Conforming to safety standards often limits collaborative robots' performance and size, restricting their applications despite their capabilities. Planning their motions in human environments involves a trade-off between optimal trajectory planning and quick adaptation to dynamic, unstructured spaces. Traditional trajectory planning methods either use simplified robot models and sacrifice robot's abilities for computational efficiency, or exploit robots' abilities fully but have high computational complexity and rely on substantial pre-computation. This paper introduces an approach for trajectory planning that exploits robot's full motion abilities while planning on-the-fly. In each step of the trajectory execution, it evaluates robot's movement ability using polytope algebra and calculates a time-optimal Trapezoidal Acceleration Profile (TAP) on the remaining trajectory. The method is shown to be near time-optimal (around 5% slower trajectories) by benchmarking it against the state-of-the-art time-optimal method TOPP-RA. The method allows reaching higher velocities (able to plan up to 100% of the robot's kinematic limits) while at the same time lowering the tracking error (under 4mm) than traditional Cartesian Space planning methods. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.

The presented work tackles the question of quantifying the pose deviations of robots subject to external disturbance forces. While this question may not be central for large robots perfectly rejecting disturbances through high controller gains, it is an important factor when considering collaborative settings where smaller robots may be deviated from their task because of unmodeled physical interactions. This is all the more true with human-robot collaboration where human capacities may fluctuate over time and have to be compensated by a proper adaptation of the robot control. To move forward in this direction, this work first derives a deviation prediction methodology and exemplifies it using three largely employed control approaches. The proposed prediction method is then validated using simulated and real robot experiments both in single and multiple robots cases. The obtained results constitute a stepping stone towards a quantitative metric for robots adapting their behaviour to human motor fluctuation.

Inverse optimal control (IOC) is a framework used in many fields, especially in robotics and human motion analysis. In this context, various methods of resolution have been proposed in the literature. This article presents Projected Inverse Optimal Control (PIOC), an approach that offers a simple and comprehensive view of IOC methods. Especially, we explain how uncertainties can be properly addressed in our view. Thus, this article highlights how classical methods can be understood as projections of trajectories in the solution space of the underlying Direct Optimal Control (DOC) problem. This perspective allows for an examination of projections other than the classical methods, which can be fruitful for researchers in the field. As an example, we propose a projection that allows us to choose the underlying cost functions of an IOC problem from a set. The IOC's sub-problems are also addressed, such as modelling observed trajectories, noise measurement and the reliability of solutions obtained by IOC. Our proposal is supported by a simple and canonical example throughout the document.

In this paper, the concept of signaling motions of a robot interacting with a human is presented. These motions consist in using the redundant degrees of freedom of a robot performing a task as new means of meaningful robot-human communication. They are generated through quasi-static torque control, in consistency with the main robot task. A double within-subject (N=16) study is conducted to evaluate the effects of two signaling motions on the performance of a task by participants and on their behavior towards the robot. Our results show a positive effect on both the task execution and the participants behavior. Additionally, both signaling motions seem to improve the situation awareness of the participants by fueling their mental model throughout the interaction.

Inverse Optimal Control (IOC) is a popular method for human motion analysis. In the context of these methods it is necessary to pay attention to the reliability of the results. This paper proposes an approach based on the evaluation of Karush-Kuhn-Tucker conditions relying on a complete analysis with Singular Value Decomposition and provides a detailed analysis of reliability. With respect to a ground truth, our simulations illustrate how the proposed method analyzes the reliability of the resolution. After introducing a clear methodology, the properties of the matrices are studied with different noise levels and different experimental models and conditions. We show how to implement the method, step by step, by explaining the numerical difficulties encountered during the resolution and thus how to make the results of the IOC problem reliable.

Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize the robot’s task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory planning. In this paper, a novel on-line polytope vertex search algorithm is proposed. It exploits the parallelotope geometry of actuator constraints. The proposed algorithm significantly reduces the complexity and computation time of the vertex search problem in comparison to commonly used algorithms. In order to highlight the on-line capability of the proposed algorithm and its potential for robot control, a challenging experiment with two collaborating Franka Emika Panda robots, carrying a load of 12 kilograms, is proposed. In this experiment, the load distribution is adapted on-line, as a function of the configuration dependant task-space force capability of each robot, in order to avoid, as much as possible, the saturation of their capacity.

