Robots have surpassed humans in terms of strength and precision, yet humans retain an unparalleled ability for decision-making in the face of unpredictable disturbances. This article aims to combine the strengths of both entities within a singular task: human motion guidance under strict geometric constraints, particularly adhering to predetermined paths. To tackle this challenge, a modular haptic guidance law is proposed that takes the human-applied wrench as an input. Using an auxiliary variable called phase, the generated desired motion is guaranteed to consistently adhere to the constraint path. The guidance policy can be generalized into physically interpretable terms, adjustable either prior to initiating the task or dynamically while the task is in progress. An illustrative guidance adaptation policy is showcased that takes into account the human's manipulability. Passivity analysis is used to ensure overall system stability. Experiments, including a 20-participant user study, explore various aspects of the approach in practice.

Robots have surpassed humans in terms of strength and precision, yet humans retain an unparalleled ability for decision-making in the face of unpredictable disturbances. This article aims to combine the strengths of both entities within a singular task: human motion guidance under strict geometric constraints, particularly adhering to predetermined paths. To tackle this challenge, a modular haptic guidance law is proposed that takes the human-applied wrench as an input. Using an auxiliary variable called phase, the generated desired motion is guaranteed to consistently adhere to the constraint path. It is demonstrated how the guidance policy can be generalized into physically interpretable terms, adjustable either prior to initiating the task or dynamically while the task is in progress. Additionally, an illustrative guidance adaptation policy is showcased that takes into account the human's manipulability. Leveraging passivity analysis, potential sources of instability are pinpointed, and subsequently, overall system stability is ensured by incorporating an augmented virtual energy tank. Lastly, a comprehensive set of experiments, including a 20-participant user study, explores various aspects of the approach in practice, encompassing both technical and usability considerations.

Manipulability analysis is a methodology employed to assess the capacity of an articulated system, at a specific configuration, to produce motion or exert force in diverse directions. The conventional method entails generating a virtual ellipsoid using the system’s configuration and model. Yet, this approach poses challenges when applied to systems such as the human body, where direct access to such information is limited, necessitating reliance on estimations. Any inaccuracies in these estimations can distort the ellipsoid’s configuration, potentially compromising the accuracy of the manipulability assessment. To address this issue, this article extends the standard approach by introducing the concept of the manipulability pseudo-ellipsoid. Through a series of theoretical analyses, simulations, and experiments, the article demonstrates that the proposed method exhibits reduced sensitivity to noise in sensory information, consequently enhancing the robustness of the approach.

Teleoperated robots are believed to play an important role for future applications in industry, medicine and other domains. Examples for this are remote assembly and maintenance, surgery, diagnosis or deep-sea and space exploration. Such applications are made possible by state-of-the-art tactile manipulators, well-researched control schemes and novel communication technologies such as the fifth generation of mobile communication (5G). The achievable performance is highly dependent on the communication delay and thus on the distance between leader and follower station, as well as the potentially used wireless protocol. Specially in this regard, 5G is a promising technology compared to the other communication protocols for transferring tactile information. In this paper, we introduce our telepresence reference platform, which can be used for empirical evaluation of different algorithms and communications. Comparative analysis are conducted to capture the influence of wireless communication protocols on telepresence systems consisting of complex robotic arms. The experiment compares the influence of 5G, LTE and WiFi communication protocols with regard to the motion and force tracking performance of the system.

In this paper we propose a novel energy-based control scheme for an assist-as-needed rehabilitation strategy, which both adapts the level of support based on patient participation and allows the patient to deviate from the prescribed motion in favor of his/her safety. We build an energy network model, with which we can monitor the energy flow through the system and prescribe a threshold on stored energy. We also develop an adaptive motion control law that shapes the desired trajectory in order to respect the stored energy threshold. Next, we show how adapting the stored energy threshold can be used to change the level of responsiveness to the patient as well as to prevent excessive energy transfer to the human by the system. A criterion is defined for setting this energy threshold, which can be further used for monitoring the patient active participation and for adapting and learning the appropriate assistance level during rehabilitation. Experimental results based on implementation in MATLAB Simscape® and on the VEMO robotic system demonstrate the feasibility of the suggested approach. The presented control scheme can be applied to any system, including position- and torque-controlled robots, and does not require the use of EMG sensors or precise force measurements.

Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact wrench feedback in the adaptation and learning process of the DMP. Despite first approaches exist in this direction, no fully integrated approach has been proposed so far. In this paper, we introduce a new class of admittance-coupled DMPs that addresses environmental changes by including contact wrench feedback dynamics into the DMP formalism. Moreover, a novel iterative learning approach is devised that is based on monitoring the overall system passivity analysis in terms of reference power tracking. Simulations and experimental results with the Kuka LWR robot maintaining a non-rigid contact with the environment (wiping a surface) are shown for supporting the validity of our approach.

In this paper the concept of a unified torque/impedance controller is applied to a pneumatically actuated, antagonistic robot joint. The investigated control algorithm consists of a cascaded structure in which the outer torque/impedance controller commands a desired torque to two cylinder-based force controllers. The torque-/impedance controller is equipped with a virtual tank that ensures passivity of the control loops above force level. Additionally, a shaping function provides a continuous transition to impedance control in case of sudden contact loss during a torque control operation. External torques used in the feedback loop for contact force regulation are estimated by a momentum observer. Experimental and simulation results show a maximum deflection of 9.7° over 130 ms in case of contact loss until a resting position is reached. Additionally, step and sinusoidal torque tracking up to 5 Hz was successfully tested at good performance.

While collision detection and contact-related injury reduction in physical human-robot interaction has been studied intensively, safety issues in physical human-robot collaboration (pHRC) with continuous coupling of human and robot(s) has received little attention so far. We develop an energy monitoring control system that observes energy flows among the different subsystems involved in pHRC, shaping them to improve human safety according to selected metrics. Port-Hamiltonian formalisms are used to model each sub-system and their interconnection. An energy-based compliance controller that enhances safety by adapting the robot behavior is proposed and validated through extensive simulations.