Lower limb exoskeletons and exosuits have shown promise in augmenting human physical capabilities, with applications ranging from rehabilitation to performance enhancement. Accurate evaluation of their impact on metabolic energy expenditure is crucial for optimizing design and control strategies. While experimental measurement of metabolic cost via indirect calorimetry provides direct assessment, it is often impractical outside laboratory settings. Computational models offer an alternative, but their effectiveness in predicting metabolic cost changes induced by assistive devices remains underexplored. This study investigates the impact of incorporating different levels of complexity and sensory information, as well as various metabolic cost models, on estimating muscle metabolic cost during walking with a passive biarticular thigh exosuit. We compare three modeling approaches: joint-space dynamics, musculoskeletal simulation with effort minimization, and EMG-informed musculoskeletal simulation, each employing several metabolic models. Results show that EMG-informed musculoskeletal simulation, particularly using the Uchida (2016) metabolic model, provides the highest accuracy in predicting metabolic cost changes. Musculoskeletal simulation with effort minimization also shows promise, offering a viable alternative without the need for EMG data. These findings highlight the potential of computational models in evaluating and optimizing assistive devices.

We propose a real-time dynamic LiDAR odometry pipeline for mobile robots in Urban Search and Rescue (USAR) scenarios. Existing approaches to dynamic object detection often rely on pretrained learned networks or computationally expensive volumetric maps. To enhance efficiency on computationally limited robots, we reuse data between the odometry and detection module. Utilizing a range image segmentation technique and a novel residual-based heuristic, our method distinguishes dynamic from static objects before integrating them into the point cloud map. The approach demonstrates robust object tracking and improved map accuracy in environments with numerous dynamic objects. Even highly non-rigid objects, such as running humans, are accurately detected at point level without prior downsampling of the point cloud and hence, without loss of information. Evaluation on simulated and real-world data validates its computational efficiency. Compared to a stateof-the-art volumetric method, our approach shows comparable detection performance at a fraction of the processing time, adding only 14 ms to the odometry module for dynamic object detection and tracking. The implementation and a new realworld dataset are available as open-source for further research.

The seamless integration and acceptance of humanoid service robots in society requires a general understanding of those robots. An overall transparent design can be a means to establish this. To guide the implementation of effective transparency mechanisms, we propose the novel Transparency Design Model, which builds on the concept of Interaction Transparency from our previous work. This model encompasses a wide range of transparency aspects aimed to improve the overall transparency of humanoid service robot designs. To demonstrate the applicability of our model and gather initial insights on its impact on robot perception, we conducted a user study involving 50 participants. The study compared a neutral and transparent robot behavior within a complex sales service scenario. Our findings indicate that the transparent design significantly improve the robot’s likeability, anthropomorphism, and benevolence. Additionally, we observed positive trends in attractiveness, stimulation, and novelty as measured by the User Experience Questionnaire. These measurable effects highlight the important role of Interaction Transparency. The results also imply that a holistic system analysis in multiple complex and application-dependent interactions is worthwhile as the service content influences the perception and effects of the individual design steps. Further, there is a need to develop a new scale for measuring Interaction Transparency. Lastly, future research could benefit from employing multi-modal assessment tools.

Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.

Everyday life scenarios where non-expert users (e.g., customers) are confronted with frontline service robots will become more and more likely. In particular, misunderstandings and incidents may occur during these interactions because of wrong expectations of the robot’s capabilities. Current applicable laws are based on technological assumptions from prior decades unsuitable to modern robotics and AI. The new AI Act as a part of the solution to this is still in development. In addition to the pure legal view, a technological viewpoint may be beneficial for establishing a fitting, trustful, and, thus, acceptable technology liability law. This work contributes to this by empirically evaluating the service robot non-expert user’s liability expectations, the use of robots, and well-being. The results in a DIY store environment significantly show that the store deploying the robot should be liable if an incident happens. Further, we examined that even a minor simulated incident affected the participants’ emotions and moods. Consequently, this influences their perception of liability while not mitigating their acceptance of frontline service robots.

