We present a Kalman filter-based approach for estimating external forces and torques relying on a dynamic model of a serial-chain robotic manipulator where only motor signals (currents, joint angles, and joint speeds) are measurable. The method does not require any additional sensing compared to standard robot control systems. The approach exploits redundancy in 7DOF arms, but also applies to traditional 6DOF manipulators. Automatic filter calibration routines are presented minimizing the number of parameters that must be tuned in order to successfully apply the proposed scheme and to optimize estimation quality. The approach is verified by measurement data gathered from an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm. Furthermore, measurement results are presented employing force and torque estimates in a compliance control scheme, verifying that the estimation quality achieved is improved compared to existing approaches and is sufficient to employ the estimates in force-controlled applications.Note to Practitioners—More and more robotic applications involve contact with at least partially unknown environments. As a consequence, they require control approaches that go beyond the traditional position control. In particular, information about contact forces and torques has to be taken into account. However, integrating additional sensing equipment to obtain the required force/torque information is often technically challenging and expensive. Cartesian contact force and torque estimation allows obtaining force/torque information solely from available sensors. The estimation technique can be regarded as a virtual sensor, and hence this brief deals with a key technology enabling force controlled robotic applications such as assembly, grinding, and deburring without the need for expensive additional sensing.

Robotic assembly tasks are subject to uncertainties arising from part tolerances. A popular approach to deal with such partially unstructured environments is to introduce compliance by using admittance control schemes. For such schemes, contact forces and torques are speed dependent, which often limits the achievable assembly speed. To overcome this limitation, we present a new method for increasing the achievable speed of compliant manipulators by iteratively reducing contact forces. The presented concept of Iteratively Learned and Temporally Scaled Force Control (ILTSFC) is based on two coupled iterative learning controllers where one increases the assembly speed and the other one adjusts the reference trajectory to reduce contact forces. The approach is verified by an experimental peg-in-hole study on an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm.

DisCoverage is a distributed strategy for frontier-based multi-robot exploration. The robots coordinate by a partition of the environment, and choose their target points by optimizing a locally decomposable objective function. In [9] DisCoverage for convex regions was proposed. In this work, we extend DisCoverage to support arbitrary non-convex real-world environments with obstacles. Therefore, we introduce a transformation of non-convex environments to robot centric star-shaped domains. This results in a general solution with broader applications for exploration and path planning. Simulations as well as experiments with real robots demonstrate the exploration progress.

The main aspect in multi-robot exploration is the efficient coordination of a group of robots. Inspired by previous results on the coverage problem, we propose a novel, frontier-based approach for multi-robot exploration. This approach merges the step of choosing appropriate target points with the step of planning a collision-free path. This is achieved by optimizing an objective function consisting of distance and orientation costs as well as an estimated information gain. The optimization yields motion control laws directly solving the exploration task. Using a Voronoi partition of the environment ensures, that each robot autonomously creates and optimizes the objective function to obtain a collision-free path in a distributed fashion. Simulations demonstrate the effectiveness of our approach.

In this article we present novel formation control laws based on artificial potential fields and consensus algorithms for a group of unicycles enabling arbitrary formation patterns for these nonholonomic vehicles. Given connected and balanced graphs we are able to prove stability of the rendezvous controller by applying the LaSalle-Krasovskii invariance principle. Further, we introduce obstacle avoidance, enabling a reactive behavior of the robotic group in unknown environments. The effectiveness of the proposed controllers is shown using computer simulations and finally, a classification w.r.t. existing solutions is done.