In this work, a robotic assistance strategy is developed to improve the safety in an artisanal task that involves a strong interaction between a machine-tool and an operator. Wood milling is chosen as a pilot task due to its importance in carpentry and its accidentogenic aspect. A physical model of the tooling process including a human is proposed and a simulator is thereafter developed to better understand situations that are dangerous for the craftsman. This simulator is validated with experiments on three subjects using an harmless mock-up. This validation shows the pertinence of the proposed control approach for the collaborative robot used to increase the safety of the task.

Despite the many advances in collaborative robotics, collaborative robot control laws remain similar to the ones used in more standard industrial robots, significantly reducing the capabilities of the robot when in proximity to a human. Improving the efficiency of collaborative robots requires revising the control approaches and modulating online and in real-time the low-level control of the robot to strictly ensure the safety of the human while guaranteeing efficient task realization. In this work, an openly simple and fast optimization based joint velocity controller is proposed which modulates the joint velocity constraints based on the robot's braking capabilities and the separation distance. The proposed controller is validated on the 7 degrees-of-freedom Franka Emika Panda collaborative robot.

Flexure joints are widely used to ensure manipulator mobility in difficult environmental conditions such as for micro-manipulation and in aerospace application. The use of flexure joints may imply complex kinematic behaviors that need to be considered to increase the device accuracy. This paper proposes, compares and analyzes three incremental models of the flexure joints used in an active wrist parallel telescope. A spherical joint model, a model issued from the beam theory and a stiffness model are considered. Because of uncertainties on the model parameters, an accurate experimental calibration with photogrammetry data is used to choose the most suitable model considering the telescope requirements. The experiments show that observability issue in calibration is the main factor limiting the model complexity in our case.

The model of a robot may not be able to consider all the physical phenomena influencing the manipulator performances since they are too numerous and/or difficult to measure: this is model discrepancy. For a highly-accurate active space telescope, an important source of inaccuracy was measured using photogrammetry: the deformation of its mobile platform. This deformation cannot be directly measured in space and needs to be properly modeled in order to enable the telescope calibration with the available measurements. Two incremental models are proposed and the parameter observability is discussed. After experimental calibration, a micrometer accuracy can be reached. The influence of model discrepancy on the experimental parameter identification is finally discussed.

This paper deals with the wrench-feasible workspace (WFW) ofn-degree-of-freedom parallel robots driven bynor more thanncables. The WFW is the set of mobile platform poses for which the cables can balance any wrench of a given set of wrenches, such that the tension in each cable remains within a prescribed range. Requirements of nonnegative cable tensions, as well as maximum admissible tensions, are thus satisfied. The determination of the WFW is an important issue since its size and shape are highly dependent on the geometry of the robot and on the ranges of allowed cable tensions. The approach proposed in this paper is mainly based on interval analysis. Two sufficient conditions are presented, namely, a sufficient condition for a box of poses to be fully inside the WFW and a sufficient condition for a box of poses to be fully outside the WFW. These sufficient conditions are relevant since they can be tested, with the means to test them being discussed in the paper. Used within usual branch-and-prune algorithms, these tests enable WFW determinations in which full-dimensional sets of poses (volumes) are found to lie within or, on the contrary, to lie outside the WFW. This provides a useful alternative to a basic discretization, the latter consisting of testing a discrete (zero-dimensional) finite set of poses. In order to improve the efficiency of the computations, a means to mitigate the undesirable effects of the so-called wrapping effect is introduced. The paper also illustrates how the proposed approach is capable of dealing with small uncertainties on the geometric design parameters of a parallel cable-driven robot.

This paper presents the preliminary development of a full scale, portable, modular, fully autonomous parallel wire crane that is intended to be used for rescue operations. This design is innovative in terms of flexibility for managing the location of the anchor points of the cables on the ground and on the platform, the possibility of partial platform location control through the management of the kinematics with less than 6 cables and the availability of on-board sensors allowing to possibly locate victims through thermal imaging and to transmit physiological data on the victims while they are still moving toward a safe area.

An automated design generation algorithm for a serial kinematic chain is presented for the reconfigurable robot used in a novel endoluminal surgical procedure (European Union project ARES). The algorithm produces the possible topologies, given the design constraints, desired performance, and available modules, such that all constraints are satisfied for every point in the desired workspace. This is achieved through the use of interval analysis methods and branch-and-bound loop that searches through the end-effector pose and the design parameter spaces. The resulting algorithm is demonstrated through an example of a serial chain manipulator made of the reconfigurable modules of the surgical robot for the application. The results are presented and discussed.

