28 research outputs found
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
The problem of finding conflict-free trajectories for multiple agents of
identical circular shape, operating in shared 2D workspace, is addressed in the
paper and decoupled, e.g., prioritized, approach is used to solve this problem.
Agents' workspace is tessellated into the square grid on which any-angle moves
are allowed, e.g. each agent can move into an arbitrary direction as long as
this move follows the straight line segment whose endpoints are tied to the
distinct grid elements. A novel any-angle planner based on Safe Interval Path
Planning (SIPP) algorithm is proposed to find trajectories for an agent moving
amidst dynamic obstacles (other agents) on a grid. This algorithm is then used
as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical,
side we show that AA-SIPP(m) is complete under well-defined conditions. On the
experimental side, in simulation tests with up to 200 agents involved, we show
that our planner finds much better solutions in terms of cost (up to 20%)
compared to the planners relying on cardinal moves only.Comment: Final version as submitted to ICAPS-2017 (main track); 8 pages; 4
figures; 1 algorithm; 2 table
Prioritized Multi-agent Path Finding for Differential Drive Robots
Methods for centralized planning of the collision-free trajectories for a
fleet of mobile robots typically solve the discretized version of the problem
and rely on numerous simplifying assumptions, e.g. moves of uniform duration,
cardinal only translations, equal speed and size of the robots etc., thus the
resultant plans can not always be directly executed by the real robotic
systems. To mitigate this issue we suggest a set of modifications to the
prominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the most
restrictive assumptions (syncronized translation only moves, equal size and
speed of the robots) and at providing robustness to the solutions. We evaluate
the suggested algorithm in simulation and on differential drive robots in
typical lab environment (indoor polygon with external video-based navigation
system). The results of the evaluation provide a clear evidence that the
algorithm scales well to large number of robots (up to hundreds in simulation)
and is able to produce solutions that are safely executed by the robots prone
to imperfect trajectory following. The video of the experiments can be found at
https://youtu.be/Fer_irn4BG0.Comment: This is a pre-print version of the paper accepted to ECMR 2019
(https://ieeexplore.ieee.org/document/8870957
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
The Multi-Agent Pickup and Delivery (MAPD) problem models applications where
a large number of agents attend to a stream of incoming pickup-and-delivery
tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and
effective. We make TP even more efficient and effective by using a novel
combinatorial search algorithm, called Safe Interval Path Planning with
Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an
advanced data structure that allows for fast updates and lookups of the current
paths of all agents in an online setting. The resulting MAPD algorithm
TP-SIPPwRT takes kinematic constraints of real robots into account directly
during planning, computes continuous agent movements with given velocities that
work on non-holonomic robots rather than discrete agent movements with uniform
velocity, and is complete for well-formed MAPD instances. We demonstrate its
benefits for automated warehouses using both an agent simulator and a standard
robot simulator. For example, we demonstrate that it can compute paths for
hundreds of agents and thousands of tasks in seconds and is more efficient and
effective than existing MAPD algorithms that use a post-processing step to
adapt their paths to continuous agent movements with given velocities.Comment: AAAI 201
A Survey of Multi-Robot Motion Planning
Multi-robot Motion Planning (MRMP) is an active research field which has
gained attention over the years. MRMP has significant roles to improve the
efficiency and reliability of multi-robot system in a wide range of
applications from delivery robots to collaborative assembly lines. This survey
provides an overview of MRMP taxonomy, state-of-the-art algorithms, and
approaches which have been developed for multi-robot systems. This study also
discusses the strengths and limitations of each algorithm and their
applications in various scenarios. Moreover, based on this, we can draw out
open problems for future research.Comment: This is my Ph.D. comprehensive exam repor
Autonomous object harvesting using synchronized optoelectronic microrobots
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the application of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate. The microrobots in turn can be used to exert forces on secondary objects and carry out a wide range of micromanipulation operations, including collecting, transporting and depositing microscopic cargos. In contrast to alternative (direct) micromanipulation techniques, OETdMs are relatively gentle, making them particularly well suited to interacting with sensitive objects such as biological cells. However, at present such systems are used exclusively under manual control by a human operator. This limits the capacity for simultaneous control of multiple microrobots, reducing both experimental throughput and the possibility of cooperative multi-robot operations. In this article, we describe an approach to automated targeting and path planning to enable open-loop control of multiple microrobots. We demonstrate the performance of the method in practice, using microrobots to simultaneously collect, transport and deposit silica microspheres. Using computational simulations based on real microscopic image data, we investigate the capacity of microrobots to collect target cells from within a dissociated tissue culture. Our results indicate the feasibility of using OETdMs to autonomously carry out micromanipulation tasks within complex, unstructured environments