In recent years, the capabilities of legged locomotion controllers have been significantly advanced enabling them to traverse basic types of uneven terrain without visual perception. However, safely and autonomously traversing longer distances over difficult uneven terrain requires appropriate motion planning using online collected environmental knowledge. In this paper, we present such a novel methodology for generic closed-loop preceding horizon footstep planning that enables legged robots equipped with capable locomotion controllers to autonomously traverse previously unknown terrain while continuously walking long distances. Hereby, our approach addresses the challenge of online terrain perception and soft real-time footstep planning. The proposed new formulation of the search-based planning problem makes no specific assumptions about the robot kinematics (e.g. number of legs) or the used locomotion control schemes. Therefore, it can be applied to a broad range of different types of legged robots. Unlike current methods, the proposed new framework can optionally consider the floating base as part of the state-space. It is possible to configure the complexity of the planner online, from efficiently solving tasks in flat terrain to using non-contiguous contacts in highly challenging terrain. Finally, the presented methodology is successfully applied and evaluated in virtual and real experiments on state of the art bipedal, quadrupedal, and a novel eight-legged robot.

The ability to optimally control robotic systems offers significant advantages for their performance. While time-dependent optimal trajectories can numerically be computed for high dimensional nonlinear system dynamic models, constraints and objectives, finding optimal feedback control policies for such systems is hard. This is unfortunate, as without a policy, the control of real-world systems requires frequent correction or replanning to compensate for disturbances and model errors.In this paper, a feedback control policy is learned from a set of optimal reference trajectories using Gaussian processes. Information from existing trajectories and the current policy is used to find promising start points for the computation of further optimal trajectories. This aspect is important as it avoids exhaustive sampling of the complete state space, which is impractical due to the high dimensional state space, and to focus on the relevant region.The presented method has been applied in simulation to a swing-up problem of an underactuated pendulum and an energy-minimal point-to-point movement of a 3-DOF industrial robot.

Bipedal robots have gained a lot of locomotion capabilities the past few years, especially in the control level. Navigation over complex and unstructured environments using exteroceptive perception, is still an active research topic. In this paper, we present a footstep planning system to produce foothold placements, using visual perception and proper environment modeling, given a black box walking controller. In particular, we extend a state-of-the-art search-based planning approach (ARA*) that produces 6DoF footstep sequences in 3D space for flat uneven terrain, to also handle rough curved surfaces, e.g. rocks. This is achieved by integrating both a curved patch modeling system for rough local terrain surfaces and a flat foothold contact analysis based on visual range input data, into the existing planning framework. The system is experimentally validated using real-world point clouds, while rough terrain stepping demonstrations are presented on the WALK-MAN humanoid robot, in simulation.

This paper presents a sequential optimum design approach for estimating the parameters of an atmospheric dispersion process model based on measurement data gathered by a team of cooperating sensor-equipped UAVs. Locally optimal waypoint sequences that account for each UAV's possibly heterogeneous motion dynamics are computed by minimizing a suitable optimality criterion. Following these waypoints, the UAVs cooperatively maximize the information gain of the acquired measurements. A decentralized data-driven online control scheme is proposed that couples parameter estimation, waypoint calculation, and vehicle control and enables the UAVs to adaptively observe the dynamic process and iteratively improve the parameter estimate. Simulations demonstrate the effectiveness of the proposed scheme in reducing the error between the estimated and the true dispersion model parameters compared to non-adaptive sensing strategies. In addition, the effect of using different optimality criteria, different numbers and types of UAVs as well as two options for decentralizing the waypoint calculation are investigated.

We present a simple and efficient mathematical framework suitable for generating motion in the context of a variety of robotic motion tasks ranging from low-level motor control up to high-level locomotion planning. Our concept is based on a one-dimensional second-order model that allows analytic computation of its inverse dynamics while respecting physical constraints. This makes it a particularly useful tool for tasks that are expressed only as a start and goal state, such as animation key frames or way points in path planning. By means of time synchronization, the model extends easily to an arbitrary number of dimensions in a way that the target is reached in all dimensions at the same time. The framework excels in terms of execution time, which lies in the microsecond range even for high-dimensional trajectory generation tasks. We demonstrate our method in two different settings - full-body trajectory generation and path planning - and show its benefits in comparison with current state-of-the-art algorithms.

