Control barrier functions are widely used to enforce safety properties in robot motion planning and control. However, the problem of constructing barrier functions online and synthesizing safe controllers that can deal with the associated uncertainty has received little attention. This paper investigates safe navigation in unknown environments, using onboard range sensing to construct control barrier functions online. To represent different objects in the environment, we use the distance measurements to train neural network approximations of the signed distance functions incrementally with replay memory. This allows us to formulate a novel robust control barrier safety constraint which takes into account the error in the estimated distance fields and its gradient. Our formulation leads to a second-order cone program, enabling safe and stable control synthesis in a priori unknown environments.
Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（机械工程与应用力学系，宾夕法尼亚大学）；Department of Electrical Engineering, Chalmers University of Technology（电气工程系，查尔姆斯理工大学）
This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping…
Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；Department of Engineering for Innovation Medicine, University of Verona（威尼斯大学创新医学工程系）；Massachusetts Institute of Technology（麻省理工学院）
With the increasing emphasis on the safe autonomy for robots, model-based safe control approaches such as Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. Instead of assuming cooperative and homogeneous robots using the same safe controllers, the ego robot is able to model the neighboring ro…
Arizona State University（亚利桑那州立大学）；Toyota Research Institute of North America（丰田北美研究院）
Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have been widely used for generating motion paths for autonomous mobile systems. In this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to generate, safe motion plans in dynamic environments with many pedestrians. Our framework is based upon a human motion prediction model which is well suited for indoor narrow environments. We demonstrate our approach on a high-fidelity model of the Toyota Human Support Robot navigating in narrow corridors. We show in three scenarios that our proposed online method can…
Control Barrier Corridors: From Safety Functions to Safe Sets
Department of Mechanical Engineering, Eindhoven University of Technology（埃因霍温理工大学机械工程系）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
Dynamic Log-Gaussian Process Control Barrier Function for Safe Robotic Navigation in Dynamic Environments
School of Intelligence Science and Engineering, Harbin Institute of Technology（智能科学与工程学院，哈尔滨工业大学）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程学院，哈尔滨工业大学）
This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis. We will consider the difference with a focus on the following aspects: generality of system dynamics, difficulty of construction and computation cost. A standard Dubins car model will be evaluated numerically to make the comparison more concrete.
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Massachusetts Institute of Technology（麻省理工学院）
This work applies universal adaptive control to control barrier functions to achieve forward invariance of a safe set despite the presence of unmatched parametric uncertainties. The approach combines two ideas. The first is to construct a family of control barrier functions that ensures the system is safe for all possible models. The second is to use online parameter adaptation to methodically select a control barrier function and corresponding safety controller from the allowable set. While such a combination does not necessarily yield forward invariance without additional requirements on the…
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control barrier functions embedded into the reinforcement learning policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle. However, control barrier functions need a good approximation of the model of the car. We use probabilistic control barrier functions as an estimate of the model uncertainty. The algorithm is implemented as an online versio…
This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems. CBFs are usually overly conservative, while guaranteeing safety. Here, we address their conservativeness by softening their definitions using environmental dependencies without loosing safety guarantees, and embed them into differentiable quadratic programs. These novel safety layers, termed a BarrierNet, can be used in conjunction with any neural network-based controller, and can be trained by gradient descent. BarrierNet allows the safety constraints…
Arizona State University（亚利桑那州立大学）；Toyota Research Institute of North America（丰田北美研究院）
In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles a…
Robert Bosch Center for Cyber-Physical Systems（罗伯特·博世信息物理系统研究中心）；Department of Aerospace Engineering（航空航天工程系）；Indian Institute of Science（印度科学学院）
Aerial package transportation often requires complex spatial and temporal specifications to be satisfied in order to ensure safe and timely delivery from one point to another. It is usually efficient to transport versatile payloads using multiple UAVs that can work collaboratively to achieve the desired task. The complex temporal specifications can be handled coherently by applying Signal Temporal Logic (STL) to dynamical systems. This paper addresses the problem of waypoint navigation of a multi-UAV payload system under temporal specifications using higher-order time-varying control barrier f…
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
Department of Robotics, University of Michigan（机器人学系，密歇根大学）；Toyota North America Research & Development（丰田北美研发）；Department of Robotics and the Department of Aerospace Engineering at University of Michigan（机器人学系和航空航天工程系，密歇根大学）
As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematic…
Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
Mechanical Engineering and Mechanics Department at Lehigh University（莱维大学机械工程与力学系）；Department of Mechanical Engineering at University of California Riverside（加州大学河滨分校机械工程系）
REASAN: Learning Reactive Safe Navigation for Legged Robots
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；Department of Electrical Engineering, Linköping University, Sweden（利厄普大学电气工程系）；Engineering and Technology Institute Groningen, University of Groningen, The Netherlands（格罗宁根大学格罗宁根工程与技术研究所）
Noah’s Ark Lab, Huawei Technologies Canada, Ltd.（华为技术加拿大有限公司诺亚方舟实验室）；University of Alberta（阿尔伯塔大学）
We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experi…
