. This report looks at a new approach to motion planning known as Probabilistic Cell Decomposition (PCD). This approach combines ideas from Approximate Cell Decomposition (ACD) and Sampling-based motion planning to create a planner that can work in highdimensional static configuration space. This report first gives an overview of PCD and then describes the implementation of the planner along wi...

Planning with concurrent durative actions and probabilistic effects, or probabilistic temporal planning, is a relatively new area of research. The challenge is to replicate the success of modern temporal and probabilistic planners with domains that exhibit an interaction between time and uncertainty. We present a general framework for probabilistic temporal planning in which effects, the time a...

We present a partial-order, conformant, probabilistic planner, Probapop which competed in the blind track of the Probabilistic Planning Competition in IPC-4. We explain how we adapt distance based heuristics for use with probabilistic domains. Probapop also incorporates heuristics based on probability of success. We explain the successes and difficulties encountered during the design and implem...

We describe Probapop, a partial-order probabilistic planning system. Probapop is a blind (conformant) planner that finds plans for domains involving probabilistic actions but no observability. The Probapop implementation is based on Vhpop, a partial-order deterministic planner written in C++. The Probapop algorithm uses plan graph based heuristics for selecting a plan from the search queue, and...

COMPLAN is a conformant probabilistic planner that finds a plan with maximum probability of success for a given horizon. The core of the planner is a a depth-first branch-andbound search in the plan space. For each potential search node, an upper bound is computed on the success probability of the best plans under the node, and the node is pruned if this upper bound is not greater than the succ...

We extend RBPP, the state-of-the-art, translation-based planner for conformant probabilistic planning (CPP) with deterministic actions, to handle a wide set of CPPs with stochastic actions. Our planner uses relevance analysis to divide a probabilistic ”failure-allowance” between the initial state and the stochastic actions. Using its ”initial-state allowance,” it uses relevance analysis to sele...

This paper presents a set of paths, called bi-elementary paths. These paths are smooth and feasible for a car-like robot (i.e. their tangent direction is continuous and they respect a minimum turning radius constraint), and they can be followed by a real vehicle without stopping (i.e. they have a continuous curvature proole) | which is not the case of Dubins' curves. These paths are composed of...

Probabilistic and deterministic planners are two major approximate-based frameworks for solving motion planning problems. Both approaches have their own advantages and disadvantages. In this work, we provide an investigation to the following question: Is there a planner that can take the advantages from both probabilistic and deterministic planners? Our strategy to achieve this goal is to use t...

The basic motion-planning problem is to plan a collision-free motion for objects moving among obstacles between free initial and goal positions, or to determine that no such motion exists. The basic problem as well as numerous variants of it have been intensively studied over the past two decades yielding a wealth of results and techniques, both theoretical and practical. In this paper, we prop...