1,066 research outputs found
Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning
We present a sampling-based framework for multi-robot motion planning which
combines an implicit representation of a roadmap with a novel approach for
pathfinding in geometrically embedded graphs tailored for our setting. Our
pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated
RRT algorithm for the discrete case of a graph, and it enables a rapid
exploration of the high-dimensional configuration space by carefully walking
through an implicit representation of a tensor product of roadmaps for the
individual robots. We demonstrate our approach experimentally on scenarios of
up to 60 degrees of freedom where our algorithm is faster by a factor of at
least ten when compared to existing algorithms that we are aware of.Comment: Kiril Solovey and Oren Salzman contributed equally to this pape
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
On the hardness of unlabeled multi-robot motion planning
In unlabeled multi-robot motion planning several interchangeable robots
operate in a common workspace. The goal is to move the robots to a set of
target positions such that each position will be occupied by some robot. In
this paper, we study this problem for the specific case of unit-square robots
moving amidst polygonal obstacles and show that it is PSPACE-hard. We also
consider three additional variants of this problem and show that they are all
PSPACE-hard as well. To the best of our knowledge, this is the first hardness
proof for the unlabeled case. Furthermore, our proofs can be used to show that
the labeled variant (where each robot is assigned with a specific target
position), again, for unit-square robots, is PSPACE-hard as well, which sets
another precedence, as previous hardness results require the robots to be of
different shapes
Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning
This paper presents a scalable multi-robot motion planning algorithm called
Conflict-Based Model Predictive Control (CB-MPC). Inspired by Conflict-Based
Search (CBS), the planner leverages a similar high-level conflict tree to
efficiently resolve robot-robot conflicts in the continuous space, while
reasoning about each agent's kinematic and dynamic constraints and actuation
limits using MPC as the low-level planner. We show that tracking high-level
multi-robot plans with a vanilla MPC controller is insufficient, and results in
unexpected collisions in tight navigation scenarios. Compared to other
variations of multi-robot MPC like joint, prioritized, and distributed, we
demonstrate that CB-MPC improves the executability and success rate, allows for
closer robot-robot interactions, and reduces the computational cost
significantly without compromising the solution quality across a variety of
environments. Furthermore, we show that CB-MPC combined with a high-level path
planner can effectively substitute computationally expensive full-horizon
multi-robot kinodynamic planners
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