Motion planning in dynamic and uncertain real-world environments remains a critical challenge in robotics, as it is essential for the effective operation of autonomous systems. One strategy for motion planning has been to introduce a state lattice where pre-computed motion primitives can be combined with graph-based search methods to find a physically feasible motion plan. However, introducing lattice planning into dynamic, uncertain settings remains challenging. It is nontrivial to incorporate uncertain dynamic information into the planning process in real time. Thus, in this paper we propose a lattice planning framework for dynamic environments with extensions to handle safety-critical edge-cases that can arise with the uncertain nature of the environment. The proposed method, Safe Lattice Planner (SLP), extends the Receding-Horizon Lattice Planner (RHLP) with enhanced replanning and survival capabilities to handle the dynamic habitat. We thoroughly evaluate SLP in a new benchmark suite against provided baselines. SLP is found to outperform the baselines in terms of safety and resilience in the dynamic environment while reaching the goal state in an efficient manner. We release the benchmark and SLP to accelerate the field of safe robotics.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent’s favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.