IROS 2015 Papers

Obtaining the optimal cost-to-go map for large scale rough terrains is computationally very expensive both in terms of duration and memory resources. A fast algorithm for approximation of the optimal cost-to-go map in terms of terrain traversability measures for path planning on known large scale rough terrains is developed. The results show that the majority of the cost-to-go map values, computed from every terrain location with respect to the goal location, are near-optimal. Unlike Dijkstra algorithm, the proposed algorithm has inherently parallel structure, and can be significantly speeded up depending on the number of used CPU cores.

This work addresses the task-space design problem of a linear-quadratic optimal tracking controller for robotic manipulators using the unit dual quaternion formalism. The efficiency, compactness, and lack of singularity of the representation render the unit dual quaternion a suitable framework for simultaneously describing the attitude and the position of the end-effector. Motivated by the advantages of this kinematic description, we propose a new task-space linear-quadratic optimal tracking controller in order to find an optimal trajectory for the end-effector, providing a tool to balance more conveniently the end-effector error and its task-space velocity. This is possible because the kinematic control problem using the dual quaternion transformation invariant error can be reduced to an affine time-varying system. The proposed optimal tracking controller allows the compensation of trajectory induced disturbances, as well as other modeled additive disturbances and known bias. Simulation results with different design parameters provide a performance overview, in comparison with standard kinematic controllers with and without a feed-forward term, for tracking a desired reference.

We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Péclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform.

In this paper, we propose a distributed control policy to achieve rendezvous by a set of robots even when some robots in the system do not follow the prescribed policy. These nonconforming robots correspond to faults in the multi-robot systems, and our control policy is thus a fault-tolerant policy. We consider the case in which each robot is an autonomous decision maker that is anonymous (i.e., robots are indistinguishable to one another), memoryless (i.e., each robot makes decisions based upon only its current information), and dimensionless (i.e., collision checking is not considered). Each robot has a limited sensing range, and is able to directly estimate the state of only those robots within that sensing range, which induces a network topology for the multi-robot systems. We assume that it is not possible for the fault-free robots to identify the faulty robots (e.g., due to the anonymous property of the robots). Our main result is a practical algorithm that achieves approximate rendezvous in the face of faulty robots under a few assumptions on the network topology. In simulation results, we show that our algorithm performs better in the face of faulty robots than other contemporary convergence algorithms, e.g., the circumcenter law, and local coordinate averaging.

In this paper, we present a novel approach to 2D street map-based localization for mobile robots that navigate mainly in urban sidewalk environments. Recently, localization based on the map built by Simultaneous Localization and Mapping (SLAM) has been widely used with great success. However, such methods limit robot navigation to environments whose maps are prebuilt. In other words, robots cannot navigate in environments that they have not previously visited. We aim to relax the restriction by employing existing 2D street maps for localization. Finding an exact match between sensor data and a street map is challenging because, unlike maps built by robots, street maps lack detailed information about the environment (such as height and color). Our approach to coping with this difficulty is to maximize statistical dependence between sensor data and the map, and localization is achieved through maximization of a Mutual Information-based criterion. Our method employs a computationally efficient estimator of Squared-loss Mutual Information through which we achieved near real-time performance. The effectiveness of our method is evaluated through localization experiments using real-world data sets.

Event exploration is the process of exploring a topologically known environment to gather information about dynamic events in this environment. Using multi-robot systems for event exploration brings major challenges such as finding and communicating relevant information. This paper presents a solution to these challenges in the form of a distributed decision-theoretic model called MAPING (Multi-Agent Planning for INformation Gathering), in which each agent computes a communication and an exploration strategy by assessing the relevance of an observation for another agent. The agents use an extended belief state that contains not only their own beliefs but also approximations of other agents' beliefs. MAPING includes a forgetting mechanism to ensure that the event-exploration remains open-ended. To overcome the resolution complexity due to the extended belief state we use a method based on the well-known adopted assumption of variables independence. We evaluate our approach on different event exploration problems with varying complexity. The experimental results on simulation show the effectiveness of MAPING, its ability to scale up and its ability to face real-word applications.

S shape dexterous continuum manipulators (DCMs) are preferred in minimally invasive surgeries for better steerability towards a target and obstacles avoidance capability. Current ways of realizing S shapes need to combine multi-bending segments which need to control the different segment separately. We propose a cross-helical tendons actuated DCM that can realize S shapes just using one segment. With two pairs of cross-helical tendons, it can realize omin-directional S shapes, where two types of planar S shapes are investigated in this paper. A prototype with 8mm diameter and 80mm length is built and the kinematics characteristics are investigated. The results show it can well realize the desired S shapes. The tip directional capability and obstacles avoidance capability are compared with C shape DCM.

