We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP manipulation has attracted significant interest resulting a proliferation of different approaches. In contrast, received considerably less attention. Autonomous robots operating real-world complex scenarios require planning the discrete (task) space and continuous (motion) spac...

Several years of consulting with online community hosts and managers have highlighted a variety of issues that recur across many online community development efforts. We summarize those issues in eight points that have functioned as useful guidelines to working with online communities, particularly within a corporate context. These recommendations focus on the location and purpose of the commun...

For autonomous artificial decision-makers to solve realistic tasks, they need to deal with searching through large state and action spaces under time pressure. We study the problem of planning in such domains and show how structured representations of the environment’s dynamics can help partition the action space into a set of equivalence classes at run time. The partitioned action space is the...

We present a novel approach for planning and directing heterogeneous groups of virtual agents based on techniques from linear programming. Our method efficiently identifies the most promising paths in both time and space and provides an optimal distribution of the groups’ members over these paths such that their average traveling time is minimized. The computed space-time plan is combined with ...

urban green spaces are one of the most important services in modern cities and form a set of urban sustainable development factors which receive considerable attention from urban planners. this paper explains how land use can be optimally allocated to urban public green spaces. in this study, some of the factors influencing urban green spaces in a period from 1373 to 1384 were studied. moreover...

Modern domain-independent heuristic planners evaluate their plans on the basis of their length only. However, in real-world problems there are other criteria that also play an important role, such as resource consumption, profit, safety, etc. This paper extends the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria. The heu...

This paper presents a mission planning method for planetary rover. As a decision support system, mission planning is important for rover navigation and control. Generating travel path and task sequence synchronously is the key point. In this paper, the environment model of planetary surface is summed up as two parts which are obstacle map and timeline. According to this, the mission planner uti...

Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically improved the speed of POMDP planning, enabl...

Great advances have marked the progress of AI planning research over the past few years. Recent systems can quickly solve problems that are orders of magnitude harder than those tackled by the best previous planners. However, we are still a long way from understanding how humans plan. Understanding how humans plan is important if we are to develop intelligent planning systems capable of dealing...

Most planning problems have strong structures. They can be decomposed into subdomains with causal dependencies. The idea of exploiting the domain decomposition has motivated previous work such as hierarchical planning and factored planing. However, these algorithms require extensive backtracking and lead to few efficient general-purpose planners. On the other hand, heuristic search has been a s...