ICRA 2017 Papers

Precision manipulation with underactuated hands is a challenging problem due to difficulties in obtaining precise gripper, object and contact models. Using vision feedback provides a degree of robustness to modeling inaccuracies, but conventional visual servoing schemes may suffer from performance degradation if inaccuracies are large and/or unmodeled phenomena (e.g. friction) have significant effect on the system. In this paper, we propose the use of Model Predictive Control (MPC) framework within a visual servoing scheme to achieve high performance precision manipulation even with very rough models of the manipulation process. With experiments using step and periodic reference signals (in total 204 experiments), we show that the utilization of MPC provides superior performance in terms of accuracy and efficiency comparing to the conventional visual servoing methods.

This paper presents a real-time visibility enhancement algorithm for effective underwater visual simultaneous localization and mapping (SLAM). Unlike an aerial environment, an underwater environment contains larger particles and is dominated by a different image degradation model. Our method starts with a thorough understanding of underwater particle physics (e.g., forward, back, multiple scattering, blur and noise). Targeting underwater image enhancement in a real-world application, we include an artificial light model in the derivation. The proposed method is effective for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method is validated by using simulated synthetic images (color) and real-world underwater images (color and grayscale). Using two underwater image sets acquired from the same area but with different water turbidity, we evaluate the proposed visibility enhancement and camera registration improvement in SLAM.

In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often requires too many experiments to be practical. In this paper, we propose a solution to this problem by exploiting prior knowledge from simulations, which are readily available for most robotic platforms. Specifically, we extend Entropy Search, a Bayesian optimization algorithm that maximizes information gain from each experiment, to the case of multiple information sources. The result is a principled way to automatically combine cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. We apply the resulting method to a cart-pole system, which confirms that the algorithm can find good control policies with fewer experiments than standard Bayesian optimization on the physical system only.

Nano-robotic systems based on Piezoelectric StickSlip (PSS) actuators have become increasingly popular in research and industry for semi-automated and automated tasks at small scales. For an efficient use of PSS actuators, a series of research have been fulfilled on design process, dynamic modeling, driving methods and position control. However, there have been very few investigations on velocity control of PSS actuators. Velocity control is important to enable the nano-robotic system to generate a smooth and efficient motion and to avoid the undesired inertial shock of the end effector. This paper deals with velocity characterization and control strategies for nano-robotic systems based on PSS actuators. The range of achievable velocities on PSS actuators is studied in air and vacuum environments. This analysis allows the definition of a detailed map of the velocity characteristics in forward and backward directions of motion. Velocity control strategies are then studied based on an instantaneous velocity feedback and an average velocity feedback. Results of the proposed method show the first experimental demonstration of velocity control for PSS actuators in medium and high speed configurations opening new perspectives on the use of nano-robotic systems in dynamic automated tasks.

Combining data from heterogeneous sensors allows to enhance tracking systems by increasing the field of view, incorporating redundancy, and improving the performance by exploiting complementary sensor characteristics. This paper proposes a new vehicle tracking approach for vehicle environment perception that fuses radar and laser data. A Random-Finite-Set-based tracking filter, which permits a clear mathematical formulation of the multi-object problem, is used as fusion center. In combination with extended object measurement models that work on the raw sensor data directly, the filter uses all available information without the need for further preprocessing routines, considers object interdependencies, and works in ambiguous situations. The results are evaluated using experimental data from a test vehicle.

Although inflatable soft robots are not yet a common robot platform, air leaking from the internal structure is a common and undesirable mode of failure for these platforms. In this paper we demonstrate a method to detect leaks in the structural chamber of an inflatable, pneumatically actuated robot. We then show that our method can adaptively lower commanded joint stiffness which slows the mass flow rate of the leak. This extends the operational life of the robot by decreasing long term error during operation by as much as 50% of the steady state error at the end effector when compared to the same leak if our adaptation method is not used. In future applications where we expect soft, inflatable robots to be useful, our methods can enable failure mitigation in resource-limited situations such as space exploration or disaster response.

Nowadays, the field of industrial robotics focuses particularly on collaborative robots that are able to work closely together with a human worker in an inherently safe way. To detect and prevent harmful collisions, a number of solutions both from the actuation and sensing sides have been suggested. However, due to the rigid body structures of the majority of systems, the risk of harmful collisions with human operators in a collaborative environment remains. In this paper, we propose a novel concept for a collaborative robot made of Variable Stiffness Links (VSLs). The idea is to use a combination of silicone based structures and fabric materials to create stiffness-controllable links that are pneumatically actuated. According to the application, it is possible to change the stiffness of the links by varying the value of pressure inside their structure. Moreover, the pressure readings from the pressure sensors inside the regulators can be utilised to detect collisions between the manipulator body and a human worker, for instance. A set of experiments are performed with the aim to assess the performance of the VSL when embedded in a robotic manipulator. The effects of different loads and pressures on the workspace of the manipulator are evaluated together with the efficiency of the collision detection control system and hardware.