Motivated by the DARPA Robotics Challenge (DRC), the application of operator assisted (semi-)autonomous robots with highly complex locomotion and manipulation abilities is considered for solving complex tasks in potentially unknown and unstructured environments. Because of the limited a priori knowledge about the state of the environment and tasks needed to achieve a complex mission, a sufficiently complete a priori design of high level robot behaviors is not possible. Most of the situational knowledge required for such behavior design is gathered only during runtime and needs to be interpreted by a human operator. However, current behavior control approaches only allow for very limited adaptation at runtime and no flexible operator interaction. In this paper an approach for definition and execution of complex robot behaviors based on hierarchical state machines is presented, allowing to flexibly change the structure of behaviors on the fly during runtime through assistance of a remote operator. The efficiency of the proposed approach is demonstrated and evaluated not only in an example scenario, but also by application in two robot competitions.

Recent improvements in virtual reality technology and head-mounted displays have led to a number of novel and innovative applications in entertainment, education, science and healthcare. The primary goal in most of these applications is to give the user a sensation of being part of the virtual reality. This focus on presence or immersion requires to create a connection between the user and the virtual environment but also between the user and his virtual avatar. Synchronizing the body movement of the user and his avatar can help to improve the feeling of presence by enhancing the experience of agency and body ownership over the avatar. A typical example is the combination of a head-mounted display and a treadmill to create a realistic walking or running simulation in a virtual environment. In this scenario, the synchronization of leg movement improves the feeling of presence and allows to further enhance the virtual experience, for example by adjusting gait parameters of the avatar or triggering customized stepping sounds. This paper presents a robust real-time step detector that uses the integrated sensors of a state-of-the-art head-mounted display and allows to recognize the pattern of individual steps. No additional sensors on the trunk or lower body are required. By applying a coordinate transformation and straightforward signal processing, it is possible to discriminate between left and right steps and detect the current walking speed. This information is used to animate a virtual avatar by scaling a predefined walking trajectory and to control the walking speed within a virtual environment.

Bio-inspired legged robots with compliant actuation can potentially achieve motion properties in real world scenarios which are superior to conventionally actuated robots. In this paper, a methodology is presented to systematically design and tailor passive and active control elements for elastically actuated robots. It is based on a formal specification of requirements derived from the main design principles for embodied agents as proposed by Pfeifer et al. which are transfered to dynamic model based multi objective optimization problems. The proposed approach is demonstrated and applied for the design of a biomechanically inspired, musculoskeletal bipedal robot to achieve walking and human-like jogging.

One of the most important capabilities of bipedal robots for energy-efficient and dynamic locomotion are shock tolerance and energy storage and release. In this paper, we study three robot models with different leg actuation designs by means of highly detailed multibody system dynamics simulation. For this purpose, we first elaborate on the term of energy-efficient and dynamic two-legged hopping and present a performance index. Subsequently we conduct the same experimental setup for passive rebound and soft landing for all models. Among others it is observed that (1) the envisioned dynamic and energy-efficient locomotion cannot be achieved through stiff actuation, (2) the energy restitution can be maximized without sacrificing the dynamic mobility and (3) such passive rebound experiments are well suited to determining the optimal leg actuation design.

Bipedal locomotion can be divided into primitive tasks, namely repulsive leg behavior (bouncing against gravity), leg swing (protraction and retraction) and body alignment (balancing against gravity). In the bipedal spring-mass model for walking and running, the repulsive leg function is described by a linear prismatic spring. This paper adopts two strategies for swinging and bouncing control from conceptual models for the human-inspired musculoskeletal BioBiped robot. The control approach consists of two layers, velocity based leg adjustment (VBLA) and virtual model control to represent a virtual springy leg between toe and hip. Additionally, the rest length and stiffness of the virtual springy leg are tuned based on events to compensate energy losses due to damping. In order to mimic human locomotion, the trunk is held upright by physical constraints. The controller is implemented on the validated detailed simulation model of BioBiped. In-place as well as forward hopping and switching between these two gaits are easily achieved by tuning the parameters for the leg adjustment, virtual leg stiffness and injected energy. Furthermore, it is shown that the achieved motion performance of in-place hopping agrees well with that of human subjects.

