In this paper we present a novel geometric approach
to motion planning for constrained robot systems.
This problem is notoriously hard, as classical sampling-based
methods do not easily apply when motion is constrained in
a zero-measure submanifold of the configuration space. Based
on results on the functional controllability theory of dynamical
systems, we obtain a description of the complementary spaces
where rigid body motions can occur, and where interaction
forces can be generated, respectively. Once this geometric setting
is established, the motion planning problem can be greatly
simplified. Indeed, we can relax the geometric constraint, i.e.,
replace the lower–dimensional constraint manifold with a fulldimensional
boundary layer. This in turn allows us to plan
motion using state-of-the-art methods, such as RRT*, on points
within the boundary layer, which can be efficiently sampled. On
the other hand, the same geometric approach enables the design
of a completely decoupled control scheme for interaction forces,
so that they can be regulated to zero (or any other desired
value) without interacting with the motion plan execution.
A distinguishing feature of our method is that it does not
use projection of sampled points on the constraint manifold,
thus largely saving in computational time, and guaranteeing
accurate execution of the motion plan. An explanatory example
is presented, along with an experimental implementation of the
method on a bimanual manipulation workstation.
In motor control studies, the question on which
parameters human beings and animals control through their
nervous system has been extensively explored and discussed,
and several hypotheses proposed. It is widely acknowledged
that useful inputs in this problem could be provided by
developing artificial replication of the neuromusculoskeletal
system, to experiment different motor control hypothesis. In
this paper we present such device, which reproduces many of
the characteristics of an agonistic-antagonistic muscular pair
acting on a joint.
Several advanced control laws are available for
complex robotic systems such as humanoid robots and mobile
manipulators. Controls are usually developed for locomotion or
for manipulation purposes. Resulting motions are usually executed
sequentially and the potentiality of the robotic platform
is not fully exploited.
In this work we consider the problem of loco–manipulation
planning for a robot with given parametrized control laws
known as primitives. Such primitives, may have not been
designed to be executed simultaneously and by composing
them instability may easily arise. With the proposed approach,
primitives combination that guarantee stability of the system
are obtained resulting in complex whole–body behavior.
A formal definition of motion primitives is provided and a
random sampling approach on a manifold with limited dimension
is investigated. Probabilistic completeness and asymptotic
optimality are also proved. The proposed approach is tested
both on a mobile manipulator and on the humanoid robot
Walk-Man, performing loco–manipulation tasks.
Notes
This work is supported by the European commission project Walk-Man EU FP7-ICT no. 611832 and the ECs Horizon 2020 robotics program ICT-23-2014 under grant agreement 644727 (CogIMon)
Compliance in robot design and control is often introduced to improve the robot performance in tasks where interaction with environment or human is required. However a rigorous method to choose the correct level of compliance is still not available. In this work we use robust optimization as a tool to select the optimal compliance value in a robotenvironment interaction scenario under uncertainties. We propose an approach that can be profitably applied on a variety of tasks, e.g.manipulation tasks or locomotion tasks. The aim is to minimize the forces of interaction considering model constraints and uncertainties. Numerical results show that: i) in case of perfect knowledge of the environment stiff robots behave better in terms of force minimization, ii) in case of uncertainties the optimal stiffness of the robot is lower than the previous case and optimal solutions provide a faster task accomplishment, iii) the optimal stiffness decreases as a function of the uncertainty measure. Experiments are carried out in a realistic set-up in case of bi-manual object handover.
Notes
This work was supported by the European Commission projects (FP7 framework) Walk-Man and the European Commission Grant no. H2020- ICT-645599 “SOMA”: SOft MAnipulation
In this paper a quasi-static framework for optimally controlling the contact force distribution is experimentally verified with the full-size compliant humanoid robot Walk-Man. The proposed approach is general enough to cope with multi-contact scenarios, i.e. robot-environment interactions occurring on feet and hands, up to the more general case of whole-body loco-manipulation, in which the robot is in contact with the environment also with the internal limbs, with a consequent loss of contact force controllability. Experimental tests were conducted with the Walk-Man robot (i) standing on flat terrain, (ii) standing on uneven terrain and (iii) interacting with the environment with both feet and a hand touching a vertical wall. Moreover, the influence of unmodeled weight on the robot, and the combination with a higher priority Cartesian tasks are shown. Results are presented also in the attached video.
