context Informed Trees (CIT*), a sampling-based motion planning algorithm that enhances exploration efficiency by biasing sampling based on uncertainty estimation from local samples and connectivity information obtained during the search process. CIT* is based on Flexible Informed Trees (FIT*) and incorporates three key components: region-based sampling, uncertainty-driven weighting, and connection-greedy prioritization (CGP). It generates regions from sampled states based on local obstacle proximity, assigning weights to these regions using probability uncertainty estimation via kernel density estimation (KDE) classification. To further refine the sampling focus, CGP prioritizes regions that exhibit strong connectivity in previous searches, ensuring that exploration is directed toward unknown and critical areas that have a higher likelihood of contributing to feasible and efficient paths. The sampling process is then guided by a mixture of Gaussian distributions centered on weighted regions, where the weighting biases sampling toward more critical regions, thereby improving search efficiency and accelerating convergence. Benchmark evaluations demonstrate that CIT* improves efficiency by reducing reliance on random sampling, which often leads to slower solution discovery and higher path costs. With biased sampling, CIT* maintains strong performance in solving complex motion planning problems in ${\mathbb{R}^4}$ to ${\mathbb{R}^{16}}$ and has been demonstrated on a real-world manipulation task. A video showcasing our method and experimental results is available at: https://youtu.be/SG2cy9WmjD0.

Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in$\mathbb {R}^{4}$to$\mathbb {R}^{16}$and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at:https://youtu.be/30RsBIdexTUNote to Practitioners—The motivation for this work stems from the challenges faced by existing informed path planners in high-dimensional, continuously valued environments, particularly when an initial feasible solution is difficult to find. Traditional asymmetric bidirectional planners rely on the best current solution to define problem subsets. When a lazy path has been found through lazy reverse search, these planners tend to re-sample and explore the entire problem space, which could hinder the path planning process. Our proposed MIT* algorithm addresses this issue by constructing an estimated informed set based on prior admissible solution costs before finding the initial solution. This estimated set helps to narrow the search area, thereby accelerating the initial convergence rate. MIT* also integrates an adaptive sampling strategy that dynamically adjusts based on the ongoing exploration process, enhancing the planner’s ability to efficiently navigate through challenging spaces. Furthermore, MIT* employs adaptive sparse collision checks, which guide the lazy reverse search that balances computational efficiency with accuracy in pathfinding. The proposed algorithm can be applied to industrial robots, humanoid robots, or service robots to achieve efficient path planning.

This paper introduces a comprehensive user-guided planning framework designed for robots operating in dynamic, human-centered environments – where the ability to execute sequential tasks flexibly and adaptively is paramount. Our planner enables robots to (i) encode object-centric constraints and user preferences via multiple demonstrations, (ii) transfer geometric features and implicit relaxations to novel scenarios while reacting to unforeseen events, and (iii) adapt to changing task conditions in real-time, including the real-time replanning and tracking of moving targets. Our approach relies on C1screw linear interpolation, which generates smooth paths satisfying the underlying geometric constraints that characterize the task. The prescribed path is combined with a hierarchical quadratic programming-based controller which explores the user demonstrations's stochastic variability to relax task constraints while ensuring real-time whole-body collision avoidance. Our framework continuously checks for dynamic changes in task targets, ensuring appropriate planning or control actions, and tending to the prescribed screw path. This comprehensive approach is deployed in different task conditions which are available at https://youtu.be/F0cMr1n1D9k.

In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent batch size is inefficient for initial pathfinding and optimal performance, it relies on effective task allocation. This paper introduces Flexible Informed Trees (FIT*), a sampling-based planner that integrates an adaptive batch-size method to enhance the initial path convergence rate. FIT* employs a flexible approach in adjusting batch sizes dynamically based on the inherent dimension of the configuration spaces and the hypervolume of the n-dimensional hyperellipsoid. By applying dense and sparse sampling strategy, FIT* improves convergence rate while finding successful solutions faster with lower initial solution cost. This method enhances the planner’s ability to handle confined, narrow spaces in the initial finding phase and increases batch vertices sampling frequency in the optimization phase. FIT* outperforms existing single-query, sampling-based planners on the tested problems in R2to R8, and was demonstrated on a real-world mobile manipulation task.

Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction Informed Trees (FDIT*), a sampling-based planner designed to enhance speed and cost-effectiveness in pathfinding. FDIT* builds upon the state-of-the-art informed sampling planner, the Effort Informed Trees (EIT*), by capitalizing on often-overlooked information in invalid vertices. It incorporates principles of physical force, particularly Coulomb’s law. This approach proposes the elliptical k-nearest neighbors search method, enabling fast convergence navigation and avoiding high solution cost or infeasible paths by exploring more problem-specific search-worthy areas. It demonstrates benefits in search efficiency and cost reduction, particularly in confined, high-dimensional environments. It can be viewed as an extension of nearest neighbors search techniques. Fusing invalid vertex data with physical dynamics facilitates force-direction-based search regions, resulting in an improved convergence rate to the optimum. FDIT* outperforms existing single-query, sampling-based planners on the tested problems in ℝ4to ℝ16and has been demonstrated on a real-world mobile manipulation task.

Human-aware path planner is essential for achieving harmonious coexistence between humans and robots in highly dynamic environments. In this paper, we propose an integrated framework to find the optimal path in the complex environment with considering collision risk, social norms, and crowded areas. In the proposed framework, a general dynamic group model (g-space) based on the Gaussian Mixed Model (GMM) is proposed as the social norms of dynamic groups, which not only considers the factors of humans (e.g., pose, quantity, distribution, psychology) but also establishes the proximity and human interacting constraints of dynamic groups. An integrated Collision Risk and Human Space (CR&HS) model is applied to achieve human-acceptable behaviors, in which both collision avoidance, human comfort, and interference-free constraints have been involved. Moreover, an Improved Virtual Doppler Method (IVDM) has been used to realize safety navigation to avoid the robot falling into the crowded area. Finally, the proposed framework has been utilized with the sampling-based rapidly-exploring random tree. Experimental results demonstrate that the proposed method can generate the optimal human-aware collision-free path in complex environments. Note to Practitioners—This paper aims to plan an optimal trajectory for the robot in highly dynamic environments. In this field, it is still a challenging task to plan a trajectory with collision-free, human-aware, and crowd-aware. To do that, we present an integrated framework to generate the optimal trajectory by involving the collision risk, social norms, and human density. First, the g-space model is adopted as interference-free constraints of dynamic groups. The integrated knowledge fusion model (CR&HS) then penalizes the manners which have higher collision risk and adverse effects on human interaction or human comfortable. Besides, human motion and density are provided to a robot by IVDM. The proposed framework is utilized in the sampling-based rapidly-exploring random tree as the evaluation module. Finally, the feasibility and reliability of the proposed method have been verified by experiments in different simulated environments. The proposed framework can be applied in most mobile service robots to achieve human-friendly manners.

Autonomous pedestrian-aware navigation in shared human-robot environments is a challenging problem. Here we consider a common situation in which a large crowd of pedestrians moves together in a limited space. Traditional planners struggle to find collision-free paths in such situations since the free space is limited and always changing. To solve this problem, we proposed a flow map-based RRT* method (FM-RRT*) containing a velocity layer and a minimally-intrusive layer. The proposed method models the velocity of the pedestrian flow and the area where the robot is less invasive to pedestrians. Furthermore, we propose an adaptive bias sampling, which drives the robot considering relative velocity, or minimal intrusion, according to the pedestrian flow. The evaluation is conducted in the Crowdbot Challenge simulator. The results show that our method can find a feasible path considering collision risk while simultaneously avoiding intrusive human movement.

Autonomous navigation among people is a com-plex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-fluid model of crowd flow for such problems. These have an intuitive physical interpretation and do not require much tuning. We further formalize an observation model to infer flow properties from discrete sensor observations, including support for partial observability, and pair it with a flow-aware planner. We demonstrate the potential of the approach in simulated navigation scenarios. We achieve state of the art results on the CrowdBot navigation benchmark, and also compare favorably against a standard ROS planner on a partially observable environment, demonstrating that the flow-aware planner successfully estimates and plans around counter-flows in the crowd in real time. We conclude that flow-based planning shows great promise for crowded environments that may exhibit such flow-like behavior.