4 research outputs found
Chance-Constrained Multi-Robot Motion Planning under Gaussian Uncertainties
We consider a chance-constrained multi-robot motion planning problem in the
presence of Gaussian motion and sensor noise. Our proposed algorithm, CC-K-CBS,
leverages the scalability of kinodynamic conflict-based search (K-CBS) in
conjunction with the efficiency of the Gaussian belief trees used in the
Belief-A framework, and inherits the completeness guarantees of Belief-A's
low-level sampling-based planner. We also develop three different methods for
robot-robot probabilistic collision checking, which trade off computation with
accuracy. Our algorithm generates motion plans driving each robot from its
initial state to its goal while accounting for the evolution of its uncertainty
with chance-constrained safety guarantees. Benchmarks compare computation time
to conservatism of the collision checkers, in addition to characterizing the
performance of the planner as a whole. Results show that CC-K-CBS can scale up
to 30 robots.Comment: Submitted to 2023 IEEE International Conference on Intelligent Robots
and Systems (IROS
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
MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation
Traditional \textit{multi-robot motion planning} (MMP) focuses on computing
trajectories for multiple robots acting in an environment, such that the robots
do not collide when the trajectories are taken simultaneously. In
\emph{safety-critical} applications, a human supervisor may want to verify that
the plan is indeed collision-free. In this work, we propose a notion of
explanation for a plan of MMP, based on visualization of the plan as a short
sequence of images representing time segments, where in each time segment the
trajectories of the agents are disjoint, clearly illustrating the safety of the
plan. We show that standard notions of optimality (e.g., makespan) may create
conflict with short explanations. Thus, we propose meta-algorithms, namely
\emph{multi-agent plan segmenting}-X (MAPS-X) and its lazy variant, that can be
plugged on existing centralized sampling-based tree planners X to produce plans
with good explanations using a desirable number of images. We demonstrate the
efficacy of this explanation-planning scheme and extensively evaluate the
performance of MAPS-
Conflict-Based Search for Explainable Multi-Agent Path Finding
In the Multi-Agent Path Finding (MAPF) problem, the goal is to find
non-colliding paths for agents in an environment, such that each agent reaches
its goal from its initial location. In safety-critical applications, a human
supervisor may want to verify that the plan is indeed collision-free. To this
end, a recent work introduces a notion of explainability for MAPF based on a
visualization of the plan as a short sequence of images representing time
segments, where in each time segment the trajectories of the agents are
disjoint. Then, the explainable MAPF problem asks for a set of non-colliding
paths that admits a short-enough explanation. Explainable MAPF adds a new
difficulty to MAPF, in that it is NP-hard with respect to the size of the
environment, and not just the number of agents. Thus, traditional MAPF
algorithms are not equipped to directly handle explainable-MAPF. In this work,
we adapt Conflict Based Search (CBS), a well-studied algorithm for MAPF, to
handle explainable MAPF. We show how to add explainability constraints on top
of the standard CBS tree and its underlying A* search. We examine the
usefulness of this approach and, in particular, the tradeoff between planning
time and explainability.Comment: To appear in International Conference on Automated Planning and
Scheduling (ICAPS 2022), June 202