Showing papers in "IEEE Transactions on Robotics in 2018"

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

Abstract: One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https://github.com/HKUST-Aerial-Robotics/VINS-Mono ) and iOS mobile devices ( https://github.com/HKUST-Aerial-Robotics/VINS-Mobile ).

A Survey on Aerial Swarm Robotics

Abstract: The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

Trajectory Planning for Quadrotor Swarms

Abstract: We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.

Discrete Cosserat Approach for Multisection Soft Manipulator Dynamics

Abstract: Nowadays, the most adopted model for the design and control of soft robots is the piecewise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model for multisection soft manipulator dynamics is presented based on a discrete Cosserat approach, in which the continuous Cosserat model is discretized by assuming a piecewise constant strain along the soft arm. As a consequence, the soft manipulator state is described by a finite set of constant strains. This approach has several advantages with respect to the existing models. First, it takes into account shear and torsional deformations, which are both essential to cope with out-of-plane external loads. Furthermore, it inherits desirable geometrical and mechanical properties of the continuous Cosserat model, such as intrinsic parameterization and greater generality. Finally, this approach allows to extend to soft manipulators, the recursive composite-rigid-body and articulated-body algorithms, whose performances are compared through a cantilever beam simulation. The soundness of the model is demonstrated through extensive simulation and experimental results.

Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

Abstract: We present an information-theoretic approach to stochastic optimal control problems that can be used to derive general sampling-based optimization schemes. This new mathematical method is used to develop a sampling-based model predictive control algorithm. We apply this information-theoretic model predictive control scheme to the task of aggressive autonomous driving around a dirt test track, and compare its performance with a model predictive control version of the cross-entropy method.

Fast, Generic, and Reliable Control and Simulation of Soft Robots Using Model Order Reduction

Abstract: Obtaining an accurate mechanical model of a soft deformable robot compatible with the computation time imposed by robotic applications is often considered an unattainable goal. This paper should invert this idea. The proposed methodology offers the possibility to dramatically reduce the size and the online computation time of a finite element model (FEM) of a soft robot. After a set of expensive offline simulations based on the whole model, we apply snapshot-proper orthogonal decomposition to sharply reduce the number of state variables of the soft-robot model. To keep the computational efficiency, hyperreduction is used to perform the integration on a reduced domain. The method allows to tune the error during the two main steps of complexity reduction. The method handles external loads (contact, friction, gravity, etc.) with precision as long as they are tested during the offline simulations. The method is validated on two very different examples of FEMs of soft robots and on one real soft robot. It enables acceleration factors of more than 100, while saving accuracy, in particular compared to coarsely meshed FEMs and provides a generic way to control soft robots.

An Efficient Acyclic Contact Planner for Multiped Robots

Abstract: We present a contact planner for complex legged locomotion tasks: standing up, climbing stairs using a handrail, crossing rubble, and getting out of a car. The need for such a planner was shown at the DARPA Robotics Challenge, where such behaviors could not be demonstrated (except for egress). Current planners suffer from their prohibitive algorithmic complexity because they deploy a tree of robot configurations projected in contact with the environment. We tackle this issue by introducing a reduction property: the reachability condition. This condition defines a geometric approximation of the contact manifold, which is of low dimension, presents a Cartesian topology, and can be efficiently sampled and explored. The hard contact planning problem can then be decomposed into two subproblems: first, we plan a path for the root without considering the whole-body configuration, using a sampling-based algorithm; then, we generate a discrete sequence of whole-body configurations in static equilibrium along this path, using a deterministic contact-selection algorithm. The reduction breaks the algorithm complexity encountered in previous works, resulting in the first interactive implementation of a contact planner (open source). While no contact planner has yet been proposed with theoretical completeness, we empirically show the interest of our framework: in a few seconds, with high success rates, we generate complex contact plans for various scenarios and two robots: HRP-2 and HyQ. These plans are validated in dynamic simulations or on the real HRP-2 robot.

Asymmetric Bimanual Control of Dual-Arm Exoskeletons for Human-Cooperative Manipulations

Abstract: In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator's motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject's movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject's muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.

Toward Dexterous Manipulation With Augmented Adaptive Synergies: The Pisa/IIT SoftHand 2

Abstract: In recent years, a clear trend toward simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon-driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine preshaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g., in terms of space and mechanical complexity. To overcome these limitations, we propose a novel design framework for tendon-driven mechanisms, in which the main idea is to turn transmission friction from a disturbance into a design tool. In this way, the degrees of actuation (DoAs) can be doubled with little additional complexity. By leveraging on this idea, we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two DoAs with hand softness, a large variety of grasping and manipulation tasks can be performed, only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, and pouring coffee from a glass.

Robust Visual Localization Across Seasons

Abstract: Localization is an integral part of reliable robot navigation, and long-term autonomy requires robustness against perceptional changes in the environment during localization. In the context of vision-based localization, such changes can be caused by illumination variations, occlusion, structural development, different weather conditions, and seasons. In this paper, we present a novel approach for localizing a robot over longer periods of time using only monocular image data. We propose a novel data association approach for matching streams of incoming images to an image sequence stored in a database. Our method exploits network flows to leverage sequential information to improve the localization performance and to maintain several possible trajectories hypotheses in parallel. To compare images, we consider a semidense image description based on histogram of oriented gradients features as well as global descriptors from deep convolutional neural networks trained on ImageNet for robust localization. We perform extensive evaluations on a variety of datasets and show that our approach outperforms existing state-of-the-art approaches.

Cooperative Collision Avoidance for Nonholonomic Robots

Abstract: In this paper, we present a method, namely $\epsilon$ CCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for smooth and safe control. The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot and account for a grid-based map representation of the environment. The method is best suited for large multirobot settings, including heterogeneous teams of robots, in which computational complexity is of paramount importance and the robots interact with one another. In particular, we consider a set of motion primitives for the robot and solve an optimization in the space of control velocities with additional constraints. Additionally, we propose a cooperative approach to compute safe velocity partitions in the distributed case. We describe several instances of the method for distributed and centralized operation and formulated both as convex and nonconvex optimizations. We compare the different variants and describe the benefits and tradeoffs both theoretically and in extensive experiments with various robotic platforms: robotic wheelchairs, robotic boats, humanoid robots, small unicycle robots, and simulated cars.

Multicontact Locomotion of Legged Robots

Abstract: Locomotion of legged robots on arbitrary terrain using multiple contacts is yet an open problem. To tackle it, a common approach is to rely on reduced template models (e.g., the linear inverted pendulum). However, most of existing template models are based on some restrictive hypotheses that limit their range of applications. Moreover, reduced models are generally not able to cope with the constraints of the robot complete model, such as the kinematic limits. In this paper, we propose a complete solution relying on a generic template model, based on the centroidal dynamics, able to quickly compute multicontact locomotion trajectories for any legged robot on arbitrary terrains. The template model relies on exact dynamics and is thus not limited by arbitrary assumption. We also propose a generic procedure to handle feasibility constraints due to the robot's whole body as occupancy measures, and a systematic way to approximate them using offline learning in simulation. An efficient solver is finally obtained by introducing an original second-order approximation of the centroidal wrench cone. The effectiveness and the versatility of the approach are demonstrated in several multicontact scenarios with two humanoid robots both in reality and in simulation.

Fourier-Based Shape Servoing: A New Feedback Method to Actively Deform Soft Objects into Desired 2-D Image Contours

Abstract: This paper addresses the design of a vision-based method to automatically deform soft objects into desired two-dimensional shapes with robot manipulators. The method presents an innovative feedback representation of the object's shape (based on a truncated Fourier series) and effectively exploits it to guide the soft object manipulation task. A new model calibration scheme that iteratively approximates a local deformation model from vision and motion sensory feedback is derived; this estimation method allows us to manipulate objects with unknown deformation properties. Pseudocode algorithms are presented to facilitate the implementation of the controller. Numerical simulations and experiments are reported to validate this new approach.

Joint Coverage, Connectivity, and Charging Strategies for Distributed UAV Networks

Abstract: This paper proposes deployment strategies for consumer unmanned aerial vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are as follows. 1) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. 2) A distributed game-theory-based scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Furthermore, a bio-inspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. 3) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. This paper demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.

Dynamic Humanoid Locomotion: A Scalable Formulation for HZD Gait Optimization

Abstract: Hybrid zero dynamics (HZD) has emerged as a popular framework for dynamic walking but has significant implementation difficulties when applied to the high degrees of freedom humanoids. The primary impediment is the process of gait design—it is difficult for optimizers to converge on a viable set of virtual constraints defining a gait. This paper presents a methodology that allows for fast and reliable generation of dynamic robotic walking gaits through the HZD framework, even in the presence of underactuation. Specifically, we describe an optimization formulation that builds upon the novel combination of HZD and direct collocation methods. Furthermore, achieving a scalable implementation required developing a defect-variable substitution formulation to simplify expressions, which ultimately allows us to generate compact analytic Jacobians of the constraints. We experimentally validate our methodology on an underactuated humanoid, DURUS, a spring-legged machine designed to facilitate energy-economical walking. We show that the optimization approach, in concert with the HZD framework, yields dynamic and stable walking gaits in hardware with a total electrical cost of transport of 1.33.

Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification

Abstract: Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling.

Development of the Humanoid Disaster Response Platform DRC-HUBO+

Abstract: This paper describes a humanoid robotics platform (DRC-HUBO+) developed for the Defense Advanced Research Projects Agency Robotics Challenge (DRC) Finals. This paper also describes the design criteria, hardware, software framework, and experimental testing of the DRC-HUBO+ platform. The purpose of DRC-HUBO+ is to perform tasks by teleoperation in hazardous environments that are unsafe for humans, such as disaster zones. We identified specific design concepts for DRC-HUBO+ to achieve this goal. For a robot to be capable of performing human tasks, a human-like shape and size, autonomy, mobility, manipulability, and power are required, among other features. Furthermore, modularized joints and a user-friendly software framework were emphasized as design concepts to facilitate research on the robot tasks. The DRC-HUBO+ platform is based on DRC-HUBO-1 and HUBO-2. The torque of each joint is increased compared to that in DRC-HUBO-1 owing to its high reduction ratio and air-cooling system. DRC-HUBO+ is designed with an exoskeletal structure to provide it with sufficient stiffness relative to its mass. All wires are enclosed within the robot body using a hollow shaft and covers to protect the wires from external shock. Regarding the vision system, active cognition of the environment can be realized using a light-detection and ranging sensor and vision cameras on the head. To achieve stable mobility, the robot can transition from the bipedal walking mode to the wheel mode using wheels located on both knees. DRC-HUBO+ has 32 degrees of freedom (DOFs), including seven DOFs for each arm and six DOFs for each leg, and a solid and light body with a height of 170 cm and a mass of 80 kg. A software framework referred to as PODO, with a Linux kernel and the Xenomai patch, is used in DRC-HUBO+.

Agile Coordination and Assistive Collision Avoidance for Quadrotor Swarms Using Virtual Structures

Abstract: This paper presents a method for controlling a swarm of quadrotors to perform agile interleaved maneuvers while holding a fixed relative formation, or transitioning between different formations. The method prevents collisions within the swarm, as well as between the quadrotors and static obstacles in the environment. The method is built upon the existing notion of a virtual structure, which serves as a framework with which to plan and execute complex interleaved trajectories, and also gives a simple, intuitive interface for a single human operator to control an arbitrarily large aerial swarm in real time. The virtual structure concept is integrated with differential flatness-based feedback control to give an end-to-end integrated swarm teleoperation system. Collision avoidance is achieved by using multiple layered potential fields. Our method is demonstrated in hardware experiments with groups of 3–5 quadrotors teleoperated by a single human operator, and simulations of 200 quadrotors teleoperated by a single human operator.

Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

Abstract: In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the $m$ -waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.

Parallel Continuum Robots: Modeling, Analysis, and Actuation-Based Force Sensing

Abstract: Parallel continuum robots (PCRs) combine the compactness, simplicity, and compliance of continuum robots with the precision and strength of rigid-link parallel robots. In this paper, we provide a generalized Cosserat-rod-based kinetostatic model framework that accommodates various joint types and problem formulations (e.g., forward and inverse kinematics under loads, and deflection-based and actuation-based force sensing) useful for simulation and control. Linearization of this general model provides the manipulator Jacobian, end-effector compliance, input stiffness, and wrench reflectivity matrices, which allow us to examine the effect of design parameters on dexterity, force application, and force-sensing ability. Using ellipsoids based on the matrices, we provide a set of design simulations and graphically depict the relationships between pose, actuation, and forces. We further provide a nondimensional analysis of the compliance of PCRs. Finally, we experimentally demonstrate and validate actuation-based force sensing on a prototype six-degree-of-freedom PCR, demonstrating 3-D force sensing with a median magnitude and a directional error of 0.23 N (8% of actual load) and 12 $^{\circ}$ , respectively.

Informed Sampling for Asymptotically Optimal Path Planning

Abstract: Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find paths to every state in the search domain. This is inefficient once an initial solution is found, as then only states that can provide a better solution need to be considered. Exact knowledge of these states requires solving the problem but can be approximated with heuristics. This paper formally defines these sets of states and demonstrates how they can be used to analyze arbitrary planning problems. It uses the well-known $L^2$ norm (i.e., Euclidean distance) to analyze minimum-path-length problems and shows that existing approaches decrease in effectiveness factorially (i.e., faster than exponentially) with state dimension. It presents a method to address this curse of dimensionality by directly sampling the prolate hyperspheroids (i.e., symmetric $n$ -dimensional ellipses) that define the $L^2$ informed set. The importance of this direct informed sampling technique is demonstrated with Informed RRT*. This extension of RRT* has less theoretical dependence on state dimension and problem size than existing techniques and allows for linear convergence on some problems. It is shown experimentally to find better solutions faster than existing techniques on both abstract planning problems and HERB, a two-arm manipulation robot.

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

Abstract: The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work Tasks must be allocated to agents and sequenced according to temporal and spatial constraints Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots In this paper, we present a centralized algorithm, named “Tercio,” that handles tightly intercoupled temporal and spatial constraints Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements

Abstract: Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

A New Approach to Time-Optimal Path Parameterization Based on Reachability Analysis

Abstract: Time-optimal path parameterization (TOPP) is a well-studied problem in robotics and has a wide range of applications. There are two main families of methods to address TOPP: numerical integration (NI) and convex optimization (CO). The NI-based methods are fast but difficult to implement and suffer from robustness issues, while CO-based approaches are more robust but, at the same time, significantly slower. Here, we propose a new approach to TOPP based on reachability analysis. The key insight is to recursively compute reachable and controllable sets at discretized positions on the path by solving small linear programs. The resulting algorithm is faster than NI-based methods and as robust as CO-based ones (100% success rate), as confirmed by extensive numerical evaluations. Moreover, the proposed approach offers unique additional benefits: admissible velocity propagation and robustness to parametric uncertainty can be derived from it in a simple and natural way.

Fundamental Actuation Properties of Multirotors: Force–Moment Decoupling and Fail–Safe Robustness

Abstract: In this paper, we shed light on two fundamental actuation capabilities of multirotors. The first is the degree of coupling between the total force and total moment generated by the propellers. The second is the ability to robustly fly completely still in place after the loss of one or more propellers, in the case of mono-directional propellers. These are formalized through the definition of some algebraic conditions on the control allocation matrices. The theory is valid for any multirotor, with arbitrary number, position, and orientation of the propellers. As a show case for the general theory, we demonstrate that standard star-shaped hexarotors with collinear propellers are not able to robustly fly completely still at a constant spot using only five of their six propellers. To deeply understand this counterintuitive result, it is enough to apply our theory, which clarifies the role of the tilt angles and locations of the propellers. The theory is also able to explain why, on the contrary, both the tilted star-shaped and the Y-shaped hexarotors can fly with only five out of six propellers. The analysis is validated with both simulations and extensive experimental results showing recovery control after rotor losses.

Robust Tactile Descriptors for Discriminating Objects From Textural Properties via Artificial Robotic Skin

Abstract: In this paper, we propose a set of novel tactile descriptors to enable robotic systems to extract robust tactile information during tactile object explorations, regardless of the number of the tactile sensors, sensing technologies, type of exploratory movements, and duration of the objects’ surface exploration. The performance and robustness of the tactile descriptors are verified by testing on four different sensing technologies (dynamic pressure sensors, accelerometers, capacitive sensors, and impedance electrode arrays) with two robotic platforms (one anthropomorphic hand and one humanoid), and with a large set of objects and materials. Using our proposed tactile descriptors, the Shadow Hand, which has multimodal robotic skin on its fingertips, successfully classified 120 materials (100% accuracy) and 30 in-hand objects (98% accuracy) with regular and irregular textural structure by executing human-like active exploratory movements on their surface. The robustness of the proposed descriptors was assessed further during the large object discrimination with a humanoid. With a large sensing area on its upper body, the humanoid classified 120 large objects with multiple weights and various textures while the objects slid between its sensitive hands, arms, and chest. The achieved 90% recognition rate shows that the proposed tactile descriptors provided robust tactile information from the large number of tactile signals for identifying large objects via their surface texture regardless of their weight.

Gait Design for a Snake Robot by Connecting Curve Segments and Experimental Demonstration

Abstract: This paper presents a method for designing the gait of a snake robot that moves in a complicated environment. We propose a method for expressing the target form of a snake robot by connecting curve segments whose curvature and torsion are already known. Because the characteristics of each combined shape are clear, we can design the target form intuitively and approximate a snake robot configuration to this form with low computational cost. In addition, we propose two novel gaits for the snake robot as a design example of the proposed method. The first gait is aimed at moving over a flange on a pipe, while the other is the crawler gait aimed at moving over rough terrain. We demonstrated the effectiveness of the two gaits on a pipe and rough terrain in experiments.

Controlling Noncooperative Herds with Robotic Herders

Abstract: We present control strategies for robotic herders to drive noncooperative herds. Our key insight enforces geometrical relationships that map the combined dynamics to simple two-dimensional or three-dimensional nonholonomic vehicle models. We prove convergence of single-agent herds to a goal and propose strategies for multi-agent herds, verified in simulations and experiments.

Comparative Peg-in-Hole Testing of a Force-Based Manipulation Controlled Robotic Hand

Abstract: Force-based manipulation control strategies are evolving as a primary mechanism in robotics for performing the fine manipulation tasks typical within manufacturing assembly. The ability to systematically compare robotic system performance and quantify true advancement in fine manipulation is of utmost importance. Accordingly, the objectives of this paper are threefold: 1) creation of a peg-in-hole test method with associated performance metrics and a systematic data analysis strategy for performance benchmarking, 2) first demonstration of a recently developed manipulation controller piloting a robotic hand and its paired task-level logic for completing the peg-in-hole test, and 3) exemplifying the performance benchmarking technique by comparing two approaches for robotic insertions—the previously mentioned compliant hand, stiff arm system, and a stiff gripper, compliant arm system. Analyses reveal that the unconventional hand system can perform at and sometimes above the level of the gripper system in the developed peg-in-hole scenario. Moreover, the hand's active control of the peg's full Cartesian pose reduces positional error sensitivity and minimizes exerted insertion forces, highlighting the strategy's potential for fine manipulation tasks. Results indicate that robotic arms equipped with highly articulated and sensorized robotic hands can provide a truly realizable solution path for performing peg-in-hole tasks.

A Novel Robotic Platform for Aerial Manipulation Using Quadrotors as Rotating Thrust Generators

Abstract: We propose a novel robotic platform for aerial operation and manipulation, a spherically connected multiquadrotor (SmQ) platform, which consists of a rigid frame and multiple quadrotors that are connected to the frame via passive spherical joints and act as distributed rotating thrust generators to collectively propel the frame by adjusting their attitude and thrust force. Depending on the number of quadrotors and their configuration, this SmQ platform can fully (or partially) overcome the issues of underactuation of the standard multirotor drones for aerial operation/manipulation (e.g., body-tilting with sideway gust/force, dynamic interaction hard to attain, complicated arm–drone integration, etc.). We present the dynamics modeling of this SmQ platform system and establish the condition for its full actuation in SE(3). We also show how to address limited range of spherical joints and rotor saturations as a constrained optimization problem by noticing the similarity with the multifingered grasping problem under the friction-cone constraint. We then design and analyze feedback control laws for the S3Q and S2Q systems as a combination of high-level Lyapunov control design and low-level constrained optimization and show that the (fully actuated) S3Q system can assume any trajectory in SE(3), whereas the S2Q system in $\Re^3\, \times$ S $^2$ with its unactuated dynamics is still internally stable. Experiments are also performed to show the efficacy of the theory.