This paper presents the design strategy of serial manipulators with constraint satisfaction. The algorithm provides certified solutions to the range of values of the manipulator design parameters that satisfy the given constraints for all points inside a desired workspace. Alternatively, it can also be used to obtain the achievable workspace of a particular manipulator topology within which a set of given constraints are satisfied. This strategy can therefore be applied to the general case of a serial manipulator design problem, robots of adjustable parameters, or even reconfigurable robot strategy to obtain a suitable topology. The interval-based algorithm was implemented on an example serial anthropomorphic manipulator with joint displacement constraints and obtains the possible variations to the manipulator topology that allow the required workspace to be achievable under the given joint displacement constraints. Results are presented and discussed.

This paper addresses the problem of certifying the performance of a precision flexure-base mechanism design with respect to the given constraints. Due to the stringent requirements associated with the applications of flexure-based precision mechanisms, it is necessary to be able to evaluate and certify the performance at the design stage, taking into account the possible sources of errors: such as fabrication tolerance and modeling inaccuracies in flexure joints. An interval-based method is proposed to certify whether various constraints are satisfied for all points within a required workspace. This paper presents the interval-based methodology and its implementation on a planar 3RRR parallel flexure-based manipulator.

The wrench-feasible workspace (WFW) of a parallel cable-driven mechanism is the set of poses of its mobile platform for which the cables can balance any wrench in a specified set of wrenches, such that the tension in each cable remains within a prescribed range. The WFW is fundamental since it takes into account both the requirement of non-negative cable tension and the requirement of a maximum admissible cable tension. This paper addresses the problem of the determination of the WFW of n-degree-of-freedom parallel mechanisms driven by more than n cables. Interval analysis based methods that allow to determine if a given n-dimensional box is fully included in the WFW are presented. Moreover, these methods are also able to approximate the WFW up to a chosen accuracy. The resulting approximation consists of a set of n-dimensional boxes such that each box of the set is fully included in the WFW.

We are considering a 6 d.o.f. Gough platform that has to move within a given workspace or on a trajectory. The legs of the robot are assumed to be constituted of a set of finite cylindrical elements and we address the problem of determining if any pair of such element would intersect during the robot motion. Collision conditions may be written mathematically according to various equivalent formulations. We show however that these formulations are not numerically equivalent and exhibit an efficient interference checking algorithm based on interval analysis that allows to check 6D workspace or arbitrary time-function trajectories for interference

We are considering a n d.o.f. parallel robot that has to move within a given workspace and whose geometry is defined by a set of parameters. The motion of active joints of the manipulator are measured with sensors with a known accuracy ±Δρ. These errors together with bounded manufacturing errors on the parameters describing the geometry of the robot induces a positioning errors ΔX of the platform. We present an algorithm that allows one to determine geometries of the robot ensuring that these positioning errors will lie within pre-specified limits for any pose of the robot in its workspace even if the physical realization of the robot differs from the theoretical model while staying within the given manufacturing errors bounds. A by-product variant of this algorithm allows one to compute the maximal positioning errors of a given robot up to a predefined accuracy.

In this paper, we demonstrate how methods based on interval arithmetic and interval analysis can be used to achieve numerical certification of the kinematic calibration of a parallel robots. We introduce our work by describing the usual calibration methods and the motivations for a numerical certification. Then, we briefly present the interval methods we used and the kinematic calibration problem. In the main part, we develop our certified approach of this problem in the case of a Gough platform, and we show with numerical examples how this approach avoids wrong solutions produced by classical approach. Details on implementation and performance are also given.

The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. The present paper proposes an algorithm based on a constrained optimization method which allows one to choose an set the measurement configurations. It works by selecting iteratively one pose after another inside the workspace, and after a few steps a set of N configurations is obtained which maximizes an index of observability associated to the parameter Jacobian. Our algorithm converges to a set of poses that are localized at the. boundary of the articular workspace. Finally, a validation through simulation of a calibration experience shows that selected configurations improve significatively the kinematic parameters identification by dividing 10 to 15 the noise associated to the results.

A new algorithm for calibrating Gough platforms is proposed. It requires internal sensor measurements and only the position information is provided by external sensors. It removes the need to measure orientation, which is intricate and error-prone, by algebraic elimination. This approach, relying on resultant and dialytic elimination, produces an equivalent, yet simpler, set of equations. A numerical simulation is given to compare the existing techniques with our method using partial information, which proves to be significantly more robust, without compromising accuracy. It reduces initial error in pose determination by 99% and 80-98%, in two sets of experiments with realistic conditions. We compare different choices for the measured configurations and show the relevance of configurations at the workspace's boundary. This increases reliability by avoiding to use any random measured configurations.