IROS 2015 Papers

As microscale robots are becoming increasingly popular due to their potential for medical and industrial applications, various designs of microscale robotic system have been developed. However, there has not been much work on autonomous control algorithms for microscale robots in microfluidic environments. In this paper, we introduce an autonomous navigation algorithm for the bacteria-powered microrobots (BPMs) in a workspace with moving obstacles. A BPM consists of a rigid inorganic body with bacteria attached on the surface. The attached bacteria provide propulsive force and are controllable using electric fields, which had been demonstrated in previous work. We take the controllability of BPMs and the unpredictable motion of dynamic obstacles into account to develop a dynamic obstacle avoidance approach. Moreover, we use finite element simulation to observe an electric field around a moving obstacle to model the field's deformation. Demonstration of dynamic obstacle avoidance approach through simulation results and experimental data are presented in the paper.

As a result of high precision and excellent dynamic properties parallel kinematic machines (PKM) are particularly suited for high-speed and high-accuracy object handling. To optimize the design of such PKM, only kinetostatic analysis is not sufficient. Additionally, dynamic properties should be taken into account. Hence, this research develops the dynamic model for a class of translational 3-RPC-manipulators with either pure prismatic joint or pure revolute joint actuation. For a given task the dynamic performance is compared to results of the kinetostatic analysis. Hence, it is shown that the dynamic properties highly influence the performance of the RPC-structure.

In this article, we build dynamic models of 2D compliant links to evaluate injury level in a human-robot interaction. Safety is a premium concern for co-robotic systems. It has been studied that using compliant links in a robot can greatly reduce the injury level. Since most safety criteria are based on tolerance of acceleration of the operator's head during the impact, an efficient and yet accurate dynamic model of compliant links is needed. In this paper, we compare three dynamic models for calculating acceleration and head injury criterion: the compliant Beam-Spring-Mass (BSM) model, the Mass-Spring-Mass (MSM) model and the Link-Spring-Mass (LSM) model. For the MSM model and LSM model, we obtain analytic expressions of acceleration. While numerical results are achieved for the compliant BSM model. To develop the compliant BSM model, we compared three different methods: the Pseudo-Rigid-Body (PRB) model, the Finite-Segment-Model (FSM), and the Assumed-Mode-Method (AMM). Finally, all these models are validated by human-robot impact simulation programs built in Matlab. The acceleration from these simulations can be used to quantitatively measure the injury level during an impact.

Bio-inspired undulatory fin propulsion holds considerable potential for endowing robotic underwater vehicles with low-speed manoeuvrability and stable station-keeping. Robotic fins typically comprise a number of serially arranged and individually actuated “fin rays”, interconnected by a membrane-like flexible surface. Propulsive forces are generated by the propagation of a traveling wave along the mechanism, via appropriately timed ray oscillations. The present paper describes a dynamic model for an elementary two-ray fin system, analyzed as a standard robot mechanism with additional contributions arising from the elastic deformation of the flexible membrane and from the hydrodynamic forces. The model's main aspects, particularly with regard to the hydrodynamic effects, are explored via simulation studies, as well as via experiments with a robotic prototype. The developed model can serve a number of purposes towards optimizing the mechanical design, the control strategies, and the propulsive efficacy of robotic undulatory fins.

In cooperative manipulation tasks, load allocation is a crucial step in order to solve the intrinsic redundancy of the system. The desired wrench needs to be suitably distributed between the end-effectors to implement the desired motion of the manipulated object. In this framework, both the grasp kinematics and the individual capacities of each manipulator provide relevant constraints. On one hand, the end effector wrenches act on the object via the grasp geometry. On the other hand, the individual admissible payload further depends on the current configuration of the robots. In this paper we focus on a heterogeneous cooperative manipulation setting and we design a proper allocation strategy to distribute the desired object wrench, considering both constant and time-varying constraints for the load distribution. The relevance of our findings is illustrated by means of an experimental study involving two anthropomorphic robots manipulating a common object.

