This paper proposes the combination of a fast motion planner, which generates potentially unfeasible trajectories for the center of mass of an underactuated UAV, with a tracking controller integrating a Control Barrier Function. In this way avoidance of both stationary and moving obstacles is guaranteed even when the UAV deviates from the collision-free trajectory issued by the planner, due to its unfeasibility. Simulations in a dynamic and cluttered environment validate the method.

Catheterization instruments are increasingly being improved to accurately diagnose and treat cardiovascular conditions. However, current catheter systems provide limited information about the shape of the catheter and tissue-instrument interaction forces during an intervention. Furthermore, relying on inconsistent feedback of such interaction forces during an intervention may result in tissue injury. This paper presents the first steps to estimate the interaction forces between a catheter and a mock-up arterial environment. We base the proposed method on a Pseudo-Rigid Body approximation of the catheter and integrate three-dimensional shape information provided by Fiber Bragg Grating sensors inside the catheter. The reconstructed forces along the catheter body can be fed back to the surgeon in visual and/or haptic form. In this work, the estimated forces are displayed in real-time in a graphical user interface with the reconstructed catheter shape. Experimental validation demonstrates a root mean square error of 0.03 N and a mean reconstruction error of 0.02 N.

We consider the problem of repeatable motion planning for redundant robotic systems performing cyclic tasks in the presence of obstacles. For this open problem, we present a control-based randomized planner, which produces closed collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. The proposed algorithm, which relies on bidirectional search and loop closure in the task-constrained configuration space, is shown to be probabilistically complete. A modified version of the planner is also devised for the case in which configuration-space paths are required to be smooth. Finally, we present planning results in various scenarios involving both free-flying and nonholonomic robots to show the effectiveness of the proposed method.

In this paper we propose a method to perform manual guidance with humanoid robots. Manual guidance is a general model of physical interaction: here we focus on guiding a humanoid by its hands. The proposed technique can be, however, used also for joint object transportation and other tasks implying human-humanoid physical interaction. Using a measure of the Instantaneous Capture Point, we develop an equilibrium-based interaction technique that does not require force/torque or vision sensors. It is, therefore, particularly suitable for low-cost humanoids and toys. The proposed method has been experimentally validated on the small humanoid NAO.

This paper considers the problem of planning the motion of a humanoid robot that must execute a manipulation task, possibly requiring stepping, in environments cluttered by obstacles. The proposed method explores the submanifold of the configuration space that is admissible with respect to the assigned task and at the same time satisfies other constraints, including humanoid equilibrium. The exploration tree is expanded using a hybrid scheme that simultaneously generates footsteps and whole-body motions. The algorithm has been implemented for the humanoid robot NAO and validated through planning experiments and dynamic playback in V-REP.

Minimally invasive surgery assisted by robots is characterized by the restriction of feasible motions of the manipulator link constrained to move through the entry port to the patient's body. In particular, the link is only allowed to translate along its axis and rotate about the entry point. This requires constraining the manipulator motion with respect to a point known as Remote Center of Motion (RCM). The achievement of any surgical task inside the patient's body must take into account this constraint. In this paper we provide a new, general characterization of the RCM constraint useful for task control in the minimally invasive robotic surgery context. To show the effectiveness of our formalization, we consider first a visual task for a manipulator with 6 degrees of freedom holding an endoscopic camera and derive the kinematic control law allowing to achieve the visual task while satisfying the RCM constraint. An example of application of the proposed kinematic modeling to a motion planning problem for a 9 degrees of freedom manipulator with assigned path for the surgical tool is then proposed to illustrate the generality of the approach.

We consider motion planning in the presence of obstacles for redundant robotic systems subject to repetitive task constraints. For this open problem, we present a novel control-based randomized planner which produces cyclic, collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. In particular, the proposed algorithm relies on bidirectional search and loop closure in the task-constrained configuration space. Planning experiments on a simple 3R planar robot and the KUKA LWR-IV 7-dof manipulator are reported to show the effectiveness of the proposed method.

We present a control-based approach for visual navigation of humanoid robots in office-like environments. In particular, the objective of the humanoid is to follow a maze of corridors, walking as close as possible to their center to maximize motion safety. Our control algorithm is inspired by a technique originally designed for unicycle robots and extended here to cope with the presence of turns and junctions. The feedback signals computed for the unicycle are transformed to inputs that are suited for the locomotion system of the humanoid, producing a natural, human-like behavior. Experimental results for the humanoid robot NAO are presented to show the validity of the approach, and in particular the successful extension of the controller to turns and junctions.

We consider the problem of planning the motion of redundant robotic systems subject to geometric task constraints in the presence of obstacles moving along known trajectories. Building on our previous results on task-constrained motion planning, we propose a control-based motion planner that works directly in the task-constrained configuration space extended with the time dimension. The generated trajectories are collision-free and satisfy the task constraint with arbitrary accuracy. Bounds on the achievable generalized velocities may also be taken into account. The proposed approach is validated through planning experiments on a 7-dof articulated robot and an 8-dof mobile manipulator.

We consider the problem of planning collision-free motions for general (i.e., possibly nonholonomic) redundant robots subject to task space constraints. Previous approaches to the solution are based on the idea of sampling and inverting the task constraint to build a roadmap of task-constrained configurations which are then connected by simple local paths; hence, task tracking is not enforced during the motion between samples. Here, we present a control-based randomized approach relying on a motion generation scheme that guarantees continued satisfaction of such constraint. The resulting planner allows to achieve accurate execution of the desired task without increasing the size of the roadmap. Numerical results on a fixed-base manipulator and a free-fying mobile manipulator are presented to illustrate the performance improvement obtained with the proposed technique.

In this paper, we characterize the time-optimal trajectories leading a Dubins car in collision with the obstacles in its workspace. Due to the constant velocity constraint characterizing the Dubins car model, these trajectories form a sufficient set of shortest paths between any robot configuration and the obstacles in the environment. Based on these paths, we define and give the algorithm for computing a distance function that takes into account the nonholonomic constraints and captures the nonsymmetric nature of the system. The developments presented here assume that the obstacles and the robot are polygons although the methodology can be applied to different shapes.

We present a cooperative exploration strategy for mobile robots. The method is based on the randomized incremental generation of a collection of data structures called sensor-based random trees, each representing a roadmap of an explored area with an associated safe region. Decentralized cooperation and coordination mechanisms are introduced so as to improve the exploration efficiency and to avoid conflicts. Simulations in various environments are presented to show the performance of the proposed technique.

This paper shows how to compute the nonholonomic distance between a polygonal car-like robot and polygonal obstacles. The solution extends previous work of Reeds and Shepp by finding the shortest path to a manifold (rather than to a point) in configuration space. Based on optimal control theory, the proposed approach yields an analytic solution to the problem

We present a framework for the stabilization of nonholonomic systems that do not possess special properties such as flatness or exact nilpotentizability. Our approach makes use of two tools: an iterative control scheme and a nilpotent approximation of the system dynamics. The latter is used to compute an approximate steering control which, repeatedly applied to the system, guarantees asymptotic stability with exponential convergence to any desired set point, under appropriate conditions. For illustration, we apply the proposed strategy to design a stabilizing controller for the plate-ball manipulation system, a canonical example of nonflat nonholonomic mechanism. The theoretical performance and robustness of the controller are confirmed by simulations, both in the nominal case and in the presence of a perturbation on the ball radius.

We present a method for sensor-based exploration of unknown environments by a mobile robot. The method is based on the randomized incremental generation of a data structure called sensor-based random tree (SRT), which represents a roadmap of the explored area with an associated safe region. Different exploration strategies may be obtained by instantiating the general method with different perception techniques. Two such techniques are discussed: the first, conservative and particularly suited to noisy sensors, results in an exploration strategy called SRT-Ball. The second perception technique is more confident, and the corresponding strategy is called SRT-Star. The two strategies are critically compared by simulations as well as by experiments on the MagellanPro robot.

