12 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
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
Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)
Conflict-Based Search (CBS) is a widely used algorithm for solving
multi-agent pathfinding (MAPF) problems optimally. The core idea of CBS is to
run hierarchical search, when, on the high level the tree of solutions
candidates is explored, and on the low-level an individual planning for a
specific agent (subject to certain constraints) is carried out. To trade-off
optimality for running time different variants of bounded sub-optimal CBS were
designed, which alter both high- and low-level search routines of CBS.
Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to
the high-level of CBS - Anytime BCBS. However, no comprehensive analysis of how
well this algorithm performs compared to the naive one, when we simply
re-invoke CBS with the decreased sub-optimality bound, was present. This work
aims at filling this gap. Moreover, we present and evaluate another anytime
version of CBS that uses FS on both levels of CBS. Empirically, we show that
its behavior is principally different from the one demonstrated by Anytime
BCBS. Finally, we compare both algorithms head-to-head and show that using
Focal Search on both levels of CBS can be beneficial in a wide range of setups.Comment: This is a preprint of the paper accepted to MICAI 202
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
Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles
Path finding is a well-studied problem in AI, which is often framed as graph search. Any-angle path finding is a technique that augments the initial graph with additional edges to build shorter paths to the goal. Indeed, optimal algorithms for any-angle path finding in static environments exist. However, when dynamic obstacles are present and time is the objective to be minimized, these algorithms can no longer guarantee optimality. In this work, we elaborate on why this is the case and what techniques can be used to solve the problem optimally. We present two algorithms, grounded in the same idea, that can obtain provably optimal solutions to the considered problem. One of them is a naive algorithm and the other one is much more involved. We conduct a thorough empirical evaluation showing that, in certain setups, the latter algorithm might be as fast as the previously-known greedy non-optimal solver while providing solutions of better quality. In some (rare) cases, the difference in cost is up to 76%, while on average it is lower than one percent (the same cost difference is typically observed between optimal and greedy any-angle solvers in static environments)