5 research outputs found
Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomial
This paper presents a new efficient algorithm which guarantees a solution for
a class of multi-agent trajectory planning problems in obstacle-dense
environments. Our algorithm combines the advantages of both grid-based and
optimization-based approaches, and generates safe, dynamically feasible
trajectories without suffering from an erroneous optimization setup such as
imposing infeasible collision constraints. We adopt a sequential optimization
method with \textit{dummy agents} to improve the scalability of the algorithm,
and utilize the convex hull property of Bernstein and relative Bernstein
polynomial to replace non-convex collision avoidance constraints to convex
ones. The proposed method can compute the trajectory for 64 agents on average
6.36 seconds with Intel Core i7-7700 @ 3.60GHz CPU and 16G RAM, and it reduces
more than of the objective cost compared to our previous work. We
validate the proposed algorithm through simulation and flight tests.Comment: 7 pages, ICRA2020 under revie
EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments
This paper presents a decentralized and asynchronous systematic solution for
multi-robot autonomous navigation in unknown obstacle-rich scenes using merely
onboard resources. The planning system is formulated under gradient-based local
planning framework, where collision avoidance is achieved by formulating the
collision risk as a penalty of a nonlinear optimization problem. In order to
improve robustness and escape local minima, we incorporate a lightweight
topological trajectory generation method. Then agents generate safe, smooth,
and dynamically feasible trajectories in only several milliseconds using an
unreliable trajectory sharing network. Relative localization drift among agents
is corrected by using agent detection in depth images. Our method is
demonstrated in both simulation and real-world experiments. The source code is
released for the reference of the community.Comment: 7 pages, 15 figures, accepted by ICRA 2021 and reported by Science
News
(https://www.sciencemag.org/news/2020/12/watch-swarm-drones-fly-through-heavy-forest-while-staying-formation
GPU Accelerated Convex Approximations for Fast Multi-Agent Trajectory Optimization
In this paper, we present a computationally efficient trajectory optimizer
that can exploit GPUs to jointly compute trajectories of tens of agents in
under a second. At the heart of our optimizer is a novel reformulation of the
non-convex collision avoidance constraints that reduces the core computation in
each iteration to that of solving a large scale, convex, unconstrained
Quadratic Program (QP). We also show that the matrix factorization/inverse
computation associated with the QP needs to be done only once and can be done
offline for a given number of agents. This further simplifies the solution
process, effectively reducing it to a problem of evaluating a few matrix-vector
products. Moreover, for a large number of agents, this computation can be
trivially accelerated on GPUs using existing off-the-shelf libraries. We
validate our optimizer's performance on challenging benchmarks and show
substantial improvement over state of the art in computation time and
trajectory quality.Comment: 8 page
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
Multi-Agent Path Finding has been widely studied in the past few years due to
its broad application in the field of robotics and AI. However, previous
solvers rely on several simplifying assumptions. They limit their applicability
in numerous real-world domains that adopt nonholonomic car-like agents rather
than holonomic ones. In this paper, we give a mathematical formalization of
Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. For the first
time, we propose a novel hierarchical search-based solver called Car-like
Conflict-Based Search to address this problem. It applies a body conflict tree
to address collisions considering shapes of the agents. We introduce a new
algorithm called Spatiotemporal Hybrid-State A* as the single-agent path
planner to generate path satisfying both kinematic and spatiotemporal
constraints. We also present a sequential planning version of our method for
the sake of efficiency. We compare our method with two baseline algorithms on a
dedicated benchmark containing 3000 instances and validate it in real-world
scenarios. The experiment results give clear evidence that our algorithm scales
well to a large number of agents and is able to produce solutions that can be
directly applied to car-like robots in the real world. The benchmark and source
code are released in https://github.com/APRIL-ZJU/CL-CBS
Efficient Trajectory Planning for Multiple Non-holonomic Mobile Robots via Prioritized Trajectory Optimization
In this paper, we present a novel approach to efficiently generate
collision-free optimal trajectories for multiple non-holonomic mobile robots in
obstacle-rich environments. Our approach first employs a graph-based
multi-agent path planner to find an initial discrete solution, and then refines
this solution into smooth trajectories using nonlinear optimization. We divide
the robot team into small groups and propose a prioritized trajectory
optimization method to improve the scalability of the algorithm. Infeasible
sub-problems may arise in some scenarios because of the decoupled optimization
framework. To handle this problem, a novel grouping and priority assignment
strategy is developed to increase the probability of finding feasible
trajectories. Compared to the coupled trajectory optimization, the proposed
approach reduces the computation time considerably with a small impact on the
optimality of the plans. Simulations and hardware experiments verified the
effectiveness and superiority of the proposed approach.Comment: 8 pages, to be published in IEEE Robotics and Automation Letters
(RA-L), source code is available at:
https://github.com/LIJUNCHENG001/multi_robot_traj_planne