Robotic manipulation by rolling contacts is an appealing method for achieving dexterity with relatively simple hardware. While there exist techniques for planning motions of rigid bodies in rolling contact under nominal conditions, an inescapable challenge is the design of robust controllers of provable performance in the presence of model perturbations. As a preliminary step in this direction, we present in this paper an iterative robust planner of arbitrary accuracy for the plate-ball manipulation system subject to perturbations on the sphere radius. The basic tool is an exact geometric planner for the nominal system, whose repeated application guarantees the desired robustness property on the basis of the iterative steering paradigm. Simulation results under perturbed conditions show the effectiveness of the method.

We consider the problem of planning collision-free motions for a redundant robot whose end-effector must travel along a given path. Although collision avoidance is one of the main reasons for introducing kinematic redundancy in manipulators, the planning methods so far proposed for this particular problem are neither efficient nor complete. In this paper, we introduce some algorithms that may be considered as an extension of probabilistic planning techniques to the problem at hand. All the algorithms are based on the same simple mechanism for generating random samples of the configuration space that are compatible with the end-effector path constraint. Experimental results illustrate the performance of the planners.

We consider the plate-ball system as a typical example of manipulation by rolling contacts. While there exist techniques for planning motions of this nonholonomic mechanism in nominal conditions, our objective in this paper is the robust execution of maneuvers in the presence of model perturbations. To this end, we adopt an iterative steering paradigm based on the use of a nilpotent approximation of the system. Simulation results are reported to confirm the robustness achieved with the proposed feedback controller.

Existing methods for nonholonomic feedback stabilization can only be applied to exactly nilpotentizable or flat systems. In this paper, a car towing two off-hooked trailers is considered as a canonical example of robot that does not fall into the above class. We show that exponential convergence to arbitrary configurations can be obtained by means of an iterative steering technique based on a nonhomogeneous nilpotent approximation of the system. Simulation results illustrate the performance of the method.

Existing methods for nonholonomic motion planning can only be applied to exactly nilpotentizable or flat systems. For nonholonomic systems that do not fall into the above classes, we conjecture that globally defined nilpotent approximations will allow the synthesis of efficient steering and stabilization strategies. In the paper, a car towing two off-hooked trailers is considered as a case study. First, it is shown how to derive a nilpotent approximation that is valid in the neighborhood of both regular and singular points. Then, such approximate model is used to compute steering controls. Simulation results are reported to show the satisfactory performance of the method.

This paper shows how to compute the nonholonomic distance between a pointwise car-like robot and polygonal obstacles. Geometric constructions to compute the shortest paths from a configuration (given orientation and position in the plane of the robot) to a position (i.e., a configuration with unspecified final orientation) are first presented. The geometric structure of the reachable set (set of points in the plane reachable by paths of given length /spl Lscr/) is then used to compute the shortest paths to straight-line segments. Obstacle distance is defined as the length of such shortest paths. The algorithms are developed for robots that can move both forward and backward (Reeds and Shepp's car) or only forward (Dubins' car). They are based on the convexity analysis of the reachable set.

This paper deals with distance computation between a forward moving car-like robot and polygonal obstacles. We propose efficient geometric algorithms to compute the shortest paths to obstacles. We then derive the "visibility" domain in the presence of obstacles, i.e. the set of positions reachable from a starting configuration, by a collision-free shortest path unaffected by the presence of the obstacles.

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.

The problem of sensor-based robot motion planning in unknown environments is addressed. The proposed solution approach prescribes the repeated sequence of two fundamental processes: perception and navigation. In the former, the robot collects data from its sensors, builds local maps and integrates them with the global maps so far reconstructed, using fuzzy logic operators. During the navigation process, a planner based on the A* algorithm proposes a path from the current position to the goal. The robot moves along this path until one of two termination conditions is verified namely (i) an unexpected obstructing obstacle is detected, or (ii) the robot is leaving the area in which reliable information has been gathered. Experimental results are presented for a Nomad 200 mobile robot.