21 research outputs found
Receding Horizon Re-ordering of Multi-Agent Execution Schedules
The trajectory planning for a fleet of Automated Guided Vehicles (AGVs) on a
roadmap is commonly referred to as the Multi-Agent Path Finding (MAPF) problem,
the solution to which dictates each AGV's spatial and temporal location until
it reaches it's goal without collision. When executing MAPF plans in dynamic
workspaces, AGVs can be frequently delayed, e.g., due to encounters with humans
or third-party vehicles. If the remainder of the AGVs keeps following their
individual plans, synchrony of the fleet is lost and some AGVs may pass through
roadmap intersections in a different order than originally planned. Although
this could reduce the cumulative route completion time of the AGVs, generally,
a change in the original ordering can cause conflicts such as deadlocks. In
practice, synchrony is therefore often enforced by using a MAPF execution
policy employing, e.g., an Action Dependency Graph (ADG) to maintain ordering.
To safely re-order without introducing deadlocks, we present the concept of the
Switchable Action Dependency Graph (SADG). Using the SADG, we formulate a
comparatively low-dimensional Mixed-Integer Linear Program (MILP) that
repeatedly re-orders AGVs in a recursively feasible manner, thus maintaining
deadlock-free guarantees, while dynamically minimizing the cumulative route
completion time of all AGVs. Various simulations validate the efficiency of our
approach when compared to the original ADG method as well as robust MAPF
solution approaches.Comment: IEEE Transactions on Robotics (T-Ro) preprint, 17 pages, 32 figure
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
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution
The Multi-Agent Path Finding (MAPF) problem involves planning collision-free
paths for multiple agents in a shared environment. The majority of MAPF solvers
rely on the assumption that an agent can arrive at a specific location at a
specific timestep. However, real-world execution uncertainties can cause agents
to deviate from this assumption, leading to collisions and deadlocks. Prior
research solves this problem by having agents follow a Temporal Plan Graph
(TPG), enforcing a consistent passing order at every location as defined in the
MAPF plan. However, we show that TPGs are overly strict because, in some
circumstances, satisfying the passing order requires agents to wait
unnecessarily, leading to longer execution time. To overcome this issue, we
introduce a new graphical representation called a Bidirectional Temporal Plan
Graph (BTPG), which allows switching passing orders during execution to avoid
unnecessary waiting time. We design two anytime algorithms for constructing a
BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following
BTPGs consistently outperforms following TPGs, reducing unnecessary waits by
8-20%.Comment: Accepted by AAAI-202
Introducing Delays in Multi-Agent Path Finding
We consider a Multi-Agent Path Finding (MAPF) setting where agents have been
assigned a plan, but during its execution some agents are delayed. Instead of
replanning from scratch when such a delay occurs, we propose delay
introduction, whereby we delay some additional agents so that the remainder of
the plan can be executed safely. We show that the corresponding decision
problem is NP-Complete in general. However, in practice we can find optimal
delay-introductions using CBS for very large numbers of agents, and both
planning time and the resulting length of the plan are comparable, and
sometimes outperform, the state-of-the-art heuristics for replanning. We also
examine the benefits of our method from an explainability point of view.Comment: 10 pages, 8 figures, and 2 table
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
Multi AGV coordination tolerant to communication failures
Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real
time, rather than planning in advance, by using a temporal estimation of the robot’s positions at
any given time. In this article, the implementation of a control system for multi-robot applications
that operate in environments where communication faults can occur and where entire sections of
the environment may not have any connection to the communication network will be presented.
This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks
whether the robot’s movements are synchronized. The implemented system allowed the creation
and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA*
algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics
and the Lazarus development environment, it was possible to simulate the execution of several
different missions by the implemented system and analyze their results.info:eu-repo/semantics/publishedVersio