This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.
This paper presents a novel control strategy to herd a group of non-cooperative evaders by means of a team of robotic herders. In herding problems, the motion of the evaders is typically determined by strong nonlinear reactive dynamics, escaping from the herders. Many applications demand the herding of numerous and/or heterogeneous entities, making the development of flexible control solutions challenging. In this context, our main contribution is a control approach that finds suitable herding actions even when the nonlinearities in the evaders' dynamics yield to implicit equations. We resort …
This paper presents a novel control strategy to herd groups of non-cooperative evaders by means of a team of robotic herders. In herding problems, the motion of the evaders is typically determined by strongly nonlinear and heterogeneous reactive dynamics, which makes the development of flexible control solutions a challenging problem. In this context, we propose Implicit Control, an approach that leverages numerical analysis theory to find suitable herding inputs even when the nonlinearities in the evaders' dynamics yield to implicit equations. The intuition behind this methodology consists in…
A leader-follower framework is proposed for multi-robot navigation of large scale teams where the leader agents corral the follower agents. A group of leaders is modeled as a 2D deformable object where discrete masses (i.e., leader robots) are interconnected by springs and dampers. A time-varying domain is defined by the positions of leaders while the external forces induce deformations of the domain from its nominal configuration. The team of followers is performing coverage over the time-varying domain by employing a perspective transformation that maps between the nominal and deformed confi…
UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
Department of Aerospace Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校航空航天工程系）；School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；NVIDIA Research（NVIDIA研究）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）
Multi-robot obstacle-aware shepherding of non-cohesive target agents
Scuola Superiore Meridionale（南方高等学院）；Centre for the Advanced Study of Collective Behaviour, Universität Konstanz（康斯坦茨大学集体行为高级研究中心）；Department of Collective Behaviour, Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为学研究所集体行为系）；Department of Electrical Engineering and Information Technology, University of Naples Federico II（那不勒斯费德里科二世大学电气与信息工程系）
IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore（机械工程系，设计与工程学院，新加坡国立大学）；Singapore Technologies Engineering Ltd（新加坡科技工程有限公司）
Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of all of the robots in the space. The dimensionality of this space increases with the number of robots, rendering this approach impractical. This work focuses on a scalable sampling-based planner for coupled multi-robot problems that provides asymptotic optimality. It extends the dRRT approach, which proposed building roadmaps for each robot and searching an implicit roadmap in the composite configuration space. This w…
Optimal Control for Clutched-Elastic Robots: A Contact-Implicit Approach
Chair of Robotics and Systems Intelligence（机器人与系统智能主席席位）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Technical University of Munich（慕尼黑工业大学）
Gaussian Process Implicit Surfaces as Control Barrier Functions for Safe Robot Navigation
School of Electrical and Computer Engineering, Georgia Institute of Technology（电子与计算机工程学院，佐治亚理工学院）；Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University（电子与生物信息学系，明治大学）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant challenge due to their functional complexity and requires adaptation to various terrains. Recently, deep reinforcement learning, inspired by how legged animals learn to walk from their experiences, has been utilized to synthesize natural quadrupedal locomotion. However, state-of-the-art methods strongly depend on a complex and reliable sensing framework. Furthermore, prior works that rely only on proprioception have…
We present a general approach for controlling robotic systems that make and break contact with their environments. Contact-implicit model predictive control (CI-MPC) generalizes linear MPC to contact-rich settings by utilizing a bi-level planning formulation with lower-level contact dynamics formulated as time-varying linear complementarity problems (LCPs) computed using strategic Taylor approximations about a reference trajectory. These dynamics enable the upper-level planning problem to reason about contact timing and forces, and generate entirely new contact-mode sequences online. To achiev…
The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people's goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual con…
