Collision detection and recovery for aerial robots remain a challenge because of the limited space for sensors and local stability of the flight controller. We introduce a novel collision-resilient quadrotor that features a compliant arm design to enable free flight while allowing for one passive degree of freedom to absorb shocks. We further propose a novel collision detection and characterization method based on Hall sensors, as well as a new recovery control method to generate and track a smooth trajectory after a collision occurs. Experimental results demonstrate that the robot can detect and recover from high-speed collisions with various obstacles such as walls and poles. Moreover, it can survive collisions that are hard to detect with existing methods based on IMU data and contact models, for example, when colliding with unstructured surfaces, or being hit by a moving obstacle while hovering.
University of Michigan（密歇根大学）；University of Arizona（亚利桑那大学）
This paper develops and experimentally evaluates a navigation function for quadrotor formation flight that is resilient to abrupt quadrotor failures and other obstacles. The navigation function is based on modeling healthy quadrotors as particles in an ideal fluid flow. We provide three key contributions: (i) A Containment Exclusion Mode (CEM) safety theorem and proof which guarantees safety and formally specifies a minimum safe distance between quadrotors in formation, (ii) A real-time, computationally efficient CEM navigation algorithm, (iii) Simulation and experimental algorithm validation.…
National Cheng Kung University（成功大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
This paper investigates the resilient control, analysis, recovery, and operation of mobile robot networks in time-varying formation tracking under deception attacks on global positioning. Local and global tracking control algorithms are presented to ensure redundancy of the mobile robot network and to retain the desired functionality for better resilience. Lyapunov stability analysis is utilized to show the boundedness of the formation tracking error and the stability of the network under various attack modes. A performance index is designed to compare the efficiency of the proposed formation …
DuctAM: A Duct-Assisted Quadrotor-Based Aerial Manipulator Enabling High-Force Push-and-Pull Interactions
Nanyang Technological University（南洋理工大学）；NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics（南洋理工大学-维纳大学具身智能与机器人联合研究实验室）；VinUniversity（维纳大学）
Zhejiang University（浙江大学）；University of Pennsylvania（宾夕法尼亚大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）
Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust…
Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science（罗伯特·博世网络物理系统中心，印度科学研究院）；Department of Mechanical Engineering, Indian Institute of Science（机械工程系，印度科学研究院）
University of Southern California（南加州大学）；Virginia Tech（弗吉尼亚理工学院暨州立大学）
We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, the framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. The central controller augments the team with additional robots from the robot pool when simply repositioning robots fails to attain a user-specified level of desired coverage. The size of the local neighborhood around the failed robot and the desired coverage …
University of Southern California（南加州大学）；Georgia Institute of Technology（佐治亚理工学院）
We propose a framework for resilience in a networked heterogeneous multi-robot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable (\eg a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually est…
Failure-Aware Multi-Robot Coordination for Resilient and Adaptive Target Tracking
Department of Electrical and Computer Engineering, Drexel University（德雷塞尔大学电气与计算机工程系）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；GRASP Lab, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）