This work proposes a conceptual model of personal space that can be used within symbolic knowledge-based robot architectures. The robot represents personal spaces in terms of affordance spaces attached to humans, i.e., portions of physical space that can be used for interactions. The model does not rely on numeric functions nor does it impose any restrictions upon the geometry of personal space. The approach is applied to human-aware robot activity placement planning: Given an activity type and a robot that is to perform an activity of this type, principles of personal space use determine the reasons pros and cons to place that particular activity at a particular affordance space. The symbolic representations of personal spaces enable robots to make choices and to communicate the reasons for their choices to humans.

Accurately localize a mobile vehicle with an easy and quickly deployable system can be very useful for many applications. Herein we present an EKF-SLAM algorithm which allows using radio frequency (RF) beacons without any prior knowledge of their location. As RF beacons provide only range information, recovering their positions is not an easy task. For this range-only SLAM case, a new procedure to instantiate the beacons in the filter is proposed. The method uses two range measurements from different robot's positions to initialize two hypotheses for the beacon's location, which are then integrated in the filter as a Gaussian mixture. This approach is evaluated and compared to other initialization techniques in simulation and with a real dataset. The results show that our approach performs as well as the other existing methods for both trajectory and map errors with a low computational cost.

We study the contact-aware state estimation (CASE) problem, i.e., the problem of estimating the state of an object while it is being actively manipulated by a robot. Several researchers have developed particle filters for this problem. They estimate the state (pose and velocity) of manipulated objects, some physical properties (such as mass and shape), and contact information (such as, gain or loss of contact and transitions between sliding and sticking). However, the effects of various contact and noise models, which can have a huge impact on the estimation results, are obfuscated by implementation details. In this paper, we study the CASE problem arising from a simple pushing task with the goal of shedding light on the fundamental contact modeling choices. Specifically, we evaluate four particle filters based upon four probabilistic state transition models generated from a deterministic multibody dynamics models with rigid or compliant contacts, each of which is augmented by one of two different noise models. Comparisons of these state transition models are carried out through the analysis of real and simulated experiments, the results of which, provide guidance to filter designers.

Computing time optimal motions along specified paths forms an integral part of the solution methodology for many motion planning problems. Conventionally, this optimal control problem is solved considering piece-wise constant parametrization for the control input which leads to convexity and sparsity in the optimization structure. However, it also results in discontinuous control trajectory which is difficult to track. Thus, in this paper we revisit this time optimal control problem with the primary motivation of ensuring a high degree of smoothness in the resulting motion profile. In particular, we solve it with continuity constraints in control and higher order motion derivatives like jerk, snap etc. It is clear that such constraints would necessitate the use of time varying control inputs over the commonly used piece-wise constant form. The primary contribution of the current work lies in the introduction of a C∞ class of time scaling functions represented as parametric exponentials. This in turn allows us to represent time varying control inputs as products of parametric exponential and a polynomial functions. We present the motivation behind adopting such representation of time scaling function over more common polynomial forms, both from mathematical as well as implementation standpoint. We also show that the proposed representation of time scaling function and control input leads to a very simple optimization structure where most of the constraints are linear. The non-linearity has a quasi-convex structure which can be reformulated into a simple difference of convex form. Thus, the resulting optimization can be efficiently solved through sequential convex programming where, at each iteration, the constraints in difference of convex form are further simplified to more conservative linear constraints.

This paper is devoted to a centralized mission planner for a group of marine robots. Mission is considered consisting of straight line segments (usually called tacks). The planner takes segments spatial configuration into account and solves a problem similar to the Multiple Travelling Salesman Problem (MTSP) in order to assign a sequence of segments to each vehicle. The contribution of this paper is in implementation details of the planner based on a specific mission mathematical model, its field test results and analysis for a group composed from an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle (AUV).

We propose a biomechatronics-based master/slave topology which is going to provide an Extended Physiological Proprioception (EPP)-equivalent control but without the use of a harness, cineplasty, or Bowden cable. The proposed control uses an implanted micro servo actuator. The original Bowden-cable EPP topology is compared to the proposed one and their simulation results are presented. The simulation results are encouraging since they indicate the materialization potential of the topology, both in terms of control and of low power, two essential factors in making the presence of an implant in the human body feasible. This control topology will provide a modern EPP-equivalent control scheme for upper-limb prostheses without the disadvantages of previous EPP configurations but with the control advantages of proprioceptive feedback.

