• Corpus ID: 8792991

A message-passing algorithm for multi-agent trajectory planning

@article{Bento2013AMA,
  title={A message-passing algorithm for multi-agent trajectory planning},
  author={Jos{\'e} Bento and Nate Derbinsky and Javier Alonso-Mora and Jonathan S. Yedidia},
  journal={ArXiv},
  year={2013},
  volume={abs/1311.4527}
}
We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM). Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments. We apply our method to classical challenging instances and observe that its computational requirements scale well… 

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References

SHOWING 1-10 OF 31 REFERENCES
ClearPath: highly parallel collision avoidance for multi-agent simulation
TLDR
The approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem and uses a discrete optimization method to efficiently compute the motion of each agent.
Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach
TLDR
An algorithm that generates collision-free trajectories in three dimensions for multiple vehicles within seconds using sequential convex programming that approximates non-convex constraints by using convex ones is presented.
Collision avoidance for multiple agents with joint utility maximization
TLDR
The proposed algorithm can provide a benchmark for distributed collision avoidance methods, in particular for those based on the VO concept that take interaction into account and is applicable to heterogeneous groups of agents moving in 2D and 3D spaces.
Randomized kinodynamic planning
  • S. LaValle, J. Kuffner
  • Mathematics
    Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
  • 1999
TLDR
A state-space perspective on the kinodynamic planning problem is presented, and a randomized path planning technique that computes collision-free kinodynamic trajectories for high degree-of-freedom problems is introduced.
Randomized Kinodynamic Planning
This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an
Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams
TLDR
An algorithm for the generation of optimal trajectories for teams of heterogeneous quadrotors in three-dimensional environments with obstacles in mixed-integer quadratic programs where the integer constraints are used to enforce collision avoidance.
Motion Planning in Dynamic Environments Using Velocity Obstacles
This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the
Real-time obstacle avoidance for manipulators and mobile robots
  • O. Khatib
  • Computer Science
    Proceedings. 1985 IEEE International Conference on Robotics and Automation
  • 1985
TLDR
This paper reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation.
Sampling-based algorithms for optimal motion planning
TLDR
The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.
...
1
2
3
4
...