In this paper, we present a vision-aided inertial navigation system (VINS) for localizing wheeled robots. In particular, we prove that VINS has additional unobservable directions, such as the scale, when deployed on a ground vehicle that is constrained to move along straight lines or circular arcs. To address this limitation, we extend VINS to incorporate low-frequency wheel-encoder data, and show that the scale becomes observable. Furthermore, and in order to improve the localization accuracy, we introduce the manifold-(m)VINS that exploits the fact that the vehicle moves on an approximately planar surface. In our experiments, we first show the performance degradation of VINS due to special motions, and then demonstrate that by utilizing the additional sources of information, our system achieves significantly higher positioning accuracy, while operating in real-time on a commercial-grade mobile device.

When full automation of mobile robots is not possible or desirable, teleoperation constitutes an alternative. The human operator can be supported with direction cues to facilitate localization or navigation. These cues are presented typically in the auditory, haptic and/or visual modality. An experiment was conducted to evaluate systematically and empirically the (uni-modal and multi-modal) effects of auditory and haptic feedback compared to visual feedback on target localization accuracy. Results show that haptic as well as auditory direction cues lead to significantly lower accuracy than visual cues. Moreover, combining feedback cues does not necessarily lead to better performance and can even reduce accuracy. Based on the results, possible implications for multi-modal human machine interface design are discussed.

Skin technology enabled a powerful way to sense the environment in robotic systems. It allows simplifying the formulation of safety tasks such as collision avoidance between the robot, the environment and surrounding objects. In this paper, a hierarchy policy based on tactile feedback is proposed to let a robot interact with its environment while performing a set of tasks. Such policy lets the safety tasks as collision avoidance and physical interaction, be reduced to simple potential field rules fed directly with tactile feedback which keeps computation demand low. In this context, the concept of “Intentional Contact” is introduced to escape from classic undesired equilibrium points produced by local minima in the potential fields. Allowed contact with the environment empowers a robot to modify its surroundings in order to fulfil the main task. Such contact is permitted as long as the generated force remains under a specific limit, otherwise, a reactive action is taken to reduce it. This new concept is validated in simulation and on a real robot.

Most airdropped cargo use a combination of one or more parachutes and an impact attenuation system to land safely. The latter adds cost, weight and complexity. However, by using their legs for impact attenuation, airdropped quadruped robots may avoid the need for such a system. In this paper, various leg configurations for attenuating impact of airborne landings were studied and tested. Using simple lumped element models for simulation and analysis, a quadruped robot with a three-segment leg was designed and built. This model was validated with experiments with a small scale 20 cm-tall test robot. During the experiments, the test robot experienced 7.7 × 10 m/s2 or 7.9 g-acceleration when dropped from height of 37.85 cm. This result is much better than the result of 1.4 × 102 m/s2 or 14.7g-acceleration when dropped at 10% of the original height with the same robot equipped with rigid legs. Such compliant leg design could be potentially used for impact attenuation of airdrop landings of robots five times larger.

Global 3D point cloud registration has been solved by finding putative matches between the point clouds for establishing alignment hypotheses. A naive approach would try to perform exhaustive search of triplets with a cubic runtime complexity in the number of data points. Super4PCS reduces this complexity to linear by making use of sets of 4 coplanar points. This paper proposes 2-Point-Normal Sets (2PNS), a new global 3D registration approach that advances Super4PCS by using 2 points and their normals for generating alignment hypotheses. The dramatic improvement in the complexity of 2PNS when compared to Super4PCS is demonstrated by the experiments that show speed-ups of two orders of magnitude in noise-free datasets and up to 5.2× in Kinect scans, while improving robustness and alignment accuracy, even in datasets with overlaps as low as 5%.

Existing camera networks in public spaces such as train terminals or malls can help social robots to navigate crowded scenes. However, the localization of the cameras is required, i.e., the positions and poses of all cameras in a unique reference. In this work, we estimate the relative location of any pair of cameras by solely using noisy trajectories observed from each camera. We propose a fully unsupervised learning technique using unlabelled pedestrians motion patterns captured in crowded scenes. We first estimate the pairwise camera parameters by optimally matching single-view pedestrian tracks using social awareness. Then, we show the impact of jointly estimating the network parameters. This is done by formulating a nonlinear least square optimization problem, leveraging a continuous approximation of the matching function. We evaluate our approach in real-world environments such as train terminals, where several hundreds of individuals need to be tracked across dozens of cameras every second.