This paper presents the design of a lightweight robot arm intended for safe physical human-robot interaction. The robot arm design combines tendon actuation with elasticity in the tendons to achieve a significant reduction in mass and passive compliant behavior. The use of elastic tendons in all joints in order to gain maximum safety and performance properties, however, results in a significant increase of the model complexity with oscillatory behavior and kinematic coupling of the joint equilibrium positions. Therefore, special effort needs to be made to model the robot arm dynamics, which is an essential basis for model-based algorithms utilizing and fully exploiting the particular properties of the robot arm. This paper therefore derives the full dynamics model of the robot arm with focus on the nonlinear elastic tendon actuators, the kinematic tendon coupling, and modeling complexity reduction by reflecting all model parameters to the joint space. The resulting model is validated by comparing the identified simulation model with experimental data of an application-related pick-and-place trajectory and a trajectory with undamped oscillating motions of the robot arm.

Efficient task allocation with timing constraints to a team of possibly heterogeneous robots is a challenging problem with application, e. g., in search and rescue. In this paper a mixed-integer linear programming (MILP) approach is proposed for assigning heterogeneous robot teams to the simultaneous completion of sequences of tasks with specific requirements such as completion deadlines. For this purpose our approach efficiently combines the strength of state of the art mixed-integer linear programming (MILP) solvers with human expertise in mission scheduling. We experimentally show that simple and intuitive inputs by a human user have substantial impact on both computation time and quality of the solution. The presented approach can in principle be applied to quite general missions for robot teams with human supervision.

The Prosthesis-User-in-the-Loop simulator concept represents an approach to integrate users to prosthetic development by a holistic simulation of gait with a prosthesis. It aims at a more user-centered design of lower limb prosthetic devices by utilizing user experience and assessment. As this requires a complex mechanical robot design and sophisticated control strategies that allow for restoring lost biomechanical function, this paper presents the conception and design of a hardware simulator for proof-of-concept studies of those issues. For those investigations, the ankle joints of healthy praticpants are locked mechanically to induce a temporary disability. The task of the simulator is to provide a simulation of physiological gait by artificially restoring ankle functionality. Therefore, the biody-namic behaviour of the locked ankle joint and the enviroment have to be mimicked mechnically. After introducing Prosthesis-User-in-the-Loop simulator idea, the conception of a proof-of-concept simulator is presented. From this, an analytical model is derived and inverse dynamics simulation are used for design. The resulting mechanism is limited to sagittal plane movements and thus has three degrees of freedom. The actuators are dimensioned to meet the requirements of walking motions in the human subject with maximum body height among the test population.

This paper presents the design of the lightweight BioRob manipulator with spring-loaded tendon-driven actuation developed for safe physical human-robot interaction. The safety of the manipulator is analyzed by an analytical worst-case estimation of impact and clamping forces in the absence of collision detection. As intrinsic joint compliance can pose a threat by storing energy, a safety evaluation method is proposed taking the potential energy stored in the elastic actuation into account. The evaluation shows that the robot arm design constrains the worst case clamping forces to only 25 N, while being able to handle loads up to 2 kg, and inherits extremely low impact properties, such as an effective mass of less than 0.4 kg in non near-singular configurations, enabling safe operation even in case of high velocities. The results are validated in simulation and experiments.

Bio-inspired, musculoskeletal design of bipedal robots offers great potential towards more human-like robot performance but imposes major challenges on their design and control, as it is challenging to analyze the contribution of each active and passive series elastic tendon to the overall joint, leg and robot dynamics. In this paper, detailed mathematical models of the tendon-driven, series elastically actuated mono- and biarticular structures of the BioBiped1 robot are presented. These enable a systematic analysis of the design space and characteristic curves as well as to derive guidelines for the design of improved prototypes. The derived models are applied to investigate the effects of the active and passive, mono- and biarticular structures on different performance criteria of 1D hopping motions by means of a detailed multi-body system dynamics simulation.

This paper introduces a robotic concept for the assessment of psychological factors in prosthetic design. Its aim is to imitate the postural movements of the participants while those are conducting squatting movements in order to investigate the integration of artificial limbs to the subject's body scheme. Therefore, the robot mimics the functionality and appearance of the human foot, shank and thigh as well as the ankle and knee joint. To induce a more realistic outer appearance, the hull of a shop-window mannequin is used as cladding. The robot is controlled by a computed torque control combined with a RGB-D sensor for the acquisition of the desired trajectories from the participant. In the test setup one leg of the participant is hidden from his view while the robot stands next to him and imitates the movements of this leg. This paper gives an insight in the theory of body schema integration. The concept of the robot is described and detailed information about the mechanical design and actuator dimensioning in accordance with psychological and biomechanical requirements are given. Furthermore, the concept of the human-machine interface, the control algorithm and simulations based on experimental data from a human subject are presented.