This paper presents a framework for planning the motion of an n-fingered robot hand to create an inertial load on a grasped object to achieve a desired in-grasp sliding motion. The model of the sliding dynamics is based on a soft-finger limit surface contact model at each fingertip. The framework is applied to the problem of regrasping a block held in a pinch grasp. The approach is applied to two examples in simulation, one of which is tested experimentally.

Applying humanoid robots in disaster sites requires strong locomotion abilities due to the cluttered and unstructured environment. Besides bipedal walking, it is beneficial to provide functionality to locomote on four limbs, and to control the transitions between bipedal and quadrupedal locomotion. In this paper, we propose a planning algorithm for bipedal and quadrupedal locomotion with intermediate transitions. The algorithm is based on the divergent component of motion for a linear time-variant inverted pendulum model with variable height and a flywheel. The model was validated by model comparisons in a simulation experiment. In addition, the algorithm was validated on a real robot, which realized a sequence of bipedal and quadrupedal locomotion without intermediate stops.

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41 % for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[16], as well as on standard bundle adjustment algorithms with motion segmentation.

Dividing the control authority of the slave robot among operators, based on the available information to them and their controlling strategy, is absolutely essential for cooperative teleoperation systems. Although there have been many studies on this, most of the analysis were limited to fixed authority and its effect on stability. None of the research has discussed how to allocate the control authority in real-time. This paper presents a dynamic authority distribution method for dual master single slave Cooperative Teleoperation systems, which allocates the authority over slave to operators on the move in each DOF separately. This method observes the energy interaction in the kinesthetic coupling link between the operators, classifies the interaction into operators' leader/follower behavior and adapts the authorities accordingly. The proposed method is experimentally tested with unique set of camera views of task environment with 3DOF dual master single slave teleoperation system. Experimental results demonstrate accurate convergence of decomposition of authorities based on camera-view. The performance of proposed dynamic authority architecture is evaluated by comparing total energy generation with fixed authority setup, and is found to be more efficient in terms of less energy generation, and hence less effort is required by operators to execute the task.

In this paper we derive and implement an algorithm for an indoor mobile robotics platform to estimate the manipulability of initially unknown obstacles while navigating through its environment to a pre-specified goal. The environment is represented by an evidence grid, where each cell contains a gamma-distributed cost as well as visual feature data in the form of a color histogram. While navigating, the robot associates visual features of objects occupying a given cell with manipulability cost estimates of that cell, learning whether an object or obstacle can be moved or not in the robot's attempt to reach the goal. We derive and utilize a lower confidence bound (LCB) estimate for the cost of each cell in order to incorporate an exploration (versus pure exploitation) element to the robot's search for the lowest-cost path. Combining the LCB cost estimates with the dynamic replanning search algorithm D*-Lite, we can quickly compute optimal navigation paths regardless of the numerous changes occurring in the robot's environmental belief state. We explain the probabilistic representation of cost in the evidence grid and provide simulation and real-world results for our algorithm in a navigation scenario with static and movable objects.

Communication is an important component in multi-robots systems; system performance may get affected when the number of robots or communication channels increases. The Dynamic Bandwidth Management Library (DBML) was designed to provide a way of maximizing bandwidth usage in multi-robots systems. The developed system prioritizes communication channels according to environment events and offers greater bandwidth for the most important channels. The library was developed in ROS (Robot Operating System) in order to separate the functionalities into independent modules to be reused and improved in future works. This paper presents the library design as a linear optimization problem and an example of the library usage in a teleoperation application. We also show the main settings and results for the system under discussion.

