13,898 research outputs found
Maxim Likhachev
Abstract Robot path-planning methods can be used in real-time computer games but then need to run fast to ensure that the game characters move in real time, an issue addressed by incremental heuristic search methods. In this paper, we demonstrate how one can speed up D* Lite, an incremental heuristic search method that implements planning with the freespace assumption to move game characters in initially unknown or partially unknown terrain to given goal coordinates. We speed up D* Lite by implementing its priority queue with buckets rather than a binary heap. This non-trivial change reduces its runtime by a factor of two and effectively doubles the number of game characters that real-time computer games can afford
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
Computational Protein Design Using AND/OR Branch-and-Bound Search
The computation of the global minimum energy conformation (GMEC) is an
important and challenging topic in structure-based computational protein
design. In this paper, we propose a new protein design algorithm based on the
AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional
branch-and-bound search algorithm, to solve this combinatorial optimization
problem. By integrating with a powerful heuristic function, AOBB is able to
fully exploit the graph structure of the underlying residue interaction network
of a backbone template to significantly accelerate the design process. Tests on
real protein data show that our new protein design algorithm is able to solve
many prob- lems that were previously unsolvable by the traditional exact search
algorithms, and for the problems that can be solved with traditional provable
algorithms, our new method can provide a large speedup by several orders of
magnitude while still guaranteeing to find the global minimum energy
conformation (GMEC) solution.Comment: RECOMB 201
Static and Dynamic Path Planning Using Incremental Heuristic Search
Path planning is an important component in any highly automated vehicle
system. In this report, the general problem of path planning is considered
first in partially known static environments where only static obstacles are
present but the layout of the environment is changing as the agent acquires new
information. Attention is then given to the problem of path planning in dynamic
environments where there are moving obstacles in addition to the static ones.
Specifically, a 2D car-like agent traversing in a 2D environment was
considered. It was found that the traditional configuration-time space approach
is unsuitable for producing trajectories consistent with the dynamic
constraints of a car. A novel scheme is then suggested where the state space is
4D consisting of position, speed and time but the search is done in the 3D
space composed by position and speed. Simulation tests shows that the new
scheme is capable of efficiently producing trajectories respecting the dynamic
constraint of a car-like agent with a bound on their optimality.Comment: Internship Repor
The Flexible Group Spatial Keyword Query
We present a new class of service for location based social networks, called
the Flexible Group Spatial Keyword Query, which enables a group of users to
collectively find a point of interest (POI) that optimizes an aggregate cost
function combining both spatial distances and keyword similarities. In
addition, our query service allows users to consider the tradeoffs between
obtaining a sub-optimal solution for the entire group and obtaining an
optimimized solution but only for a subgroup.
We propose algorithms to process three variants of the query: (i) the group
nearest neighbor with keywords query, which finds a POI that optimizes the
aggregate cost function for the whole group of size n, (ii) the subgroup
nearest neighbor with keywords query, which finds the optimal subgroup and a
POI that optimizes the aggregate cost function for a given subgroup size m (m
<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
which finds optimal subgroups and corresponding POIs for each of the subgroup
sizes in the range [m, n]. We design query processing algorithms based on
branch-and-bound and best-first paradigms. Finally, we provide theoretical
bounds and conduct extensive experiments with two real datasets which verify
the effectiveness and efficiency of the proposed algorithms.Comment: 12 page
Multi-agent pathfinding for unmanned aerial vehicles
Unmanned aerial vehicles (UAVs), commonly known as drones, have become more and
more prevalent in recent years. In particular, governmental organizations and companies
around the world are starting to research how UAVs can be used to perform tasks such
as package deliver, disaster investigation and surveillance of key assets such as pipelines,
railroads and bridges. NASA is currently in the early stages of developing an air traffic
control system specifically designed to manage UAV operations in low-altitude airspace.
Companies such as Amazon and Rakuten are testing large-scale drone deliver services in
the USA and Japan.
To perform these tasks, safe and conflict-free routes for concurrently operating UAVs must
be found. This can be done using multi-agent pathfinding (mapf) algorithms, although
the correct choice of algorithms is not clear. This is because many state of the art mapf
algorithms have only been tested in 2D space in maps with many obstacles, while UAVs
operate in 3D space in open maps with few obstacles. In addition, when an unexpected
event occurs in the airspace and UAVs are forced to deviate from their original routes
while inflight, new conflict-free routes must be found. Planning for these unexpected
events is commonly known as contingency planning. With manned aircraft, contingency
plans can be created in advance or on a case-by-case basis while inflight. The scale at
which UAVs operate, combined with the fact that unexpected events may occur anywhere
at any time make both advanced planning and planning on a case-by-case basis impossible.
Thus, a new approach is needed. Online multi-agent pathfinding (online mapf) looks to
be a promising solution. Online mapf utilizes traditional mapf algorithms to perform path
planning in real-time. That is, new routes for UAVs are found while inflight.
The primary contribution of this thesis is to present one possible approach to UAV
contingency planning using online multi-agent pathfinding algorithms, which can be used
as a baseline for future research and development. It also provides an in-depth overview
and analysis of offline mapf algorithms with the goal of determining which ones are likely
to perform best when applied to UAVs. Finally, to further this same goal, a few different
mapf algorithms are experimentally tested and analyzed
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