-- Sampling-based motion planners such as Rapidly-exploring Random Tree* (RRT*) and its informed variant IRRT* are widely used for optimal path planning in complex environments. However, these methods often suffer from slow convergence and high variance due to their reliance on random sampling, particularly when initial solution discovery is delayed. This paper presents Enhanced SIRRT* (E-SIRRT*), a structure-aware planner that improves upon the original SIRRT* framework by introducing two key enhancements: hybrid path smoothing and bidirectional rewiring. Hybrid path smoothing refines the initial path through spline fitting and collision-aware correction, while bidirectional rewiring locally optimizes tree connectivity around the smoothed path to improve cost propagation. Experimental results demonstrate that E-SIRRT* consistently outperforms IRRT* and SIRRT* in terms of initial path quality, convergence rate, and robustness across 100 trials. Unlike IRRT*, which exhibits high variability due to stochastic initialization, E-SIRRT* achieves repeatable and efficient performance through deterministic skeleton-based initialization and structural refinement.

Generating overtaking trajectories in high-speed scenarios presents significant challenges and is typically addressed through hierarchical planning methods. However, this method has two primary drawbacks. First, heuristic algorithms can only provide a single initial solution, which may lead to local optima and consequently diminish the quality of the solution. Second, the time efficiency of trajectory refinement based on numerical optimization is insufficient. To overcome these limitations, this paper proposes an overtaking trajectory planning framework based on spatio-temporal topology and reachable set analysis (SROP), to improve trajectory quality and time efficiency. Specifically, this paper introduces topological classes to describe trajectories representing different overtaking behaviors, which support the spatio-temporal topological search method employed by the upper-layer planner to identify diverse initial paths. This approach helps prevent getting stuck in local optima, enhancing the overall solution quality by considering multiple initial solutions from distinct topologies. Moreover, the reachable set method is integrated into the lower-layer planner for parallel trajectory evaluation. This method enhances planning efficiency by decoupling vehicle model constraints from the optimization process, enabling parallel computation while ensuring control feasibility. Simulation results show that the proposed method improves the smoothness of generated trajectories by 66.8% compared to state-of-the-art methods, highlighting its effectiveness in enhancing trajectory quality. Additionally, this method reduces computation time by 62.9%, demonstrating its efficiency.

This paper presents a novel method for real-time 3D navigation in large-scale, complex environments using a hierarchical 3D visibility graph (V-graph). The proposed algorithm addresses the computational challenges of V-graph construction and shortest path search on the graph simultaneously. By introducing hierarchical 3D V-graph construction with heuristic visibility update, the 3D V-graph is constructed in O(K*n^2logn) time, which guarantees real-time performance. The proposed iterative divide-and-conquer path search method can achieve near-optimal path solutions within the constraints of real-time operations. The algorithm ensures efficient 3D V-graph construction and path search. Extensive simulated and real-world environments validated that our algorithm reduces the travel time by 42%, achieves up to 24.8% higher trajectory efficiency, and runs faster than most benchmarks by orders of magnitude in complex environments. The code and developed simulator have been open-sourced to facilitate future research.

In this paper, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative is greedy detour replanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach named OARP-Replan that performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of mixed-integer linear programs (MILPs) to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate our approach in simulation using maps of real-world environments and compare our approach against a greedy detour replanner and other state-of-the-art approaches.

Abstract--Navigation is a must-have skill for any mobile robot. For the design of our user study, we will extend and formalize our approach to explain both path planning failures There is an increasing deployment of autonomous robots and deviations from the initial trajectory, i.e. to be able to in various domains [1]. Currently, we use one and accountability in their decision-making exists [2]. Navigation is a pivotal aspect of an autonomous robot create different planning failures and trajectory-contrastive decision-making spectrum. After generating explanations for the created scenarios, a key role in achieving accurate and efficient navigation in we will perturb the explanations in the following way: changing environments.

In this paper, we deal with the problem of full-body path planning for walking robots. The state of walking robots is defined in multi-dimensional space. Path planning requires defining the path of the feet and the robot's body. Moreover, the planner should check multiple constraints like static stability, self-collisions, collisions with the terrain, and the legs' workspace. As a result, checking the feasibility of the potential path is time-consuming and influences the performance of a planning method. In this paper, we verify the feasibility of sampling-based planners in the path planning task of walking robots. We identify the strengths and weaknesses of the existing planners. Finally, we propose a new planning method that improves the performance of path planning of legged robots.

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of optimal collision-free path are both critical parts for solving path planning problem. Although conventional sampling-based algorithms, such as the rapidly-exploring random tree (RRT) and its improved optimal version (RRT*), have been widely used in path planning problems because of their ability to find a feasible path in even complex environments, they fail to find an optimal path efficiently. To solve this problem and satisfy the two aforementioned requirements, we propose a novel learning-based path planning algorithm which consists of a novel generative model based on the conditional generative adversarial networks (CGAN) and a modified RRT* algorithm (denoted by CGANRRT*). Given the map information, our CGAN model can generate an efficient possibility distribution of feasible paths, which can be utilized by the CGAN-RRT* algorithm to find the optimal path with a non-uniform sampling strategy. The CGAN model is trained by learning from ground truth maps, each of which is generated by putting all the results of executing RRT algorithm 50 times on one raw map. We demonstrate the efficient performance of this CGAN model by testing it on two groups of maps and comparing CGAN-RRT* algorithm with conventional RRT* algorithm.

Minimum energy paths for transitions such as atomic and/or spin rearrangements in thermalized systems are the transition paths of largest statistical weight. Such paths are frequently calculated using the nudged elastic band method, where an initial path is iteratively shifted to the nearest minimum energy path. The computational effort can be large, especially when ab initio or electron density functional calculations are used to evaluate the energy and atomic forces. Here, we show how the number of such evaluations can be reduced by an order of magnitude using a Gaussian process regression approach where an approximate energy surface is generated and refined in each iteration. When the goal is to evaluate the transition rate within harmonic transition state theory, the evaluation of the Hessian matrix at the initial and final state minima can be carried out beforehand and used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence. A Gaussian process model also provides an uncertainty estimate for the approximate energy surface, and this can be used to focus the calculations on the lesser-known part of the path, thereby reducing the number of needed energy and force evaluations to a half in the present calculations. The methodology is illustrated using the two-dimensional M\"uller-Brown potential surface and performance assessed on an established benchmark involving 13 rearrangement transitions of a heptamer island on a solid surface.

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments.