Multi-robot coordination is crucial for autonomous systems, yet real-world deployments often encounter various failures. These include both temporary and permanent disruptions in sensing and communication, which can significantly degrade system robustness and performance if not explicitly modeled. Despite its practical importance, failure-aware coordination remains underexplored in the literature. To bridge the gap between idealized conditions and the complexities of real-world environments, we propose a unified failure-aware coordination framework designed to enable resilient and adaptive multi-robot target tracking under both temporary and permanent failure conditions. Our approach systematically distinguishes between two classes of failures: (1) probabilistic and temporary disruptions, where robots recover from intermittent sensing or communication losses by dynamically adapting paths and avoiding inferred danger zones, and (2) permanent failures, where robots lose sensing or communication capabilities irreversibly, requiring sustained, decentralized behavioral adaptation. To handle these scenarios, the robot team is partitioned into subgroups. Robots that remain connected form a communication group and collaboratively plan using partially centralized nonlinear optimization. Robots experiencing permanent disconnection or failure continue to operate independently through decentralized or individual optimization, allowing them to contribute to the task within their local context. We extensively evaluate our method across a range of benchmark variations and conduct a comprehensive assessment under diverse real-world failure scenarios. Results show that our framework consistently achieves robust performance in realistic environments with unknown danger zones, offering a practical and generalizable solution for the multi-robot systems community.

Multi-robot collaboration for target tracking presents significant challenges in hazardous environments, including addressing robot failures, dynamic priority changes, and other unpredictable factors. Moreover, these challenges are increased in adversarial settings if the environment is unknown. In this paper, we propose a resilient and adaptive framework for multi-robot, multi-target tracking in environments with unknown sensing and communication danger zones. The damages posed by these zones are temporary, allowing robots to track targets while accepting the risk of entering dangerous areas. We formulate the problem as an optimization with soft chance constraints, enabling real-time adjustments to robot behavior based on varying types of dangers and failures. An adaptive replanning strategy is introduced, featuring different triggers to improve group performance. This approach allows for dynamic prioritization of target tracking and risk aversion or resilience, depending on evolving resources and real-time conditions. To validate the effectiveness of the proposed method, we benchmark and evaluate it across multiple scenarios in simulation and conduct several real-world experiments.

Motivated by the increasing use of quadrotors for payload delivery, we consider a joint trajectory generation and feedback control design problem for a quadrotor experiencing aerodynamic wrenches. Unmodeled aerodynamic drag forces from carried payloads can lead to catastrophic outcomes. Prior work model aerodynamic effects as residual dynamics or external disturbances in the control problem leading to a reactive policy that could be catastrophic. Moreover, redesigning controllers and tuning control gains on hardware platforms is a laborious effort. In this paper, we argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking for quadrotor systems experiencing drag forces. To achieve this, we formulate a drag-aware planning problem by applying a suitable relaxation to an optimal quadrotor control problem, introducing a tracking cost function which measures the ability of a controller to follow a reference trajectory. This tracking cost function acts as a regularizer in trajectory generation and is learned from data obtained from simulation. Our experiments in both simulation and on the Crazyflie hardware platform show that changing the planner reduces tracking error by as much as 83%. Evaluation on hardware demonstrates that our planned path, as opposed to a baseline, avoids controller saturation and catastrophic outcomes during aggressive maneuvers.

He also explains where he draws inspiration from in his research, and why robotics has yet to meet science fiction. Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering with appointments in the Departments of Mechanical Engineering and Applied Mechanics, Computer and Information Science, and Electrical and Systems Engineering at the University of Pennsylvania. Kumar's group works on creating autonomous ground and aerial robots, designing bio-inspired algorithms for collective behaviors, and on robot swarms. They have won many best paper awards at conferences, and group alumni are leaders in teaching, research, business and entrepreneurship. Kumar is a fellow of ASME and IEEE and a member of the National Academy of Engineering.

Vijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present. This conversation is part of the Artificial Intelligence podcast run by Lex Fridman.

Kumar discusses the guiding ideas behind his research on micro unmanned aerial vehicles, gives his thoughts on the future of robotics in the lab and field, and speaks about setting realistic expectations for robotics technology. Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering with appointments in the Departments of Mechanical Engineering and Applied Mechanics, Computer and Information Science, and Electrical and Systems Engineering at the University of Pennsylvania. Dr. Kumar received his Bachelor of Technology degree from the Indian Institute of Technology, Kanpur and his Ph.D. from The Ohio State University in 1987. He has been on the Faculty in the Department of Mechanical Engineering and Applied Mechanics with a secondary appointment in the Department of Computer and Information Science at the University of Pennsylvania since 1987. In his time at the university, Dr. Kumar has held numerous positions including director of the GRASP Laboratory, Chairman of the Department of Mechanical Engineering and Applied Mechanics, and Deputy Dean for Education in the School of Engineering and Applied Science.

Roboticists are putting a tremendous amount of time and effort into finding the right combination of sensors and algorithms that will keep their drones from smashing into things. It's a very difficult problem: With a few exceptions, you've got small platforms that move fast and don't have the payload capability for the kind of sensors or computers that you really need to do real-time avoidance of things like trees or powerlines. And without obstacle avoidance, how will we ever have drones that can deliver new athletic socks to our doorstep in 30 minutes or less? At the University of Pennsylvania's GRASP Lab, where they've been working very very hard at getting quadrotors to fly through windows without running into them, Yash Mulgaonkar, Luis Guerrero-Bonilla, Anurag Makineni, and Professor Vijay Kumar have come up with what seems to be a much simpler solution for navigation and obstacle avoidance with swarms of small aerial robots: Give them a roll cage, and just let them run into whatever is in their way. This kind of "it'll be fine" philosophy is what you find in most small flying insects, like bees: They don't worry all that much about bumbling into stuff, or each other, they just kind of shrug it off and keep on going.

Quadrotors are capable of doing some incredible stunts, like flying through narrow windows and thrown hoops. Usually, when we talk about quadrotors doing stuff like this, we have to point out that there are lots of very complicated and expensive sensors and computers positioned around the room doing all of the hard work, and the quadrotor itself is just following orders. Vijay Kumar's lab at the University of Pennsylvania is often responsible for some of the most spectacular quadrotor stunts, but their latest research is some of the most amazing yet: They've managed to get quadrotors flying through windows using only onboard sensing and computing, meaning that no window is safe from a quadrotor incursion. When you watch quadrotors flying indoors, if you look closely, you'll almost always see a motion-capture system in the background: Arrays of external cameras mounted on the walls that work together to collect very precise positional information hundreds of times every second. With the data that a system like this provides, a computer has no problem issuing very precise commands to a quadrotor flying under remote control to get it to do just about whatever you want.