Many walking robot presented in literature stand
on rigid flat feet, with a few notable exceptions that embed
flexibility in their feet to optimize the energetic cost of walking.
This paper proposes a novel adaptive robot foot design, whose
main goal is to ease the task of standing and walking on uneven
terrains. After explaining the rationale behind our design
approach, we present the design of the SoftFoot, a foot able
to comply with uneven terrains and to absorb shocks thanks to
its intrinsic adaptivity, while still being able to rigidly support
the stance, maintaining a rather extended contact surface,
and effectively enlarging the equivalent support polygon. The
paper introduces the robot design and prototype and presents
preliminary validation and comparison versus a rigid flat foot
with comparable footprint and sole.
A novel MATLAB/Simulink based modeling and simulation environment for the design and rapid prototyping of state-of-the-art aircraft control systems is proposed. The toolbox, named APRICOT, is able to simulate the longitudinal and laterodirectional dynamics of an aircraft separately, as well as the complete 6 degrees of freedom dynamics. All details of the dynamics can be easily customized in the toolbox, some examples are shown in the paper. Moreover, different aircraft models can be easily integrated. The main goal of APRICOT is to provide a simulation environment to test and validate different control laws with different aircraft models. Hence, the proposed toolbox has applicability both for educational purposes and control rapid prototyping. With respect to similar software packages, APRICOT is customizable in all its aspects, and has been released as open source software. An interface with Flightgear Simulator allows for online visualization of the flight. Examples of control design with simulation experiments are reported and commented.
Due to the increasing usage of service and industrial autonomous vehicles, a precise localisation is an essential component required in many applications, e.g. indoor robot navigation. In open outdoor environments, differential GPS systems can provide precise positioning information. However, there are many applications in which GPS cannot be used, such as indoor environments. In this work, we aim to increase robot autonomy providing a localisation system based on passive markers, that fuses three kinds of data through extended Kalman filters. With the use of low cost devices, the optical data are combined with other robots’ sensor signals, i.e. odometry and inertial measurement units (IMU) data, in order to obtain accurate localisation at higher tracking frequencies. The entire system has been developed fully integrated with the Robotic Operating System (ROS) and has been validated with real robots.
A Serious Game (SG) system for the assessment of the potential of the multi-vehicle surveillance is presented. The SG system is applied to the problem of protection of strategic assets from underwater asymmetric threats. The SG platform integrates the active sonar performance evaluator able to estimate the real performance on the basis of the environmental conditions. The final goal is to provide new technology tools to realize a Decision Support System (DDS) to support the design phase of a naval unit. The SG system is developed in the framework of the ProDifCon project supported by the (DLTM) (Italy).
NoStop is an open source simulator dedicated to distributed and cooperative mobile robotics systems. It has been designed as a framework to design and test multi–agent collaborative algorithms in terms of performance and robustness. The particular application scenario of a team of autonomous guards that coordinate to protect an area from asymmetric threat is considered. NoStop system is an integrated tool able to both evaluate the coordination protocol performance and to design the team of guards involved in the asymmetric threat protection. Moreover, NoStop is designed to validate robustness of coordination protocol through the use of a remote pilot that control the intruder motion to escape from the guards that monitor the area and accomplish its mission. The project core is a simulation server with a dynamic engine and a synchronization facility. Different coordination protocol can be designed and easily integrated in NoStop. The framework is fully integrated with the Robot Operating System (ROS) and it is completed by a control station where the remote pilot moves the intruder following the guards evolution in a 3D viewer.
