Posts in Planning & Controls
Distributed Submodular Maximization on Partition Matroids for Planning on Large Sensor Networks

This paper provides an efficient and distributed algorithm for planning for multi-robot sensor coverage. Sensor coverage is ubiquitous in robotics and models, for example, the objects that robots will observe with their cameras as they move through an environment. Popular sequential algorithms for multi-robot coverage planning guarantee good performance but require increasing numbers of planning steps for large teams of robots. We propose a randomized algorithm that uses only a fixed number of planning steps and approaches the performance of sequential planning when dependencies between agents decrease with distance.

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Online planning for human – multi-robot interactive theatrical performance

This paper describes a full system for controlling multi-robot teams online, meaning that plans do not have to be designed prior to operation. The system is specifically illustrated with quadrotors in the context of an improvisational theatrical performance. The robot system interprets commands received from a human operator into dynamically feasible and collision free trajectories, and smoothly transitions from currently executing plans to the new trajectories in real time.

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Interactive Online Choreography for a Multi-Quadrotor System

This short workshop paper gives an overview of our system enabling a theatrical performance, performed with six quadrotors over three acts, and dynamically directed by a theatric performer. This means that all behaviors performed by the robots during the course of the 100+ behavior production were not prescripted, but commanded, planned for, and executed online in real time during the production. Plots of the acceleration and minimum clearance distance over the robot team show that the generated trajectories are safe and dynamically feasible, and still images from the performance (accompanying video link provided) highlight elements of the approach.

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Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Vehicle dynamics and model parameters are rarely well known in practice. Robot controllers are further challenged by actuator constraints and noisy state estimates. This paper presents a controller that efficiently learns vehicle dynamics while at the same time efficiently ensuring that the vehicle satisfies state and input constraints in the presence of state uncertainty. Tracking error improvements are shown while maintaining constraint satisfaction and flying a quadrotor through a windy environment.

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Reactive Collision Avoidance using Real-Time Local Gaussian Mixture Model Maps

A robot flying in unknown cluttered environments must be able to create a real-time map of its immediate surroundings and make sure that it does not collide into any obstacles. In this work, we propose representing the world as a mixture of Gaussian distributions, which enables us to represent the environment as a compressed and succinct representation. We use the geometric properties of this representation to enable efficient collision checking the robot surroundings.

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Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

A robot or a team of robots operating in large known environments require accurate knowledge of their exact location in the environment, in order to execute complex tasks. Limited size, weight and power constraints lead to constraints on the on-board computational capacity of aerial robots. This work presents a Monte-Carlo based real-time localization framework capable of running on such robots, enabled by using a compressed representation of the environment point cloud.

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Persistent Robot Formation Flight via Online Substitution

This work presents a means of ensuring continuous operation by computing non-colliding paths to guide robots in and out of a planned, long duration path, allowing robots to temporarily depart and recharge. The focus of our approach is to quickly generate collision-free paths that minimally diverge from the original plan. In this manner, a user could generate an extremely long-duration plan that neglects battery capacity limitations and rely on our approach to swap depleted and charged robots, ensuring continuous operation.

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Fast and Agile Vision-Based Flight with Teleoperation and Collision Avoidance on a Multirotor

We present a system capable of flying fast and with agility while avoiding obstacles and obeying user commands. Our flying hexarotor robot can be flown as aggressively as a racing drone by an untrained operator while actively avoiding collisions with the surrounding environment.

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Experience-Based Models of Surface Proximal Aerial Robot Flight Performance in Wind

We developed a strategy for an aerial robot to build a map of the wind strength and direction in its operating environment by aggregating its flight experiences into a non-parametric model. By considering this wind map in path planning, the aerial robot is able to move between points with greater safety and energy efficiency.

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