585 research outputs found
Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance
We address the controller synthesis problem for distributed formation control. Our solution requires only relative bearing measurements (as opposed to full translations), and is based on the exact gradient of a Lyapunov function with only global minimizers (independently from the formation topology). These properties allow a simple proof of global asymptotic convergence, and extensions for including distance measurements, leaders and collision avoidance. We validate our approach through simulations and comparison with other stateof-the-art algorithms.ARL grant W911NF-08-2-0004, ARO grant W911NF-13-1-0350, ONR grants N00014-07-1-0829, N00014-14-1-0510, N00014-15-1-2115, NSF grant IIS-1426840, CNS-1521617 and United Technologies
Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons
We consider the following motion-planning problem: we are given unit
discs in a simple polygon with vertices, each at their own start position,
and we want to move the discs to a given set of target positions. Contrary
to the standard (labeled) version of the problem, each disc is allowed to be
moved to any target position, as long as in the end every target position is
occupied. We show that this unlabeled version of the problem can be solved in
time, assuming that the start and target positions are at
least some minimal distance from each other. This is in sharp contrast to the
standard (labeled) and more general multi-robot motion-planning problem for
discs moving in a simple polygon, which is known to be strongly NP-hard
Motion Planning for Unlabeled Discs with Optimality Guarantees
We study the problem of path planning for unlabeled (indistinguishable)
unit-disc robots in a planar environment cluttered with polygonal obstacles. We
introduce an algorithm which minimizes the total path length, i.e., the sum of
lengths of the individual paths. Our algorithm is guaranteed to find a solution
if one exists, or report that none exists otherwise. It runs in time
, where is the number of robots and is the total
complexity of the workspace. Moreover, the total length of the returned
solution is at most , where OPT is the optimal solution cost. To
the best of our knowledge this is the first algorithm for the problem that has
such guarantees. The algorithm has been implemented in an exact manner and we
present experimental results that attest to its efficiency
Formation Control Algorithms With Limited or No Communication
Formation control refers to a collective behaviour of multi-agent systems where individual agents come together to form a pattern, often geometric. These formations can enable multi-agent systems to function more effectively in a broad range of applications. Many formation control algorithms require centralized decision making, communication between agents or a centralized decision maker and other factors that increase per-agent cost and reduce the robustness and scalability of multi-agent systems. To this end, we introduce two algorithms that operate using local decision making and limited or no communication. The first algorithm is a communication-free and index-free algorithm based on polar indicator distributions. The second is a progressive assignment algorithm using limited, situated communication that deterministically assigns agents a position in the objective formation along a convex spiral directed path graph. We also present an extension of the second algorithm for 3-dimensional formation definitions. The first algorithm is demonstrated in a physical experiment using ground-based agents while the second one is simulated using micro air vehicles (MAVs) in a physics-based simulator
Optimal scheduling for refueling multiple autonomous aerial vehicles
The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed
A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)
This work presents a novel framework for the formation control of multiple
autonomous ground vehicles in an on-road environment. Unique challenges of this
problem lie in 1) the design of collision avoidance strategies with obstacles
and with other vehicles in a highly structured environment, 2) dynamic
reconfiguration of the formation to handle different task specifications. In
this paper, we design a local MPC-based tracking controller for each individual
vehicle to follow a reference trajectory while satisfying various constraints
(kinematics and dynamics, collision avoidance, \textit{etc.}). The reference
trajectory of a vehicle is computed from its leader's trajectory, based on a
pre-defined formation tree. We use logic rules to organize the collision
avoidance behaviors of member vehicles. Moreover, we propose a methodology to
safely reconfigure the formation on-the-fly. The proposed framework has been
validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1
Multi-agent Path Planning and Network Flow
This paper connects multi-agent path planning on graphs (roadmaps) to network
flow problems, showing that the former can be reduced to the latter, therefore
enabling the application of combinatorial network flow algorithms, as well as
general linear program techniques, to multi-agent path planning problems on
graphs. Exploiting this connection, we show that when the goals are permutation
invariant, the problem always has a feasible solution path set with a longest
finish time of no more than steps, in which is the number of
agents and is the number of vertices of the underlying graph. We then give
a complete algorithm that finds such a solution in time, with
being the number of edges of the graph. Taking a further step, we study time
and distance optimality of the feasible solutions, show that they have a
pairwise Pareto optimal structure, and again provide efficient algorithms for
optimizing two of these practical objectives.Comment: Corrected an inaccuracy on time optimal solution for average arrival
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