ICRA 2016 Papers

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

In this paper, we explore the problem of rendezvous of synchronous multi-robot systems. Each robot has its own unique, bounded yet controllable sensing range which can be adjusted. The state of those robots within the sensing range can be estimated, which induces the directed network topology of the multi-robot systems. In particular, we consider multi-robot systems containing faulty robots which can behave arbitrarily. Our recent work [1] has addressed the problem of achieving rendezvous in the presence of faulty robots under a restrictive class of conditions on network topology. In this work, we presented a theoretically correct, but computationally intractable algorithm. We extend our past work by proposing a new approximate algorithm that is computationally efficient, and by showing that the proposed algorithm solves our problem given faulty robots in general configurations under mild assumptions on the network topology. Thus, the main contribution of this paper is to provide an efficient computational framework and analysis of robust rendezvous algorithm in the presence of faulty robots. Several simulation results are provided to demonstrate that our algorithm performs well in the face of both stationary and dynamic faults.

This paper presents an analysis on flexible formations of nonholonomic mobile robots from the perspectives of motion planning and control. The formations are flexible in the sense that their formation configurations are defined based on curvilinear longitudinal and lateral relative separations instead of the commonly used rectilinear relative separations. And the mobile robots' motions considered here are kinodynamically constrained. In this work, we relate the formation maneuverability with the mobile robots' limited motion capabilities and the formation's configuration which in turn provides a measure on the formation's motion capabilities. In addition, useful findings pertaining to fundamental formations are also derived, which are advantageous for efficient and effective formation motion planning and control. These results lay a foundation to address the problems of planning kinodynamically feasible formation leader's trajectories to connect flexible formations between different locations such that every robot of the formation can follow its assigned formation point without violating it's kinodynamical motion constraints.

This paper presents a fast approximate multi-agent decision theoretic planning method extended from the well-known Markov Decision Process (MDP). Our objective is to plan motions for a team of homogeneous micro air vehicles (MAVs) toward a set of goals, such that each MAV at any state at any moment follows an action policy toward a unique goal, while considering action uncertainty. We pursue an efficient formulation by first considering a deterministic abstraction of the stochastic system based on approximate initial paths. These deterministic and decoupled sub-problems are converted to the stochastic domain and improved by individual agents or a subset of agents. The resulting decoupled formulation requires processing of a partial state space and enables online operation given applications with emerging tasks.

While designing robot hands based on grasping data is more common, fewer previous works have used details of human manipulation kinematics to improve robot hand design. The current work involves an underactuated, tendon driven, anthropomorphic manipulator with two flexor tendons and an abduction-adduction tendon, and describes its design based on experimental human precision manipulation data. Link lengths, joint axis alignment, and moment arms were derived from human subject data and values in the literature. The spring ratios, determining the torque relationships between joints, were then selected to maximize the achievable manipulation workspace from the human trial without requiring large forces, which are likely to lead to instability and object ejection. This is done by minimizing the stored spring energy in the robotic fingers across the range of precision manipulation workspace positions achieved by a representative human subject. After fabricating the hand, the energy characteristics of the resulting prototype are analyzed, and the robotic workspace is compared against the original human one. Despite only having three actuators, the hand is able to manipulate the test object within a 2.7 cm3 workspace volume, compared to an average human workspace of 5.4 cm3 for the same object. Future work could include adding antagonist actuators to achieve a larger motion range along the palmar-dorsal axis, which is currently the most limited axis of motion in comparison to the original human workspace.

This paper presents a highly sensitive dual mode tactile and proximity sensor for robotic applications that uses Carbon Microcoils (CMCs). The sensor consists of multiple electrode layers printed on a Flexible Printed Circuit Board (FPCB) and a dielectric substrate into which the CMCs are dispersed. The dielectric layer is simply put on the top of the FPCB. Thus, the sensor provides ease of fabrication and robustness against repetitive external contacts because the dielectric layer protects the electrodes. The electrical properties of the sensor are changed when an object approaches or touches the sensor. The sensor uses a capacitive sensing mode for tactile sensing and an inductive sensing mode for proximity sensing. CMCs amplify the change of the sensor signal because of electrical impedance formed by the CMCs, and thus, the sensitivity of the sensor increases. We fabricate the prptotype sensor with the dimensions of 30 × 30 × 0.6 mm3, and with 3 mm spatial-resolution. The sensor detects the applied pressure up to 330 kPa and the distance of an object as much as 150 mm away.

We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.