ICRA 2015 Papers

One of the key challenges in dynamic manipulation is to establish continuous contact between a moving object and a manipulator. Direct robotic catching is a benchmark in this context, especially without grasping devices. In this paper, we explain why it is unrewarding to aim for an ideal initial dynamic contact if only imprecise knowledge of the object states is available. Robust initial contacts are proposed such that a successful quasi-direct catch can be predicted. The proposed robustness originates in a negative relative acceleration between object and catching end-effector. Sets, for which an upper negative relative acceleration bound holds, are evaluated throughout an exemplary catching motion. Reachable set computations allow to express and visualize these sets of successful object states at any point in time before contact. The paper closes with a robot-robot experiment that shows successful quasi-direct catches without feedback on the object states for a robotic throw.

We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that asks whether there is a controller (policy) to ensure that the objective holds with probability 1 (almost-surely). While the qualitative analysis of POMDPs with parity objectives is undecidable, recent results show that when restricted to finite-memory policies the problem is EXPTIME-complete. While the problem is intractable in theory, we present a practical approach to solve the qualitative analysis problem. We designed several heuristics to deal with the exponential complexity, and have used our implementation on a number of well-known POMDP examples for robotics applications. Our results provide the first practical approach to solve the qualitative analysis of robot motion planning with LTL properties in the presence of uncertainty.

In this work we study the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs an forward looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface. The platform may also be tilted with respect to the mobile robot. The overall goal is to design the aerial robot's control inputs such that it initially approaches the platform, while maintaining it within the camera's field of view and finally lands on it, in a way that minimizes the errors in position, attitude and velocity, while avoiding collision. Owing to the inclined ground and landing surface, the desired final state of the aerial robot is not an equilibrium state, which complicates significantly the control design. In that respect, a discrete-time non-linear model predictive controller was developed that optimizes both the trajectories and the time horizon, towards achieving the aforementioned objectives while respecting the input constraints as well. Finally, an extensive experimental study, with a Pioneer mobile robot and a Parrot ARDrone quadrotor, clarifies and verifies the theoretical findings.

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile ARM processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a local depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV.

In a cable-driven parallel robot, elastic cables are used to manipulate the end effector within the workspace. Cable force measurement is necessary for several control algorithms like cable force control, contact control, or load identification. The cable force sensor can be placed directly at the connection point on the platform or somewhere along the cable using pulleys. The pulleys between the force sensor and the platform disturb the force measurement accuracy due to friction. This paper deals with modeling and compensation of the friction. The friction behavior in the drive train with focus on the effects of the pulleys is non-trivial, as the cable movement consists of microscopic and macroscopic movements and standstills. Friction models from Coulomb and Dahl are adapted to deal with the pulley friction. The experimental evaluation showed an improvement of 70% with respect to the uncompensated case.

This paper presents a kinematic study of a pseudo-rigid-body model (PRBM) of MRI-compatible, magnetically actuated, steerable catheters. It includes a derivation of a mathematical model of the PRBM of the catheter, singularity studies of the model, and a new manipulability measure. While the forward kinematics of the model presented here is applicable to PRBMs for other applications, actuation method is unique to the particular design. Hence, a careful study of singularities and manipulability of the model is required. The singularities are studied from the underlying equations of motion with intuitive interpretations. The proposed manipulability measure is a generalization of the inverse condition number manipulability measure of robotic manipulators. While the PRBM is an approximation of the flexible catheter, kinematic studies of the PRBM still provide some insight into feasibility and limitations of the catheter, which is beneficial to the design and motion planning of the catheter.

We have developed a new type of robot which accompanies the healthcare professionals making medical rounds in patient's bedrooms at hospitals. This novel robot mainly executes two tasks: the carrying armamentarium of round medical supplies and the recording electronic health data on rounds. An omni-directional mobile mechanism and a human tracking control system to follow a specified medical professionals realize smooth transfer movement from the nurses' station to patient's bedroom. Electronic health data on rounds is automatically recorded by using a CCD camera and a voice recorder. When the robot is connected via a cable to a medical data server in nurses' station, the robot transmits the patients' data to the server and receives new patients' information.

We present a super wide-measurement range load sensor with high-durability for multi-biosignal sensing in daily life. We employed quartz crystal resonator (QCR) load sensor with super wide range (from 6 × 10-5 to 30 [N]) for sensing of biosignals ranging from mN order (e.g. heartbeat) to 100 N order (e.g. body motion). Furthermore, we implemented an outer case which makes the sensor durable against unexpected loads in practical use. The outer case increased the durability of the sensor 88 times higher than the sensor without outer case. Finally we succeeded in simultaneous sensing of heartbeat (≈ 100 [mN]) and body motion (≈ 25 [N]) with identical sensor.

Capsule endoscopy is becoming an important tool for diagnosis in the digestive system. In certain parts of the gastrointestinal tract, such as the stomach, the capsule should be self-propelled. This study examines the swimming mechanism for a miniature gastric pill endoscope. Our method is based on a constant external magnetic field, such as the B0 field of an MRI (Magnetic Resonance Imaging), that drives a pair of coils in order to create an undulating swimming motion. We present a dynamic model of the two coil swimmer and compare it to the results obtained using PIV (Particle Image Velocimetry). We demonstrate that our swimmer is able to create an average propulsive force of 0.03 mN and 0.07 mN for the flow regimes tested, which is in a reasonable agreement with the results of the proposed dynamic model.