Actuation with variable elasticity is considered a key property for the realization of human-like bipedal locomotion. Also, an intelligent and self-stable mechanical system is indispensable. While much effort of current research has been devoted to the development of variable impedance joint actuators, this paper deals with the important question of how to determine the actuation requirements of a compliant, musculoskeletal robot that is targeted at fast dynamic motions. In a step-by-step approach, design decisions for the elastic humanoid robot BioBiped1 are presented. Using multibody system dynamics models and simulations, incorporating bidirectional series elastic actuator models and a realistic ground contact model, we analyze the actuation requirements of the employed electrical motors for computer generated hopping and human data based running motions. The numerical simulation results are accompanied by videos of the dynamics simulations. Recent experiments on the real hardware have indicated that the selected motor-gear units and elastic transmissions support the desired dynamic motion goals.

Finding injured humans is one of the primary goals of any search and rescue operation. The aim of this paper is to address the task of automatically finding people lying on the ground in images taken from the on-board camera of an unmanned aerial vehicle (UAV). In this paper we evaluate various state-of-the-art visual people detection methods in the context of vision based victim detection from an UAV. The top performing approaches in this comparison are those that rely on flexible part-based representations and discriminatively trained part detectors. We discuss their strengths and weaknesses and demonstrate that by combining multiple models we can increase the reliability of the system. We also demonstrate that the detection performance can be substantially improved by integrating the height and pitch information provided by on-board sensors. Jointly these improvements allow us to significantly boost the detection performance over the current de-facto standard, which provides a substantial step towards making autonomous victim detection for UAVs practical.

Software for autonomous robots solving challenging tasks in research or application is becoming increasingly complex. System integration has to deal with various different functional components. To decouple those components from each other and to enable a modular and reuseable software architecture a robot middleware is typically used. But this intermediate layer introduces significant additional overhead during run-time. In this work a methodology is described to utilize specific application characteristics to improve communication efficiency between different robot software modules. By composing several components in a single thread memory copying or locking operations can be avoided, when data is exchanged between those parts. The optimization can be achieved without compromising the advantages of a communication layer. Still the modifications are transparent to the maybe already existing components. Experimental results in the scenario of autonomous soccer-playing humanoid robots are presented and exhibit remarkable reduction in communication overhead. Furthermore this approach can be implemented in or on-top of other communication layers.

In this paper we present a case study of cooperation of a strongly heterogeneous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some competing capabilities. By combining two strongly heterogeneous robots the diversity of accomplishable tasks increases as the variety of sensors and actuators in the robot systems is extended compared with a team consisting of homogeneous robots. The scenario describes a tightly cooperative task, where the humanoid robot and the wheeled robot follow for a long distance a ball, which is kicked finally by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots. For task distribution and allocation, a newly developed objective function is presented which is based on a proper modeling of the sensing, perception, motion and onboard computing capabilities of the cooperating robots. Aspects of reliability and fault tolerance are considered.

Methods for modeling, simulating and optimizing the dynamics, stability and performance of legged robot locomotion are discussed in this paper. It is demonstrated how these tools are used in the design, implementation and operation of a humanoid robot. The selection and integration of fundamental hard- and software needed for autonomous operation and high agility is presented for a recently developed fully-actuated 17 DoF humanoid. The results are additionally reported form simulations and gait optimizations completed during its development using a 3D dynamic biped model coupled with multiple physical and stability constraints.

This paper discusses the design concept and system development of a small and relatively fast walking, autonomous humanoid robot with 17 degrees-of-freedom (DoF). The selection of motor size and gear ratios is based on numerical optimization of detailed multibody dynamics and optimal control corresponding to fast steps of the robot with an envisioned target speed of more than 0.5 m/s. In this paper the design considerations based on numerical optimal control studies and the mechanical realization of the robot are presented including first investigations on the achievable performance of a decentralized, microcontroller-based control architecture.