In this paper, a dual-mode twisting actuation mechanism with an active clutch is newly presented for a high performance tendon-driven robot (e.g., robot hand). This mechanism is a kind of mechanical automatic power transmission mechanism which provides fast motion and large contraction force by two geared motors. One of the motors is adopted for main actuation and the other is utilized for active clutch. The active clutch consisting of low-power DC motor and gear set allows easy control of the dual-mode twisting actuation and simplifies relaxation process of twisted strings which was a critical problem by a passive brake in previous research [17]. Kinematics of the proposed mechanism is represented and its simulation is performed to verify the performance numerically. By using BLDC & DC motor with 8 W & 0.3 W power, we developed a prototype of the dual-mode twisting actuation with the weight of 45.7 g and the size of 71 mm × 21.5 × 15 mm. Despite of simple structure, the proposed mechanism shows that operation mode-change can be easily managed and the relaxation time was much more reduced than that of the passive brake version.

We have developed a robot with a new control mechanism in order to collect information on flying robots in multiple fields. We aimed for a function that could rotate the tilt angle continuously and without limit and a function for flying maintaining any desired tilt angle with a structure that could efficiently use the thrust generated by the propellers. We devised a mechanism that connected two bicopter modules, each of which combines two of the four propellers into one set and named this mechanism the “Bi2 Copter”. This mechanism provided movements including landing, take-off, and flying with any desired tilt angle. This ability of this mechanism to fly walls with continuously changing surface angles and full 360° spherical coverage makes possible applications in investigation, measurement, etc. This report covers the design concepts of this flying robot, the structure design, basic control and operations verification.

Formations of camera-equipped quadrotors (flying cameras) have the actuation agility to track moving targets from multiple viewing angles. In this paper we propose an infrastructure-free distributed control method for multiple flying cameras tracking a moving object. The proposed vision-based servoing can deal with noisy and missing target observations, accounts for quadrotor oscillations and does not require an external positioning system. The flight direction of each camera is inferred via geometric derivation, and the formation is maintained by employing a distributed algorithm that uses the target position information on the camera plane and the position of neighboring flying cameras. Simulations show that the proposed solution enables the tracking of a moving target by the cameras flying in formation despite noisy target detections and when the target is outside some of the fields of view.

Teams of fixed wing micro-aerial vehicles (MAVs) could provide a wide area coverage and relay data in wireless ad-hoc networks. In such applications fixed wing MAVs have to be able to regulate an inter-robot distance. Fixed wing MAVs have reduced maneuverability, that is, they cannot perform sharp turns or hover on the spot. This kinematic property presents the main challenge to design a formation algorithm that will regulate inter-MAV distance and cover the desired area. In this paper we present a distributed control strategy that is based on attraction and repulsion between MAVs and relies only on local information. We show in simulation and in field experiments with a team of fixed wing MAVs that using our strategy MAVs can cover an area by creating an equilateral triangular lattice and regulate communication link quality between neighboring MAVs.

We present a number of powerful local mechanisms for maintaining a dynamic swarm of robots with limited capabilities and information, in the presence of external forces and permanent node failures. We propose a set of local continuous algorithms that together produce a generalization of a Euclidean Steiner tree. At any stage, the resulting overall shape achieves a good compromise between local thickness, global connectivity, and flexibility to further continuous motion of the terminals. The resulting swarm behavior scales well, is robust against node failures, and performs close to the best known approximation bound for a corresponding centralized static optimization problem.

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments.

The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.

This paper presents an efficient approach to recognize objects captured with an RGB-D sensor. The proposed approach uses a Bag-of-Words (BOW) model to learn feature representations from raw RGB-D point clouds in a weakly supervised manner. To this end, we introduce a novel method based on randomized clustering trees to learn visual vocabularies which are fast to compute and more discriminative compared to the vocabularies generated by classical methods such as k-means. We show that, when combined with standard spatial pooling strategies, our proposed approach yields a powerful feature representation for RGB-D object recognition. Our extensive experimental evaluation on two challenging RGB-D object datasets and live video streams from Kinect shows that our learned features result in superior object recognition accuracies compared with the state-of-the-art methods.