A control concept is presented for hopping robots with hip and tail actuation. The flight phase is controlled by a novel nonlinear control concept that accounts for state and input contraints on the hip and tail while pursuing a linear error dynamics for the desired landing angle of the leg. An additional nonlinear model predictive control (MPC) scheme is superposed to coordinate the hopping cycles and to maximize the hopping speed. The MPC can be designed with a simple nonlinear optimization algorithm, as the constraints are already accounted for by the cascaded controller. Simulation results for a nonlinear dynamical model of the Festo BionicKangaroo show the working principle of the bi-level predictive control scheme.

Application domains of robotic systems are growing in complexity. It seems therefore plausible that robotic software will continue to be designed to be executed on distributed computer architectures interconnected through a network. It is a common practice today to rely on best-effort performance and assume that the latter are adequate given enough computational and networking resources. This approach however does not make best use of the available resources and, maybe more importantly, does not guarantee that performance remain constant over time. Real-time and Quality of Service become therefore important aspects in the software architecture of a robot. This article describes an approach for introducing these concepts in a publish-subscribe software middleware. The key contribution of our approach is that it leverages on the services provided by the operating system (scheduling priority and packet QoS) and abstracts them in a set of levels of priority that can be assigned dynamically, and with the granularity of individual communication channels. We implemented our approach on the YARP middleware and performed an experimental evaluation that demonstrates its benefit for increasing determinism and reducing latency in data communication. We further demonstrate this in a real-robot experiment that shows increased performance in a closed-loop scenario.

This paper presents a filtering algorithm for non-linear systems in the case of sensor degradation. The algorithm adapts the relative importance of the sensor measurements, compared to the model predictions, in real time; yielding a filter that is robust to noisy observations and sensor blackouts. The filter is constructed using a Variational Bayes Approximation of the conditional probability distribution of the system's state; i.e., the probability distribution of the state, given the measurements from the sensors. The algorithm is evaluated both in simulation and experimentally on a robotic platform. In the experiments, the sensor measurements from an Autonomous Underwater Vehicle (AUV) are altered artificially. The sensor output is either corrupted with outliers or manually stuck to a constant value; simulating in this fashion a sensor defect. In both cases, the filter reconstructs the robot's state accurately, thus enabling the vehicle to resume with mission execution.

Reliable mapping and hazard detection are prerequisites for autonomous navigation for unmanned ground vehicles. Because of the uncertainty and vibration induced by high-speed navigation and rugged terrain, the problem of mapping for high-speed off-road autonomous navigation has not been completely solved yet. A relative probabilistic mapping (RPM) algorithm is introduced to address the problem. Firstly, the relative probabilistic map is updated by Kalman filter and Gaussian Mixture algorithm based on the probabilistic exteroceptive measurements model. Then, terrain traversability is evaluated to identify obstacles in the map. Experiments on off-road high-speed autonomous vehicle, which suffers from severe vibration, with different sensor configurations are carried out to demonstrate the capability of the RPM algorithm.

Seagoing vessels have to undergo regular visual inspections in order to detect the typical defective situations affecting metallic structures, such as cracks and corrosion. These inspections are currently performed by ship surveyors manually at a great cost. To make ship inspections safer and more cost-efficient, this paper presents a Micro-Aerial Vehicle (MAV) intended for visual inspection and based on supervised autonomy. On the one hand, the vehicle is equipped with a vision system that effectively teleports the surveyor from the base station to the areas of the hull that need inspection. On the other hand, the MAV is the result of a complete redesign of a visual inspection-oriented aerial platform that we proposed some years ago, with the aim of introducing the surveyor in the control loop and, in this way, enlarge the range of inspection operations that can robustly be carried out. Another goal is to make the platform as usable as possible for a non-expert. All this has been accomplished by means of the definition of different autonomous functions, including obstacle detection and collision prevention, and extensive use of behavior-based high-level control. The results of some experiments conducted to assess both the performance and usability of the platform are discussed at the end of the paper.

This paper introduces a novel Balance Feedback Interface (BFI) that addresses the problem of bilateral feedback for teleoperation of humanoid robots. With this new device we expect to enhance robot's high force manipulation performance to a level comparable to humans by dynamically synchronizing master and slave. Through this Human-Machine Interface (HMI) we aim to achieve two goals: (i) have the human pilot learn from the robot's dynamic behavior; and (ii) have the humanoid robotic platform learn from human's motor skills. The eventual goal is to fuse the teleoperator's commands and an autonomous controller for optimal performance. Initial results evaluate the stability of the robot while being teleoperated by a human pilot for a simple upright balancing task. During the experiment the user has no visual information about the robot's state and is expected to compensate for unpredicted instabilities. The bilateral feedback system shows robustness to inertial variations and to external disturbances with the proposed human-in-the-loop control strategy offering valuable insight for future work.