Autonomous Robots最新文献

Robots should learn new tasks from humans. But how do humans convey what they want the robot to do? Existing methods largely rely on humans physically guiding the robot arm throughout their intended task. Unfortunately — as we scale up the amount of data — physical guidance becomes prohibitively burdensome. Not only do humans need to operate robot hardware but also modify the environment (e.g., moving and resetting objects) to provide multiple task examples. In this work we propose L2D2, a sketching interface and imitation learning algorithm where humans can provide demonstrations by drawing the task. L2D2 starts with a single image of the robot arm and its workspace. Using a tablet, users draw and label trajectories on this image to illustrate how the robot should act. To collect new and diverse demonstrations, we no longer need the human to physically reset the workspace; instead, L2D2 leverages vision-language segmentation to autonomously vary object locations and generate synthetic images for the human to draw upon. We recognize that drawing trajectories is not as information-rich as physically demonstrating the task. Drawings are 2-dimensional and do not capture how the robot’s actions affect its environment. To address these fundamental challenges the next stage of L2D2 grounds the human’s static, 2D drawings in our dynamic, 3D world by leveraging a small set of physical demonstrations. Our experiments and user study suggest that L2D2 enables humans to provide more demonstrations with less time and effort than traditional approaches, and users prefer drawings over physical manipulation. When compared to other drawing-based approaches, we find that L2D2 learns more performant robot policies, requires a smaller dataset, and can generalize to longer-horizon tasks. See our project website: https://collab.me.vt.edu/L2D2/

Mutual relative localization and identification are important features for multi-unmanned aerial vehicle (UAV) systems. Camera-based communications technology, also known as optical camera communications in the literature, is a novel technology that brings a valuable solution to this task. In such a system, the UAVs are equipped with LEDs acting as beacons, and with cameras to locate the LEDs of the other UAVs. Specific blinking sequences are assigned to the LEDs of each of the UAVs to uniquely identify them. This camera-based system is immune to radio frequency electromagnetic interference and operates in global navigation satellite-denied environments. In addition, the implementation of this system is inexpensive. In this article, we study in detail the capacity of this system and its limitations. Furthermore, we show how to construct blinking sequences for UAV LEDs to improve system performance. Finally, experimental results are presented to corroborate the analytical derivations.

UAV transitions across UGVs enable diverse air-ground cooperation (AGC) applications, such as cross-vehicle landing, delivery, and rescue. However, achieving precise and efficient transitions across multiple moving UGVs without prior knowledge of their trajectories remains highly challenging. This paper proposes COVER, a cross-vehicle transition framework for quadrotor control in AGC scenarios. In COVER, the UAV is directly controlled in UGVs’ body frames as non-inertial frames, thus eliminating all dependencies in the world frame. Each transition process is divided into three stages: the initial stage, transition stage, and final stage, with pre-set stage transition points and stage-varying system states. Then, an optimal reference trajectory is generated at each stage by solving a non-linear programming (NLP) problem. The effect of the target UGV’s rotation on the initial relative velocity is eliminated to obtain a dynamically feasible and smooth transition reference trajectory. Finally, we design a stage-adaptive model predictive control (SAMPC) method, proposing a novel MPC position reference mode to avoid indirect routes at the transition stage. The SAMPC method effectively mitigates the flight instability caused by reference frame transition and eliminates the effect of reference frame rotation at the transition stage. And it can flexibly adapt to accurate requirements at the final stage by switching position reference mode and adjusting cost weights. Simulation benchmarks and extensive real-world experiments validate that our approach can achieve smooth, short-distance, and accurate cross-vehicle operations.

We are considering the geometric amoebot model where a set of n amoebots is placed on the triangular grid. An amoebot is able to send information to its neighbors, and to move via expansions and contractions. Since amoebots and information can only travel node by node, most problems have a natural lower bound of (Omega (D)) where D denotes the diameter of the structure. Inspired by the nervous and muscular system, Feldmann et al. (Computat Biol 29(4):317–343, 2022) have proposed the reconfigurable circuit extension and the joint movement extension of the amoebot model with the goal of breaking this lower bound. In the joint movement extension, the way amoebots move is altered. Amoebots become able to push and pull other amoebots. Feldmann et al. (Computat Biol 29(4):317–343, 2022) demonstrated the power of joint movements by transforming a line of amoebots into a rhombus within (O(log n)) rounds. However, they left the details of the extension open. The goal of this paper is therefore to formalize and extend the joint movement extension. In order to provide a proof of concept for the extension, we develop centralized algorithms for two fundamental problems of modular robot systems: reconfiguration and locomotion. We approach these problems by defining meta-modules of rhombical and hexagonal shape, respectively. The meta-modules are capable of movement primitives like sliding, rotating, and tunneling. This allows us to simulate reconfiguration algorithms of various modular robot systems. Finally, we construct three amoebot structures capable of locomotion by rolling, crawling, and walking, respectively.

Maintaining energy sufficiency of a battery-powered robot system is essential for long-term missions. This capability should be flexible enough to deal with different types of environments and a wide range of missions, while constantly guaranteeing that the robot does not run out of energy. We present a framework based on Control Barrier Functions (CBFs) which provides an energy sufficiency layer that can be applied on a wide range of sample based path planners and provides guarantees on sufficiency of robot’s energy during mission execution. In practice, we smooth the output of an arbitrary path planner (i.e. a set of waypoints) using double sigmoid functions and then use CBFs to ensure energy sufficiency along the smoothed path, for robots described by single integrator and unicycle kinematics. We present results using a physics-based robot simulator, as well as with real robots with a full localization and mapping stack to show the validity of our approach.

In biological swarms, army ants and bees have demonstrated the ability to form functional structures for collaborative tasks. Achieving similar functionality with robot swarms requires forming connections between robots using electrical, magnetic, or mechanical means. Our research introduces the PuzzleBots–robot swarms equipped with passive coupling mechanisms that enable collective behavior. These mechanisms leverage the individual mobility and dexterity of each robot to achieve complex assemblies. By coupling together, PuzzleBots can form both rigid and flexible structures that significantly enhance their ability to navigate challenging terrains. Rigid structures offer high load-bearing and transportation capabilities, while flexible structures provide compliance with environmental geometries. We demonstrated that these assembled structures can be precisely controlled using our distributed Model Predictive Control framework. Our results show that passive coupling in robot swarms significantly improves the traversal capability on rough and discontinuous terrains compared with individual robots.

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains.

In this paper, we propose a learning-free approach for an autonomous robotic system to grasp, hand over, and regrasp previously unseen objects. The proposed framework includes two main components: a novel grasping detector to predict grasping poses directly from the point cloud and a reachability-aware handover planner to select the exchange pose and grasping poses for two robots. In the grasping detection stage, multiple superquadrics are first recovered at different positions within the object, representing the local geometric feature of the object. Our algorithm then exploits the tri-symmetry feature of superquadrics and synthesizes a list of antipodal grasps from each recovered superquadric. An evaluation model is designed to assess and quantify the quality of each grasp candidate. In the handover planning stage, the planner first selects grasping candidates that have high scores and a larger number of collision-free partners. Then the exchange location is computed by utilizing two signed distance fields (SDF) which model the reachability space for the pair of two robots. To evaluate the performance of the proposed method, we first run experiments on isolated and packed scenes to corroborate the effectiveness of our grasping detection method. Then the handover experiments are conducted on a dual-arm system with two 7 degrees of freedom (DoF) manipulators. The results indicate that our method shows better performance compared with the state-of-the-art, without the need for large amounts of training.

We present the design, implementation and testing of a multi-robot exploration algorithm for NASA’s upcoming Cooperative Autonomous Distributed Robotic Exploration (CADRE) lunar technology demonstration mission. The CADRE mission, among its various objectives, entails utilizing a trio of autonomous mobile robots to collaboratively explore and construct a map of a designated area of the lunar surface. Given the mission’s inherent constraints, including limited mission duration, constrained power resources, and restricted communication capabilities, we formulate an exploration algorithm to improve exploration efficiency, facilitate equitable workload distribution among individual agents, and minimize inter-robot communication. To achieve these requirements, we employ a semi-centralized exploration algorithm that partitions the unexplored area, regardless of its shape and size, into a series of non-overlapping partitions, assigning each partition to a specific robot for exploration. Each robot autonomously explores its designated region without intervention from other robots. We explore the design space of the proposed algorithm and evaluate its performance under diverse conditions in simulations. Finally, we validate the algorithm’s functionality through two sets of hardware experiments: the first utilizes prototype rovers using a ROS-based navigation software stack for feasibility testing, while the second employs high-fidelity development model rovers running CADRE’s custom flight-software stack for flight-like performance validation. Both sets of experiments are conducted in the Jet Propulsion Laboratory’s lunar-simulated rover testing facilities, demonstrating the algorithm’s robustness and readiness for lunar deployment.

Distributed multi-target tracking is a canonical task for multi-robot systems, encompassing applications from environmental monitoring to disaster response to surveillance. In many situations the unknown distribution of the targets in a search area is non-uniform, e.g., herds of animals moving together. This paper develops a novel distributed multi-robot multi-target tracking algorithm to effectively search for and track clustered targets. There are two key features. First, there are two parallel estimators, one to provide the best guess of the current states of targets and a second to provide a coarse, long-term distribution of clusters. Second, robots use the power diagram to divide the search space between agents in a way that effectively trades off between tracking detected targets within high density areas and searching for other potential targets. Extensive simulation experiments demonstrate the efficacy of the proposed method and show that it outperforms other approaches in tracking accuracy of clustered targets while maintain good performance for uniformly distributed targets.