Palpation is an essential step of several open surgical procedures for locating arteries by arterial pulse detection. In this context, surgical simulation would ideally provide realistic haptic sensations to the operator. This paper presents a proof of concept implementation of tactile augmented reality for open-surgery training. The system is based on the integration of a wearable tactile device into an augmented physical simulator which allows the real time tracking of artery reproductions and the user finger and provides pulse feedback during palpation. Preliminary qualitative test showed a general consensus among surgeons regarding the realism of the arterial pulse feedback and the usefulness of tactile augmented reality in open-surgery simulators.
This work proposes a game theoretic approach
to tackle the problem of multi-robot coordination
in critical scenarios where communication is
limited and the robots must accomplish different tasks
simultaneously. An important application falls in underwater
robotic framework where robots are used to
protect a ship against asymmetric threats guaranteeing
simultaneously the coverage of the area around the ship
and the tracking of a possible intruder. The problem is
modelled as a potential game for which novel learning
protocols are introduced. Indeed, a general extension
of pay-off based algorithms is herein proposed where
the main difference with state-of-the-art protocols is
that the trajectory optimization is considered instead
of single action optimization. Moreover, the proposed
T-algorithms, steer the robots toward Nash equilibria
that will be shown to correspond to the accomplishment
of different, possibly antagonistic, goals. Finally, performances
of the algorithms, under different scenarios,
have been evaluated in simulations.
The purpose of this work is to move a step toward the automation of industrial plants through full exploitation of autonomous robots. A planning algorithm is proposed to move different objects in desired configurations with heterogeneous robots such as manipulators, mobile robots and conveyor belts.
The proposed approach allows different objects to be handled by different robots simultaneously in an efficient way and avoiding collisions with the environment and self–collisions between robots. In particular, the integrated system will be capable of planning paths for a set of objects from various starting points in the environment (e.g. shelves) to their respective final destinations. The proposed approach unifies the active (e.g., grasping by a hand) and passive (e.g., holding by a table) steps involved in moving the objects in the environment by treating them as end–effectors with constraints and capabilities.
Time varying graphs will be introduced to model the problem for simultaneous handling of objects by different end–effectors.
Optimal exploration of such graphs will be used to determine paths for each object with time constraints. Results will be validated through simulations.
The deployment of robots to assist in environments hostile for humans during emergency scenarios require robots to demonstrate enhanced physical performance, that includes adequate power, adaptability and robustness to physical interactions and efficient operation. This work presents the design and development of the lower body of the new high performance humanoid WALK-MAN, a robot developed recently to assist in disaster response scenarios. The paper introduces the details of the WALK-MAN lower-body, highlighting the innovative design optimization features considered to maximize the leg performance. Starting from the general lower body specifications the objectives of the design and how they were addressed are introduced, including the selection of the leg kinematics, the arrangement of the actuators and their integration with the leg structure to maximize the range of motion, reduce the leg mass and inertia, and shape the leg mass distribution for better dynamic performance. Physical robustness is ensured with the integration of elastic transmission and impact energy absorbing covers. Experimental walking trials demonstrate the correct operation of the legs while executing a walking gait.
State of the art of hand prosthetics is divided between simple and reliable gripper-like systems and sophisticate hi-tech poly-articular hands which tend to be complex both in their design and for the patient to operate. In this paper, we introduce the idea of decoding different movement intentions of the patient using the dynamic frequency content of the control signals in a natural way. We move a step further showing how this idea can be embedded in the mechanics of an underactuated soft hand by using only passive damping components.
In particular we devise a method to design the hand hardware to obtain a given desired motion. This method, that we call of the dynamic synergies, builds on the theory of linear descriptor systems, and is based on the division of the hand movement in a slow and a fast components. We use this method to evolve the design of the Pisa/IIT SoftHand in a prototype prosthesis which, while still having 19 degrees of freedom and just one motor, can move along two different synergistic directions of motion (and combinations of the two), to perform either a pinch or a power grasp. Preliminary experimental results are presented, demonstrating the effectiveness of the proposed design
