arXiv:2607. 00065v1 Announce Type: cross Abstract: Any-angle path planning extends traditional graph-based path planning by allowing movement between any pair of vertices, rather than being restricted by predefined edges.
arXiv:2607. 00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained.
The paper introduces the Unified Path Planner (UPP), a graph‑search algorithm that balances safety and optimality by adaptively weighting heuristics and using a local inverse‑distance safety field. UPP auto‑tunes its parameters during search, guaranteeing suboptimality bounds while improving obstacle clearance. Evaluation on ten simulated environments shows UPP achieving a 0.94 OptiSafe score—significantly higher than existing methods—while adding only 0.5–1% to path length and maintaining a 100% success rate, with hardware validation on a TurtleBot confirming practical benefits.
arXiv:2606. 18730v1 Announce Type: cross Abstract: The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a minimum cost trajectory for an agent that departs from a static depot, visits a set of moving targets, each within one of their assigned time windows, and returns to the depot.
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
The paper presents DORA, an online learning algorithm for robot navigation that uses Dijkstra’s algorithm as an exact planning engine under a weaker condition than usual causality—specifically, nonnegativity of a reduced cost on a determinized map. DORA calls a shortest‑path oracle a fixed number of times per episode, avoids estimating transition kernels, and incorporates a logarithmic survival weight to keep contact probabilities with dynamic obstacles within a budget. Experiments on grid‑world, directional drilling, and drone surveillance benchmarks show that DORA matches optimistic value iteration with the true transition kernel while performing 4.5 to 19.3 times less planner work, reduces contacts by a factor of seventeen compared to determinize‑and‑replan, and maintains contact rates within wide budget ranges.
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.
arXiv:2607. 19306v1 Announce Type: cross Abstract: Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time.
The paper introduces SafeHarness, an obstacle‑aware framework that improves the safety of coding agents for robot manipulation. By decomposing tasks into route planning and contact execution, the harness enables the agent to prioritize collision avoidance, achieving 71.9% task success and 87.5% collision avoidance—significantly better than prior methods. The study demonstrates that safety constraints can be effectively integrated into language‑model‑driven robot controllers.
Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown true traversal costs of the map apriori and imperfect actuation...
arXiv:2608. 06702v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints.