ICRA 2015 Papers

This paper presents an approach for decentralized range-only simultaneous localization and mapping (RO-SLAM) of a network of aerial vehicles and a set of static range-only sensors deployed in the environment. The paper makes use of a multi-hypothesis framework developed by the authors [1] in order to deal with the multiple hypotheses that are present in the early stages of the undelayed RO-SLAM, this paper extends the approach to consider the integration of landmark estimations provided by other aerial vehicles nearby the robot. The method will enable a significant reduction in the convergence time needed to remove wrong localization hypotheses for every range-only landmark and, as a results, a map with improved accuracy. The proposed approach is validated first in simulations and later on with real experiments involving real range-only sensors and two unmanned aerial vehicles.

In this paper, a distributed approach for the estimation of kinematic and inertial parameters of an unknown rigid body is presented. The body is manipulated by a pool of ground mobile manipulators. Each robot retrieves a noisy measurement of its velocity and the contact forces applied to the body. Kinematics and dynamics arguments are used to distributively estimate the relative positions of the contact points. Subsequently, distributed estimation filters and nonlinear observers are used to estimate the body mass, the relative position between its geometric center and its center of mass, and its moment of inertia. The manipulation strategy is functional to the estimation process, and is suitably designed to satisfy nonlinear observability conditions that are necessary for the success of the estimation. Numerical results corroborate our theoretical findings.

This paper addresses the problem of cooperative object transportation by two nonholonomic wheeled robots, with the coordination relying exclusively on implicit communication. We implement a leader-follower scheme, considering compliant contact between the object and the follower. Only the leader has knowledge of the object's goal configuration. The follower employs force/torque measurements to keep the contact stable and align itself with the object. The control scheme of the follower is based on the prescribed performance methodology guaranteeing thus the satisfaction of certain predefined force/torque constraints. In this way, the overall system acts as a perturbed version of the nominal car-like model. As a result, the leader implements a discontinuous control scheme, that drives robustly the system arbitrarily close to the goal configuration. No explicit data is exchanged among the robots, thus reducing bandwidth and increasing robustness and stealthiness. Finally, the proposed method is experimentally validated using two Pioneer mobile robots interconnected with a rod.

This paper presents a novel, decentralized, control-theoretic approach to address collision avoidance for multi-robot systems. We create a virtual obstacle at the mean position of the robots. A control is be designed such that each robot will avoid the closest obstacle when a collision is possible. The closest obstacle can be the virtual obstacle or the nearest robot. We present two such control laws. The first assumes perfect knowledge of the velocities of all nearby robots and can allow a saturated velocity input for each robot. In practice, the velocities of the other robots are hard to measure or estimate precisely. Therefore, the second control law removes the assumption of known velocities based on a high-gain, robust control scheme. We prove the first control scheme is globally asymptotically stable, and the robust control law is globally uniformly ultimately bounded. To verify the effectiveness of the proposed approach, Monte Carlo simulations and experiments have been conducted.

In this paper we propose an algorithm for decentralized control of Automated Guided Vehicles (AGVs) operating in automated warehouse environments. The motion planning part of the algorithm provides vehicles with capabilities for autonomous motion planning considering nonholonomic vehicle constraints and collision-free path execution. The decision making part of the algorithm ensures safe vehicle motions and reliable conflict situation resolution. The proposed control algorithm also prevents occurrence of deadlock and livelock situations. Stability of the algorithm has been proven by its analysis based on automata theory, while its performance has been validated by simulation on a system comprising twenty vehicles as well as experimentally on a laboratory setup comprising five Pioneer 3DX vehicles.

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems.

This paper presents distributed algorithms for formation control of multiple robots in three dimensions. In particular, we leverage the mathematical properties of cyclic pursuit along with results from contraction and partial contraction theory to design distributed control algorithms ensuring global convergence to symmetric formations. As a base case we consider regular polygons as desired formations and then provide extensions to Johnson solid formations. Finally, we analyze the robustness of the control algorithms under bounded additive disturbances and provide performance bounds with respect to the formation error.

This paper addresses the problem of controlling mobile sensing systems to improve the accuracy and efficiency of gathering information autonomously. It applies to scenarios such as environmental monitoring, search and rescue, surveillance and reconnaissance, and simultaneous localization and mapping (SLAM). A multi-sensor active information acquisition problem, capturing the common characteristics of these scenarios, is formulated. The goal is to design sensor control policies which minimize the entropy of the estimation task, conditioned on the future measurements. First, we provide a non-greedy centralized solution, which is computationally fast, since it exploits linearized sensing models, and memory efficient, since it exploits sparsity in the environment model. Next, we decentralize the control task to obtain linear complexity in the number of sensors and provide suboptimality guarantees. Finally, our algorithms are applied to the multi-robot active SLAM problem to enable a decentralized nonmyopic solution that exploits sparsity in the planning process.

The development of variable impedance actuators (VIAs) has highlighted the need for proper control of passive impedance to attain suitable interaction performance. Until recently the regulation of the intrinsic impedance in VIAs is achieved in an open-loop model-based manner, mainly due to the lack of physical sensors capable of measuring impedance components such as stiffness and damping. Hence, the estimation of variable stiffness and damping has been explored, with the target to provide monitoring and feedback for potential closed loop control schemes. However, the use of the output of these estimators in the feedback control of variable impedance actuators has never been implemented/demonstrated in practice. This work contributes to the field with the development and experimental evaluation of a novel damping feedback control for a class of variable impedance compliant actuators able to realize a variable physical damping principle. The scheme is based on non-model-based damping estimation feedback to compensate model uncertainties in the action of an inner controller that uses a model-based friction estimator. Experimental results demonstrate the ability of the proposed scheme to replicate with good fidelity constant and time-varying damping levels.

Series-Parallel Elastic Actuators (SPEA) enable variable recruitment of parallel springs and variable load cancellation. In previous work, we validated a MACCEPA-based SPEA prototype with a self-closing intermittent mechanism, to reduce motor load and improve energy efficiency. However, the mechanism only allowed for 4 parallel springs and a limited equilibrium angle range, which limits the variable load cancellation and operation range. Therefore, we developed a novel cylindrical cam mechanism for unlimited subsequent spring recruitment. This paper describes and validates the working principle of the cylindrical cam mechanism. Furthermore, the latest MACCEPA-based SPEA is presented with a maximum output torque of 40Nm and variable stiffness. Additive and traditional manufacturing techniques go hand in hand to overcome the actuator's complexity. The experiments endorse the working principle, demonstrate the variable stiffness, and prove the motor torque can be reduced to 5Nm while an output torque of 40Nm can be achieved.

Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semi-structured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

In continuously operating robotic systems, efficient representation of the previously seen camera feed is crucial. Using a highly efficient compression coreset method, we formulate a new method for hierarchical retrieval of frames from large video streams collected online by a moving robot. We demonstrate how to utilize the resulting structure for efficient loop-closure by a novel sampling approach that is adaptive to the structure of the video. The same structure also allows us to create a highly-effective search tool for large-scale videos, which we demonstrate in this paper. We show the efficiency of proposed approaches for retrieval and loop closure on standard datasets, and on a large-scale video from a mobile camera.

In this work, we develop a cooperative launching system for a 13.2 gram ornithopter micro-aerial vehicle (MAV), the H2Bird, by carrying it on the back of a 32 gram hexapedal millirobot, the VelociRoACH. We determine the necessary initial velocity and pitch angle for take off using force data collected in a wind tunnel and use the VelociRoACH to reach these initial conditions for successful launch. In the wind tunnel predicted success region, we were able to complete a successful launch for 75 percent of the 12 trials in that region. Although carrying the H2Bird on top of the VelociRoACH at a stride frequency of 17 Hz increases our average power consumption by about 24.5 percent over solo running, the H2Bird, in turn, provides stability advantages to the VelociRoACH. We observed that the variance in pitch and roll velocity with the H2Bird is about 90 percent less than without. Additionally, with the H2Bird flapping at 5 Hz during transport, we observed an increase of 12.7 percent of the steady state velocity. Lastly, we found that the costs of transport for carrying the H2Bird flapping and without (6.6 and 6.8) are lower than the solo costs of transport for the VelociRoACH and for the H2Bird (8.1 and 10.1).

For a team of mobile robots with limited onboard resources, we propose a partially decentralized implementation of an extended Kalman filter for cooperative localization. In the proposed algorithm, unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single device, we only require that the robots communicate with a central command unit at the time of a measurement update. In addition, the computational and storage cost per robot in terms of the size of the team is reduced to O(1). Moreover, the algorithm is robust to occasional in-network communication link failures while the estimation update of the robots receiving the update message is of minimum variance. We demonstrate the performance of the algorithm in simulations.

This paper reports on an underwater cooperative localization algorithm for faulty low-bandwidth communication channels based on a factor graph estimation framework. Vehicles measure the one-way-travel-time (OWTT) of acoustic broadcasts to obtain a relative range observation to the transmitting vehicle. We present a method to robustly share locally observed sensor data across the network by exploiting odometry factor composition. Our algorithm calls on approximate marginalization techniques to compute a compact set of informative factors that enable local navigation data to be shared efficiently. We provide results from a real-time implementation of our algorithm using two autonomous underwater vehicles and a surface vehicle.

The fusion of IMU and RGB-D sensors presents an interesting combination of information to achieve autonomous localization and mapping using robotic platforms such as ground robots and flying vehicles. In this paper, we present a software framework for cooperative localization and mapping while simultaneously using multiple aerial platforms. We employ a monocular visual odometry algorithm to solve the localization task, where the depth data flow associated to the RGB image is used to estimate the scale factor associated with the visual information. The current framework enables autonomous onboard control of each vehicle with cooperative localization and mapping. We present a methodology that provides both a sparse map generated by the monocular SLAM and a multiple resolution dense map generated by the associated depth. The localization algorithm and both 3D mapping algorithms work in parallel improving the system real-time reliability. We present experimental results to show the effectiveness of the proposed approach using two quadrotors platforms.

Deadlock prevention policies for flexible manufacturing systems (FMS) usually suffer from redundant monitors since some monitors may be added to the siphons that are originally controlled. To eliminate these redundant monitors, the controllability condition of siphons is studied in this work. For a class of Petri nets called Systems of Simple Sequential Processes with Resources (S3PR), new concepts of simple and complex strict minimal siphons (SMS) are introduced. More importantly, a necessary and sufficient condition of controllability of complex SMS is proposed for the first time. Based on this condition, an algorithm is given to control all SMS via M-controlling part of them and a structurally simple optimal liveness-enforcing supervisor can thus be synthesized for some classes of S3PR. Two examples are provided to illustrate the application of the proposed algorithm and show its superiority to prior work.

This paper addresses the problem of optimal control of a unicycle-type robot perturbed with stochastic noise in an environment with sparsely populated obstacles. The objective is that the robot pose converges to a neighborhood of a desired position and orientation. A feedback control law is constructed such that it is compatible with the differential constraints of the unicycle. The construction is based on numerical solution of the Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE) associated with a stochastic optimal control problem. The control law is optimal in terms of control effort and comes with probabilistic guarantees of convergence to the goal set.

During human-robot interaction tasks, a human may physically touch a robot and engage in a collaboration phase with exchange of contact forces and/or requiring coordinated motion of a common contact point. Under the premise of keeping the interaction safe, the robot controller should impose a desired motion/force behavior at the contact or explicitly regulate the contact forces. Since intentional contacts may occur anywhere along the robot structure, the ability of controlling generalized contact motion and force becomes an essential robot feature. In our recent work, we have shown how to estimate contact forces without an explicit force sensing device, relying on residual signals to detect contact and on the use of an external (depth) sensor to localize the contact point. Based on this result, we introduce two control schemes that generalize the impedance and direct force control paradigms to a generic contact location on the robot, making use of the estimated contact forces. The issue of human-robot task compatibility is pointed out in case of control of generalized contact forces. Experimental results are presented for a KUKA LWR robot using a Kinect sensor.