This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarantees, regardless of the complexity of the underlying graph topology and the explicit time dependence of the objective function. The proposed framework systematically addresses a particularly challenging problem in multi-agent systems, i.e., decentralization of entangled dynamics among different agents, and it naturally supports multi-objective robotics and real-time implementations. To demonstrate its generality and effectiveness, the framework is implemented across three experiments, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions without any approximations, which is a long-standing open problem, and safe formation navigation in dense environments.

Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning over a look-ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.

As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude. We also demonstrate that our approach yields implementable trajectories in an experiment with a Crazyflie quadrotor.

This paper demonstrates that the safety override arising from the use of a barrier function can in some cases be needlessly restrictive. In particular, we examine the case of fixed-wing collision avoidance and show that when using a barrier function, there are cases where two fixed-wing aircraft can come closer to colliding than if there were no barrier function at all. In addition, we construct cases where the barrier function labels the system as unsafe even when the vehicles start arbitrarily far apart. In other words, the barrier function ensures safety but with unnecessary costs to performance. We therefore introduce model-free barrier functions which take a data driven approach to creating a barrier function. We demonstrate the effectiveness of model-free barrier functions in a collision avoidance simulation of two fixed-wing aircraft.

Powered by a pair of photovoltaic panels and designed to linger in the forest canopy continuously for months, SlothBot moves only when it must to measure environmental changes -- such as weather and chemical factors in the environment -- that can be observed only with a long-term presence. The proof-of-concept hyper-efficient robot, described May 21 at the International Conference on Robotics and Automation (ICRA) in Montreal, may soon be hanging out among treetop cables in the Atlanta Botanical Garden. "In robotics, it seems we are always pushing for faster, more agile and more extreme robots," said Magnus Egerstedt, the Steve W. Chaddick School Chair of the School of Electrical and Computer Engineering at the Georgia Institute of Technology and principal investigator for Slothbot. "But there are many applications where there is no need to be fast. You just have to be out there persistently over long periods of time, observing what's going on."

Both projects will be presented at the 2017 IEEE International Conference on Robotics and Automation (ICRA) May 29 -- June 3 in Singapore. In the first, five swarm quadcopters zip back and forth in formation, then change their behaviors based on user commands. The trick is to maneuver without smacking into each other or flying underneath another machine. If a robot cuts into the airstream of a higher flying quadcopter, the lower machine must quickly recover from the turbulent air or risk falling out of the sky. "Ground robots have had built-in safety'bubbles' around them for a long time to avoid crashing," said Magnus Egerstedt, the Georgia Tech School of Electrical and Computer Engineering professor who oversees the project.

On the road to an increasingly autonomous future, robots and AI systems will need to be programmed to instinctively avoid collisions when they take the wheel. But if bots are designed to be too careful, performance may suffer. A team at Georgia Tech has created new algorithms that aim to strike a balance between the two extremes, allowing robots to move in a swarm safely and efficiently. Collision avoidance is one of the most important considerations of autonomous vehicles and robots, but some researchers have pondered the ethics of allowing self-driving cars to break minor laws to keep things running smoothly. It follows that autonomous robots may need to relax their own "bubbles" of personal space a little, too.

A few years ago, Magnus Egerstedt was walking through the swarm robotics laboratory at the Georgia Institute of Technology, where he is associate director of research, feeling proud of the research spearheaded there, when a disturbing thought crossed his mind. "I began thinking about the robotics laboratories where people are doing things that matter. There's not even ten of them globally," Egerstedt says. "That's weird, because so many people are working on swarm robotics, but it takes money and people to drive research that matters. He immediately envisioned a way to give robotics researchers who aren't with those top labs access to top-lab capabilities. And he knew students at all levels, grade school to graduate school, could benefit as well. "I used as a model the Large Hadron Collider," Egerstedt says. "Physicists realized large particle colliders were too expensive to build separately, so they share.