In this paper, a Propositional Temporal Logic (PTL) inference engine is implemented in the cognitive level of a Concurrent Autonomous Agent (CAA). The hardware architecture of the system is an embedded microcontrollers network designed to fit the concurrency requirements of the cognitive model of the aforementioned agent. The architecture of the agent comprises three levels, namely, the reactive level, instinctive level and the cognitive level, which runs concurrently. The reactive level is embedded in a PSoC 5LP, consisting of behaviours created over a embedded kinematic controller. The instinctive level runs in an ARM mbed, which receives perceptions from and sends the active behaviour to the reactive level through a CAN bus. This level uses a Knowledge Based System (KBS) with First Order Logic (FOL) as automatic reasoning mechanism. The cognitive level runs an implementation of the METATEM algorithm on a DNP/2486 which, in turn, receives symbolic information from the instinctive level to update its logical world model. It also sends new local goals to instinctive level through an Ethernet network. Finally, the architecture used in the experiments is presented, followed by the results.

As autonomous vehicles operating on the urban roads, being conscious of the road context is a crucial prerequisite to safely negotiate with the other vehicles. This paper proposes a probabilistic approach to infer the road context from the vehicle behaviors. Specifically, the consistencies of the randomly-observed vehicle states are extracted first, thereafter the road context is inferred in a probabilistic manner by coupling these consistencies. The feasibility of the proposed road context inference approach has been validated by the case study of an urban road that includes roundabout and T-junction. The experiments demonstrate that the inferred road context can be successfully applied for the autonomous vehicles in various aspects.

This paper deals with the generation of motion for complex dynamical systems (such as humanoid robots) to achieve several concurrent objectives. Hierarchy of tasks and optimal control are two frameworks commonly used to this aim. The first one specifies control objectives as a number of quadratic functions to be minimized under strict priorities. The second one minimizes an arbitrary user-defined function of the future state of the system, thus considering its evolution in time. Our recent work on prioritized optimal control merges the advantages of both these methods. This paper reformulates the original prioritized optimal control algorithm with the precise goal of improving its computational speed. We extend the dynamic programming method to work with a hierarchy of tasks. We compared our approach in simulation with both our previous algorithm and classical optimal control. The measured computational improvement represents another step towards the application of prioritized optimal control for online model predictive control of humanoid robots. We believe that this could be the key to unlock the (so far unexploited) dynamic capabilities of these mechanical systems.

We introduce the problem of navigating a group of robots having prioritized formations amidst static and dynamic obstacles. Our formulation allows users to define a number of template formations, each with a specified priority value. At each planning cycle, we compute a new formation which accounts for both these priority values and the safe progress of the robots towards their goal. To this end, we introduce a new velocity-based navigation approach which we denote as Formation Velocity Obstacles (FVO). Like other velocity-based approaches, FVO allows anticipatory collision avoidance accounting for the likely future motion of nearby obstacles. However, we extend these previous approaches and allow anisotropic agents which rotate themselves to orient along their direction of travel. We integrate these FVOs with a Bayesian framework to infer priority values for arbitrary formations from the user-given templates. The result is a complete framework for prioritized formation planning.

We are proposing a general framework that incorporates multiple task definitions in the prioritized inverse kinematics problem. First, a mathematical description of multiple task definitions is constructed that provides an efficient way to show unprioritized or prioritized accumulations of tasks. Then, smooth transitions between all task definitions are studied, so a method, called task transition control, is developed that interpolates joint trajectories using barycentric coordinates and linear dynamical systems to overcome difficulties of interpolating task trajectories in the conventional methods. Consequently, smooth, arbitrary, and consecutive task transitions are achieved in a simple, direct, and general manner and also boundedness of joint trajectories is assured regardless of singularity. Lastly, the idea is tested by two kinematic simulations: obstacle avoidance with the KUKA LWR and task scheduling of a humanoid robot.

Aiming to apply soft actuators in driving a walking robot, the design, fabrication and locomotion of a bio-inspired printable hexapod robot are studied. The robot mimics the insect's design and walking posture by driving six legs with alternating tripod gait which provides its locomotive adaptability on flat terrains. The versatile movements of the robot's leg are achieved by using soft and multiple degree-of-freedom actuators. The actuators are made by dielectric elastomers with a simple mechanism based on antagonistic configuration. By using 3D printing method, the actuator can be embedded into the frame of the robot and a control system is developed. Finally, the robot's locomotion is successfully demonstrated with variable speeds and stride lengths.

Physical stochastic disturbances, such as wind, often affect the motion of robots that perform complex tasks in real-world conditions. These disturbances pose a control challenge because resulting drift induces uncertainty and changes in the robot's speed and direction. This paper presents an online control policy based on supervised machine learning, Least Squares Axial Sum Policy Approximation (LSAPA), that generates trajectories for robotic preference-balancing tasks under stochastic disturbances. The task is learned offline with reinforcement learning, assuming no disturbances, and then trajectories are planned online in the presence of disturbances using the current observed information. We model the robot as a stochastic control-affine system with unknown dynamics impacted by a Gaussian process, and the task as a continuous Markov Decision Process. Replacing a traditional greedy policy, LSAPA works for high-dimensional control-affine systems impacted by stochastic disturbances and is linear in the input dimensionality. We verify the method for Swing-free Aerial Cargo Delivery and Rendezvous tasks. Results show that LSAPA selects an input an order of magnitude faster than comparative methods, rejecting a range of stochastic disturbances. Further, experiments on a quadrotor demonstrate that LSAPA trajectories that are suitable for physical systems.

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot's belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.

To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function which “explains” the motion of the human. To do this, we gather example trajectories from two participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict a human's motion for a given task by iteratively replanning a trajectory for a 23 DoF human kinematic model using the STOMP algorithm with the learned cost function in the presence of a moving collaborator. Our results suggest that our method outperforms baseline methods and generalizes well for tasks similar to those that were demonstrated.