26 research outputs found
Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies
Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation.
Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety.
In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors.
We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions
Downwash-Aware Trajectory Planning for Large Quadrotor Teams
We describe a method for formation-change trajectory planning for large
quadrotor teams in obstacle-rich environments. Our method decomposes the
planning problem into two stages: a discrete planner operating on a graph
representation of the workspace, and a continuous refinement that converts the
non-smooth graph plan into a set of C^k-continuous trajectories, locally
optimizing an integral-squared-derivative cost. We account for the downwash
effect, allowing safe flight in dense formations. We demonstrate the
computational efficiency in simulation with up to 200 robots and the physical
plausibility with an experiment with 32 nano-quadrotors. Our approach can
compute safe and smooth trajectories for hundreds of quadrotors in dense
environments with obstacles in a few minutes.Comment: 8 page
Recycling controllers
The problem of designing control schemes for teams of robots to satisfy complex high-level tasks is a challenging problem which becomes more difficult when adding constraints on relative locations of robots. This paper presents a method for automatically creating hybrid controllers that ensure a team of heterogeneous robots satisfy some user specified high-level task while guaranteeing collision avoidance and predicting and reducing deadlock. The generated hybrid controller composes atomic controllers based on information the robots gather during runtime; thus these atomic controllers can be reused in different scenarios for multiple tasks. As a demonstration of this general approach we examine a task in which a group of robots sort different items to be recycled
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
The Multi-Agent Pickup and Delivery (MAPD) problem models applications where
a large number of agents attend to a stream of incoming pickup-and-delivery
tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and
effective. We make TP even more efficient and effective by using a novel
combinatorial search algorithm, called Safe Interval Path Planning with
Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an
advanced data structure that allows for fast updates and lookups of the current
paths of all agents in an online setting. The resulting MAPD algorithm
TP-SIPPwRT takes kinematic constraints of real robots into account directly
during planning, computes continuous agent movements with given velocities that
work on non-holonomic robots rather than discrete agent movements with uniform
velocity, and is complete for well-formed MAPD instances. We demonstrate its
benefits for automated warehouses using both an agent simulator and a standard
robot simulator. For example, we demonstrate that it can compute paths for
hundreds of agents and thousands of tasks in seconds and is more efficient and
effective than existing MAPD algorithms that use a post-processing step to
adapt their paths to continuous agent movements with given velocities.Comment: AAAI 201