21 research outputs found
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path
in the presence of dynamic obstacles. SIPP returns provably optimal solutions.
However, in many practical applications of SIPP such as path planning for
robots, one would like to trade-off optimality for shorter planning time. In
this paper we explore different ways to build a bounded-suboptimal SIPP and
discuss their pros and cons. We compare the different bounded-suboptimal
versions of SIPP experimentally. While there is no universal winner, the
results provide insights into when each method should be used
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
Combined Target-Assignment and Path-Finding problem (TAPF) requires
simultaneously assigning targets to agents and planning collision-free paths
for agents from their start locations to their assigned targets. As a leading
approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA)
leverages both K-best target assignments to create multiple search trees and
Conflict-Based Search (CBS) to resolve collisions in each search tree. While
being able to find an optimal solution, CBS-TA suffers from scalability due to
the duplicated collision resolution in multiple trees and the expensive
computation of K-best assignments. We therefore develop Incremental Target
Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS
generates only a single search tree and avoids computing K-best assignments by
incrementally computing new 1-best assignments during the search. We show that,
in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice,
is computationally efficient
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
A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding
Conventional multi-agent path planners typically compute an ensemble of paths
while optimizing a single objective, such as path length. However, many
applications may require multiple objectives, say fuel consumption and
completion time, to be simultaneously optimized during planning and these
criteria may not be readily compared and sometimes lie in competition with each
other. Naively applying existing multi-objective search algorithms, such as
multi-objective A* (MOA*), to multi-agent path finding may prove to be
inefficient as the size of the space of possible solutions, i.e., the
Pareto-optimal set, can grow exponentially with the number of agents (the
dimension of the search space). This article presents an approach named
Multi-Objective Conflict-Based Search (MO-CBS) that bypasses this so-called
curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a
well-known algorithm for single-objective multi-agent path finding, and
principles of dominance from multi-objective optimization literature. We also
develop several variants of MO-CBS to further improve its performance. We prove
that MO-CBS and its variants are able to compute the entire Pareto-optimal set.
Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a
recently developed state-of-the-art multi-objective multi-agent planner.Comment: 11 pages, preliminary version published in ICRA 2021, journal version
submitte
Improving Continuous-time Conflict Based Search
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally
solving classical multi-agent path finding (MAPF) problems, where time is
discretized into the time steps. Continuous-time CBS (CCBS) is a recently
proposed version of CBS that guarantees optimal solutions without the need to
discretize time. However, the scalability of CCBS is limited because it does
not include any known improvements of CBS. In this paper, we begin to close
this gap and explore how to adapt successful CBS improvements, namely,
prioritizing conflicts (PC), disjoint splitting (DS), and high-level
heuristics, to the continuous time setting of CCBS. These adaptions are not
trivial, and require careful handling of different types of constraints,
applying a generalized version of the Safe interval path planning (SIPP)
algorithm, and extending the notion of cardinal conflicts. We evaluate the
effect of the suggested enhancements by running experiments both on general
graphs and -neighborhood grids. CCBS with these improvements significantly
outperforms vanilla CCBS, solving problems with almost twice as many agents in
some cases and pushing the limits of multiagent path finding in continuous-time
domains.Comment: This is a pre-print of the paper accepted to AAAI 202
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
We present a scalable and effective multi-agent safe motion planner that
enables a group of agents to move to their desired locations while avoiding
collisions with obstacles and other agents, with the presence of rich
obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation
limits, and disturbances. We address this problem by finding a piecewise linear
path for each agent such that the actual trajectories following these paths are
guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial
tracking error of the actual trajectories of the controlled agents can be
pre-computed for any qualified path that considers the minimum duration of each
path segment due to actuation limits. Using these bounds, we find a
collision-free path for each agent by solving Mixed Integer-Linear Programs and
coordinate agents by using the priority-based search. We demonstrate our method
by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors,
respectively, and show improvements over the solving time and the solution
quality compared to two state-of-the-art multi-agent motion planners.Comment: Accepted at AAAI2021. 9 pages, 5 figures, 1 tabl