The use of drones in human-populated areas is increasing day by day. Such robots flying in close proximity to humans and potentially interacting with them, as in object handover or delivery, need to carefully plan their navigation considering the presence of humans. We propose a humanaware 3D reactive planner based on stochastic optimization for drone navigation. Besides considering the kinematics constraints of the drone, we propose two criteria to produce socially acceptable trajectories. The first, called discomfort, considers the unease caused to the humans spatially close to fast-moving drones. The second, called visibility, promotes the drone's visibility for humans. We demonstrate the planner's performance and adaptability in various simulated experiments.

In this article, we consider the problem of delivering an object to a human coworker by means of an aerial robot (AR). To this aim, we present an ergonomics-aware Nonlinear Model Predictive Control (NMPC) designed to autonomously perform the handover. The method is general enough to be applied to any multi-rotor aerial vehicle (MRAV) with a minimal adaptation of the robot model. The formulation of the optimal control problem steers the AR toward a handover location by optimizing the human coworker ergonomics, which includes the predicted arm joint torques of the human. The motion task is expressed in a frame relative to the human, whose motion model is included in the equations of the NMPC. This allows the controller to promptly adapt to the human movements by predicting her future poses over the horizon. The control framework also accounts for the problem of maintaining visibility on the human coworker, while respecting both the actuation and state limits of the robot. Additionally, a safety barrier is embedded in the controller to avoid any risk of collision with the human partner. Realistic simulations are performed to validate the feasibility of the approach and the source code of the implementation is released open-source.

As humans and robots work more and more closely, robots must quickly react to unforeseen human behavior. Online Trajectory Generation (OTG), based on simple trajectory models like series of polynomial cubic functions, has demonstrated its efficiency to plan and control reactive motions of robots. However to ensure the safety and comfort of humans, fast trajectory adaptation algorithms are necessary to bring back the robot inside an acceptable domain that is defined by a set of kinematic constraints. The algorithm presented herein extends a time-optimal OTG to cope with non admissible robot's state. This feature enables time-variant kinematic constraints. With the possibility to specify velocity and acceleration at both ends under short computation times, it makes the robot able to react quickly to unforeseen events. Short computation times can lead to more refined architectures where sensors can be integrated to a low control level and make the system more reactive.

International audience

International audience

This paper aims to smooth jerky trajectories for high-DOF manipulators with Soft Motion [1] shortcuts which are bounded in velocity, acceleration and jerk. The algorithm presented here iteratively picks two points on the trajectory and attempts to replace the intermediate trajectory with a shorter and collision-free segment. The objective of this algorithm is to shorten the execution time of an input trajectory as much as possible while retaining the feasibility. Simulation and real-world experimental results on reaching tasks in human environments show that this technique can generate smooth and collision-free motions for a KUKA Light-Weight Robot.

International audience

This paper presents a new method to compute enveloping grasps with a multi-fingered robotic hand. The method is guided by the idea that a good grasp should maximize the contact surface between the held object and the hand's palmar surface. Starting from a given hand pregrasp configuration, the proposed method finds the hand poses that maximize this surface similarity. We use a surface descriptor that is based on a geodesic measure and on a continuous representation of the surfaces, unlike previous shape matching methods that rely on the Euclidean distance and/or discrete representation (e.g. random point set). Using geodesic contours to describe local surfaces enables us to detect details such as a handle or a thin part. Once the surface matching returns a set of hand poses, sorted by similarity, a second step is performed to adjust the hand configuration with the purpose of eliminating penetration of the object. Lastly, the grasp stability is tested in order to definitely validate the candidate grasps.

In this paper, we consider a 3D grasping problem. In previous work, we presented the central-axis approach and proven its capability to analyze multifingered grasps. In the present paper, we extend our study by developing and analyzing modified force-closure algorithms, and giving rigorous theoretical demonstrations. Through numerical simulations, we show that the proposed approach is computationally efficient when comparing with the qualitative ray-shooting algorithm. The proposed quantitative force-closure test offers a good quality metric without computing the convex hull of the primitive contact wrenches in R6, which efficiently reduces the amount of computation. Several simulation examples showing the efficiency of the proposed approach are included in the paper.

To build autonomous robots capable to plan and control tasks in human environments, we need a description of trajectories that allows the robot to reason on his moves. In this paper we propose to use series of cubic polynomial curves to define the trajectories with bounded jerk, acceleration and velocity. This solution is well adapted to plan safe and acceptable moves of the robot in the vicinity of humans. It is also a simple solution to approximate any trajectory and synchronize different robots or element of the robots. These curves have a simple representation, can be computed quickly and when used in a fitting algorithm can build controller.

This paper proposes a planning framework to deal with the problem of computing the motion of a robot with dual arm/hand, during an object pick-and-place task. We consider the situation where the start and goal configurations of the object constrain the robot to grasp the object with one hand, to give it to the other hand, before placing it in its final configuration. To realize such a task, the proposed framework treats the grasp computation, for one or two multi-fingered hands, of an arbitrarily-shaped object, the exchange configuration and finally the motion of the robot arms and body. In order to improve the planner performance, a context-independent grasp list is computed offline for each hand and for the given object as well as computed offline roadmap that will be adapted according to the environment composition. Simulation results show the planner performance on a complex scenario.

In this paper, we propose a new approach for computing force-closure grasps of two-dimensional and three-dimensional objects. Assuming n hard-finger contact with Coulomb friction model and based on central axes of the grasp wrench (i.e., force and torque), we develop a new necessary and sufficient condition for n-finger grasps to achieve force-closure. We demonstrate that a grasp is force-closure if and only if, its wrench can generate any arbitrary central axis. According to this condition, we reformulate the force-closure test as a linear programming problem without computing the convex hull of the primitive contact wrenches. Therefore, we present an efficient algorithm for computing n-finger force-closure grasps. Finally, we have implemented the proposed algorithm and verified its efficiency through some examples.

Human interaction introduces two main constraints: safety and comfort. Therefore service robot manipulator canpsilat be controlled like industrial robotic manipulator where personnel is isolated from the robotpsilas work envelope. In this paper, we present a soft motion trajectory planner to try to ensure that these constraints are satisfied. This planner can be used on-line to establish visual and force control loop suitable in presence of human. The cubic trajectories build by this planner are good candidates as output of a manipulation task planner. The obtained system is then homogeneous from task planning to robot control. The soft motion trajectory planner limits jerk, acceleration and velocity in cartesian space using quaternion. Experimental results carried out on a Mitsubishi PA10-6CE arm are presented.

The future personal robot must be capable to work and take decisions in a dynamic human environment. One important capability of such a robot is manipulation. Grasp, as the beginning of any manipulation task is a key point. In this paper we present a grasp planner for manipulating real objects modeled by polyhedra. The algorithm is based on a random generation of grasp oriented by mass and inertial properties of the object. Recent techniques to filter and evaluate the quality of grasp are discussed.

The authors present an assembly motion planner for two polyhedral objects. The retraction subspace is defined by the configuration of these objects where they are in a particular contact such that the motion has zero or one degree of freedom. The planner gives assembly trajectories defined by a set of contacts. Each portion of the generated path is associated to a contact state with constant qualitative properties. A contact graph is associated to the boundary structure of the configuration space. The planner includes an incremental method to build the graph and to find a path. Some heuristics are defined to select locally the best arcs to compute. A first version of the planner has been implemented. Simulation results of an assembly trajectory planned by the system are presented.>