Safe Robotic Grasping: Minimum Impact-Force Grasp Selection

This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving the longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper, we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms applying minimum impact forces if a collision occurs during a desired post-grasp manipulation. We build on the methods of augmented robot-object dynamics models and "effective mass" and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the post-grasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201

Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous analyses on quasi-static pushing mechanics, and introduce a data-driven model that can accurately represent these mechan- ics and provide a prediction for the object’s inertial parameters. We evaluate the model with two datasets. For the first dataset, we set up a V-REP simulation of seven robots pushing objects with large range of inertial parameters, acquiring 48000 pushes in total. For the second dataset, we use the object pushes from the MIT M-Cube lab pushing dataset. We extract features from force, moment and velocity measurements of the pushes, and train a Multi-Output Regression Random Forest. The experimental results show that we can accurately predict the 2D inertial parameters from a single push, and that our method retains this robust performance under various surface types

Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems are addressed jointly and a solution that takes both into consideration is proposed. To do so, a manipulation capability index is defined, which is a function of both the task execution waypoints and the object grasping contact points. We build on recent state-of-the-art grasp-learning methods, to show how this index can be combined with a likelihood function computed by a probabilistic model of grasp selection, enabling the planning of grasps which have a high likelihood of being stable, but which also maximise the robot's capability to deliver a desired post-grasp task trajectory. We also show how this paradigm can be extended, from a single arm and hand, to enable efficient grasping and manipulation with a bi-manual robot. We demonstrate the effectiveness of the approach using experiments on a simulated as well as a real robot

On-Orbit Robotic Grasping of a Spent Rocket Stage:Grasp Stability Analysis and Experimental Results

The increased complexity of the tasks that on-orbit robots have to undertake has led to an increased need for manipulation dexterity. Space robots can become more dexterous by adopting grasping and manipulation methodologies and algorithms from terrestrial robots. In this paper, we present a novel methodology for evaluating the stability of a robotic grasp that captures a piece of space debris, a spent rocket stage. We calculate the Intrinsic Stiffness Matrix of a 2-fingered grasp on the surface of an Apogee Kick Motor nozzle and create a stability metric that is a function of the local contact curvature, material properties, applied force, and target mass. We evaluate the efficacy of the stability metric in a simulation and two real robot experiments. The subject of all experiments is a chasing robot that needs to capture a target AKM and pull it back towards the chaser body. In the V-REP simulator, we evaluate four grasping points on three AKM models, over three pulling profiles, using three physics engines. We also use a real robotic testbed with the capability of emulating an approaching robot and a weightless AKM target to evaluate our method over 11 grasps and three pulling profiles. Finally, we perform a sensitivity analysis to demonstrate how a variation on the grasping parameters affects grasp stability. The results of all experiments suggest that the grasp can be stable under slow pulling profiles, with successful pulling for all targets. The presented work offers an alternative way of capturing orbital targets and a novel example of how terrestrial robotic grasping methodologies could be extended to orbital activities

Formal Modelling and Runtime Verification of Autonomous Grasping for Active Debris Removal

Active debris removal in space has become a necessary activity to maintain and facilitate orbital operations. Current approaches tend to adopt autonomous robotic systems which are often furnished with a robotic arm to safely capture debris by identifying a suitable grasping point. These systems are controlled by mission-critical software, where a software failure can lead to mission failure which is difficult to recover from since the robotic systems are not easily accessible to humans. Therefore, verifying that these autonomous robotic systems function correctly is crucial. Formal verification methods enable us to analyse the software that is controlling these systems and to provide a proof of correctness that the software obeys its requirements. However, robotic systems tend not to be developed with verification in mind from the outset, which can often complicate the verification of the final algorithms and systems. In this paper, we describe the process that we used to verify a pre-existing system for autonomous grasping which is to be used for active debris removal in space. In particular, we formalise the requirements for this system using the Formal Requirements Elicitation Tool (FRET). We formally model specific software components of the system and formally verify that they adhere to their corresponding requirements using the Dafny program verifier. From the original FRET requirements, we synthesise runtime monitors using ROSMonitoring and show how these can provide runtime assurances for the system. We also describe our experimentation and analysis of the testbed and the associated simulation. We provide a detailed discussion of our approach and describe how the modularity of this particular autonomous system simplified the usually complex task of verifying a system post-development.</jats:p