Organizations: Department of Computer Science and Engineering, University at Buffalo, NY 14260, USA. · Department of Mechanical and Aerospace Engineering, University at Buffalo, NY 14260, USA.
Mission-level autonomy for disaster response, search and rescue, and tactical UGV operations requires repeated path and mission planning as terrain conditions, agent types, and objectives change. Pixel-grid search is costly for repeated kilometer-scale queries, while semantic abstractions must maintain valid costs and connectivity as conditions change. We present HOPHY (Hierarchical Off-Road Planning using Hypergraphs), a reusable hierarchical terrain representation that organizes map-scale terrain into geometrically connected semantic regions (GSNodes), connectivity-preserving critical regions (Coarse Regions), and typed hyperedges for terrain, agent, and weather context. Hyperedge intersections select affected regions and incident edges for state updates without rebuilding the hierarchy. Across real off-road maps spanning kilometer-scale areas, HOPHY achieves 100% planning success and less than 0.01% median cost deviation from the oracle (pixel A*), with substantially lower query and replanning latency than the evaluated pixel and abstraction baselines. Applied to a multi-robot task-allocation (MRTA) problem, these gains reduce total computation by 79x over pixel A* and 7.2x over the fastest abstraction baseline, with mission makespan comparable to pixel A*. Finally, we demonstrate HOPHY on a physical Clearpath Jackal that successfully executes a 1.5-km, eight-task mission across mixed-surface outdoor terrain and a blockage-triggered replanned route.
Recent learning-based path planners use neural networks to process visual map representations and approximate heuristics for classical search algorithms, yielding near-optimal paths with reduced search effort. However, these methods are tied to the shortest-path objective implicit in their supervision, which limits their flexibility to accommodate alternative criteria. We introduce FlexPath, a two-stage framework that decouples feasibility from preference. In Stage 1, we use imitation learning to acquire a task-independent spatial prior over feasible paths from visual map inputs. In Stage 2, differentiable Path Shape Objectives (PSOs) adapt this prior toward task-specific criteria without relearning path structure, requiring only efficient objective-level adaptation. A single pretrained model can be adapted to multiple objectives. For shortest-path planning, FlexPath reduces search effort on TMP by 14.3% compared to the state-of-the-art TransPath, while also finding lower-cost paths on average and demonstrating strong zero-shot generalization across three unseen domains. For obstacle clearance with minimum clearance distance 2, it achieves 96.8% full obstacle avoidance while maintaining low search cost. The framework further extends to semantic-aware avoidance and waypoint guidance via objective-level adaptation, and remains compatible with classical planners at inference time. Data and code are available at https://github.com/FraunhoferIVI/FlexPath.
We focus on the problem of efficient anytime kinodynamic planning for systems with complex dynamics in unstructured environments that make precomputing motion primitives infeasible. Directly applying A* to such problems is computationally infeasible due to the curse of dimensionality. Methods such as Hybrid A* addressed this burden by discretizing the state space, but in turn creating a coupling between tree discovery and the discretization resolution. The Incremental Generalized Hybrid A* (IGHA*) performs search over a hierarchy of resolutions in an anytime fashion to break this coupling, by freezing vertices to use in later search iterations rather than pruning them. However, the frozen vertices can hide solution-supporting vertices from the search at a particular iteration. While classical bidirectional search is motivated by the reduction of search depth, extending IGHA* into the bidirectional setting (termed Bi-IGHA*) obtains additional benefit by fundamentally mitigating the behaviour induced by frozen vertices hiding solutions. We show that Bi-IGHA* preserves IGHA*'s guarantees on monotonic cost improvement and termination. We empirically show that Bi-IGHA* substantially reduces expansions on R3, R4, and R6 planning problems, and achieves equivalent closed-loop performance with kinodynamic planning for high-speed off-road autonomy while requiring significantly fewer expansions. Website: https://personalrobotics.github.io/IGHAStar/biighastar.html
Autonomous navigation across large off-road environments remains a challenging problem. Onboard sensors perceive only the immediate surroundings, yet safe and efficient routes depend on terrain features that extend well beyond the sensor horizon. Geo-spatial data sources such as satellite imagery, aerial LiDAR, and vector maps can close this gap, but learning traversability from them is difficult: dense labels are unavailable at scale, and existing methods rely on short-range sensing. We propose an efficient formulation that learns a continuous traversability map from overhead data, supervised directly by human-driven GPS trajectories and shaped by supervised geometric priors from LiDAR. Alongside the model, we release a dataset, curated from public sources, consisting of 299 scenes spanning ∼1,244km2 of diverse terrain, paired with 1,130km of human driving. In field trials on a Clearpath Warthog across seven routes at two sites,our method achieves trajectories within 5.5% of human path length and reduces operator interventions by ∼85% compared to local-planner-only autonomy.
Kasi Viswanath, Jason M. Gregory, Shaunak Kolhe +1