Searching for parallel solutions in state space planners is a challenging problem, because it would require the planners to branch on all possible subsets of parallel actions, exponentially increasing their branching factor. We introduce a variant of our heuristic state search planner AltAlt, which generates parallel plans by using greedy online parallelization of partial plans. Empirical resul...

In this letter, we propose a lightweight yet effective Topology Guided Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited onboard computing resources. The proposed system follows the traditional hierarchical planning workflow, novel designs to improve robustness and efficiency in both pathfinding trajectory optimization sub-modules. Firstly, topology guided graph, w...

In this paper, we present a new hybrid motion planner that i s capable of exploiting previous planning episodes when confronted with new planning problems. Our approach i s applicable when several (similar) problems are successively posed for the same static environment, or when the environment changes incrementally between planning episodes. At the heart of our system lie two low-level motion ...

This doctoral work centers on developing domain-independent heuristics for planning problems characterized by state incompleteness and action non-determinism. The key means by which the heuristics are computed is through planning graph analysis. The approach has been used to construct conformant and contingent plans in two different search techniques. The initial focus of the research was inves...

Purpose This paper aims to present a hierarchical motion planner for planning the manipulation repose long and heavy objects considering external support surfaces. Design/methodology/approach The includes task-level layer motion-level layer. formulates problem at task level by grasp poses as nodes object edges. considers regrasping constrained in-hand slip (drooping) during building graphs find...

During the resolution of a planning problem the planner performs causal and resource reasoning. Causal reasoning refers to the generation of a sequence of actions (instantiated operators) which when executed in the initial state will result in the state of the world that contains the goals. Resource reasoning refers to the allocation of resources to the actions that form the plan in order to ob...

The goal of this paper is to develop a continuous optimization-based refinement the reference trajectory ‘push it out’ obstacle-occupied space in global phase for Multi-rotor Aerial Vehicles unknown environments. Our proposed approach comprises two planners: planner and local planner. refines initial when goes either through an obstacle or near lets calculate near-optimal control policy. convex...

GraphPlan-like and SATPLAN-like planners have shown to outperform classical planners for most of the classical planning domains. However, these two propositional approaches do not exhibit good results for large-size problems due to the graph size they have to handle. In this paper we show a new approach for planning as an attempt to combine the advantanges of a graph-based analysis and a partia...

We present a partial-order probabilistic planning algorithm that adapts plan-graph based heuristics implemented in Repop. We describe our implemented planner, Reburidan, named after its predecessors Repop and Buridan. Reburidan uses plan-graph based heuristics to first generate a base plan. It then improves this plan using plan refinement heuristics based on the success probability of subgoals....

A two-level search algorithm for motion planning is presented in this paper. The algorithm combines a multiheuristic local search algorithm with a subgoal graph based global guidance. A novel feature of the planner is that it can adjust the balance between local and global planning. As the experimental data suggests that the optimal balance depends on the problem, a scheduling mechanism is adde...