In this work, we present an anytime planner for creating open-loop trajectories that solve rearrangement planning problems under uncertainty using nonprehensile manipulation. We first extend the Monte Carlo Tree Search algorithm to the unobservable domain. We then propose two default policies that allow us to quickly determine the potential to achieve the goal while accounting for the contact that is critical to rearrangement planning. The first policy uses a learned model generated from a set of user demonstrations. This model can be quickly queried for a sequence of actions that attempts to create contact with objects and achieve the goal. The second policy uses a heuristically guided planner in a subspace of the full state space. Using these goal informed policies, we are able to find initial solutions to the problem quickly, then continuously refine the solutions as time allows. We demonstrate our algorithm on a 7 degree-of-freedom manipulator moving objects on a table.

We present a novel approach to perform underwater simultaneous localization and mapping (SLAM) using a small inspection-class remotely operated vehicle (ROV) equipped with a single-beam scanning sonar, amidst high levels of noise present in the sonar data, and in the absence of inertial/odometry measurements. Features are extracted from hierarchically grouped clusters of sonar returns, data association is performed via the iterative joint compatibility test, and the vehicle's trajectory and map are estimated using incremental smoothing and mapping (iSAM). The resulting point clouds derived from the ROV's sonar are used to produce Gaussian process occupancy maps, which interpolate among gaps in the acoustic range data to produce descriptive 3D maps of submerged structures. The proposed localization and mapping approach is demonstrated using data gathered in two harbor environments in close proximity to piers and seawalls.

This paper presents a systematic approach for the 3-D mapping of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. In addition to the underwater vision constraints, cave mapping presents extra challenges in the form of lack of natural illumination and harsh contrasts, resulting in failure for most of the state-of-the-art visual based state estimation packages. A new approach employing a stereo camera and a video-light is presented. Our approach utilizes the intersection of the cone of the video-light with the cave boundaries: walls, floor, and ceiling, resulting in the construction of a wire frame outline of the cave. Successive frames are combined using a state of the art visual odometry algorithm while simultaneously inferring scale through the stereo reconstruction. Results from experiments at a cave, part of the Sistema Camilo, Quintana Roo, Mexico, validate our approach. The cave wall reconstruction presented provides an immersive experience in 3-D.

For heavy duty tasks which are needed in field or rough terrain, we developed a hydrostatically actuated anthropomorphic hand. The hand is specifically designed for the humanoid robot HYDRA, whose 40 joints are driven by back-drivable electro-hydrostatic actuators (EHA). Each designed hand has four fingers with a total of five DOF. Each finger has three joints underactuated by one tendon. Opposition/reposition of the thumb joint is also driven by one tendon. The five tendons are pulled by a miniature linear cluster EHA mounted in the forearm. The cluster EHA consists of a light weight tie-rod cylinder cluster with five pistons, and five low friction trochoid pumps with a crescent separator. Its 300 N nominal tension generates 1.5 Nm joint torque on each of the finger joints. Design of the cylinder and pump, with results of evaluation experiments is shown in this paper. Forearm structure with a mechanism to measure the tendon tension, low friction tendon routing in the forearm, low friction wire guiding link for the wrist, and parallel link wrist driving mechanism with two EHAs are also described.

This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds the branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization and mapping uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated. The proposed planner is capable of running online onboard a small aerial robot and its performance is evaluated using experimental studies in a challenging environment.

Advancements in the design of drones have led to their use in varied environments and applications such as battle field surveillance. In such scenarios, swarms of drones can coordinate to survey a given area. We consider the problem of covering an arbitrary area containing obstacles using multiple drones, i.e., the so-called coverage path planning (CPP) problem. The goal of the CPP problem is to find paths for each drone such that the entire area is covered. However, a major limitation in such deployments is drone flight time. To most efficiently use a swarm, we propose to minimize the maximum energy consumption among all drones' flight paths. We perform measurements to understand energy consumption of a drone. Using these measurements, we formulate an Energy Efficient Coverage Path Planning (EECPP) problem. We solve this problem in two steps: a load-balanced allocation of the given area to individual drones, and a minimum energy path planning (MEPP) problem for each drone. We conjecture that MEPP is NP-hard as it is similar to the Traveling Salesman Problem (TSP). We propose an adaptation of the well-known Lin-Kernighan heuristic for the TSP to efficiently solve the problem. We compare our solution to the recently proposed depth-limited search with back tracking algorithm, the optimal solution, and rastering as a baseline. Results show that our algorithm is more computationally efficient and provides more energy-efficient solutions compared to the other heuristics.