This paper addresses the path-tracking problem of time-optimal trajectories under model uncertainties, by proposing a real-time predictive scaling algorithm. The algorithm is formulated as a convex optimization problem, designed to balance the trade-off between improving feasibility and time optimality of a trajectory. The predicted trajectory is scaled based on the presence of path segments that are particularly sensitive to model uncertainties within the prediction horizon. Numerical simulations and experiments demonstrate that the proposed scaling algorithm reduces the path traversal time, while preserving similar path-tracking accuracy compared to an existing non-predictive method.

The availability of high-quality datasets is crucial for developing behavior prediction algorithms in autonomous vehicles. This paper highlights the need to standardize the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualization, and evaluation in the form of an open-sourced toolbox designed for researchers working on trajectory prediction problems. The clear specification of necessary preprocessing steps and evaluation metrics is intended to alleviate development efforts and facilitate the comparison of results across different studies. The toolbox is available at: https://github.com/westny/dronalize.

Human-robot collaboration (HRC) in manufacturing environments requires that physical safety can be guaranteed. Control methods that implicitly regulate the interaction forces between a controlled robot and its environment, such as impedance control, are often used for safety in HRC. However, these methods could be complemented by restricting the robot operational space for additional safety guarantees. In this context, obstacle avoidance might benefit from considering a prediction of the controlled-robot motion and/or the behavior of the human collaborator. To this end, we proposed to include linearized Safety Control Barrier Functions (SCBFs) in a linear Model Predictive Control (MPC) strategy for robot impedance variation online. The convex optimization problem that was obtained from our proposal presented two advantages compared to nonlinear MPC alternatives. First, optimality was ensured in our method under linearity assumptions on human guidance and linearized robot dynamics, whereas a controller synthesized by nonlinear MPC strategies would depend on the fundamental characteristics of the problem. Second, our method enabled implementation at a faster control frequency, thus allowing a rapid adaptation to changes occurring in the robot environment. Finally, experimental validation was performed using a Franka Emika Panda robot in a human-robot collaborative scenario, and the stability of the method was shown using Lyapunov theory. Note to Practitioners—Modern-day industrial manufacturing environments are characterized by collaboration between human operators and robot manipulators. In this scenario, where humans and robots share workspace, physical safety is required. This research aims to improve safety in human-robot collaboration by proposing a novel robot control strategy. In our approach, the interaction forces between the controlled robot and its environment were regulated implicitly using impedance control, to allow, among other interactions, that an operator could manually guide the robot. Then, obstacle avoidance was included to modify the robot impedance behavior for restricting undesired collisions with, for example, the operator head, while ensuring stability of the method. Our main contribution is that the proposed formulation allows to consider a prediction of the robot motion and/or the operator behavior for robot obstacle avoidance. It was shown in experiments with a real robot that adding prediction capabilities reduced the risk of undesired collisions, while also decreasing the robot trajectory error. Moreover, the method could be implemented at a fast rate so that the robot could react rapidly to changes in its environment. Also, this implementation allowed to achieve a minimal variation with respect to the nominal impedance behavior of the robot. To conclude, this method is intended for scenarios where a robot is required to interact with its, possible restricted, environment: for example, a robot with a drill attached to its end-effector that is being guided to modify its trajectory, but where the operator should not be allowed to accidentally be harmed; or a robot performing a polishing task where a section of the polished object should remain unpolished. Therefore, using the proposed robot control strategy, a possible extension of this research would be to provide an improved prediction of the human operator intention depending on the desired robotic task and the role of the operator.

In this paper, an iterative learning strategy was developed to improve trajectory tracking for an impedance-controlled robot manipulator. In this learning strategy, an update law was proposed to modify the Cartesian reference of an impedance controller. Also, the conditions that ensure its convergence considering the dynamics of the robot were derived. Finally, an experimental evaluation was performed using a Franka Emika Panda robot in two different robot tasks, and its results showed that robot task completion was achieved in a lower number of iterations, while maintaining a smooth physical interaction between the robot and its surroundings.

Optimization of vehicle maneuvers using dynamic models in constrained spaces is challenging. Homotopic optimization, which has shown success for vehicle maneuvers with kinematic models, is studied in the case where the vehicle model is governed by dynamic equations considering road-tire interactions. This method involves a sequence of optimization problems that start with a large free space. By iteration, this space is progressively made smaller until the target problem is reached. The method uses a homotopy index to iterate the sequence of optimizations, and the method is verified by solving challenging maneuvering problems with different road surfaces and entry velocities using a double-track vehicle dynamics model. The main takeaway is that homotopic optimization is also efficient for dynamic vehicle models at the limit of road-tire friction, and it demonstrates capabilities in solving demanding maneuvering problems compared with alternative methods like stepwise initialization and driver model-based initialization.

An integration of distributionally robust risk allocation into sampling-based motion planning algorithms for robots operating in uncertain environments is proposed. We perform non-uniform risk allocation by decomposing the distributionally robust joint risk constraints defined over the entire planning horizon into individual risk constraints given the total risk budget. Specifically, the deterministic tightening defined using the individual risk constraints is leveraged to define our proposed exact risk allocation procedure. Embedding the risk allocation technique into sampling-based motion planning algorithms realises guaranteed conservative, yet increasingly more risk-feasible trajectories for efficient state-space exploration.

In this paper, Safety Control Barrier Functions (SCBFs) were used to adjust the null-space compliant behavior of a redundant robot to improve safety in Human-Robot Collaboration (HRC) without modifying the robot behavior with respect to its main Cartesian task. A Lyapunov function was included in an energy storage formulation compatible with strict passivity to provide global asymptotic stability guarantees for the null-space compliance variation, and the necessary conditions for stability were formulated as inequality constraints of the optimization problem used for the null-space compliance variation. Experimental validation was performed using a Franka Emika Panda robot for a collaborative assembly application and its results showed that safety can be improved by using SCBFs simultaneously to the optimization of the robot configuration, while employing a single degree of freedom.

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can greatly improve predictions.

Collaborative robots have been designed to per-form tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot’s environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot–environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers, are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated external forces, whereas external force direction and sense relative to the robot’s motion is used to classify its source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators.

Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by extending previous research that was performed on a 6-DOF robot. We have also used null-space motion to ensure a continuous movement of all joints during the entire trajectory execution as an approach to avoid joint stiction and allow accurate kinesthetic teaching. As is conventional for collaborative and industrial robots, the Panda robot is equipped with an internal controller, which allows to send position and velocity references directly to the robot. Therefore, null-space motion can be added directly to the MPC-generated velocity references. The observed trajectory deviation caused by discretization approximations of the Jacobian matrix when implementing null-space motion has been corrected experimentally using sensor feedback for the real-time velocity-reference recalculation and by performing a fast sampling of the null-space vector. Null-space motion has been experimentally seen to contribute to reducing the friction torque dispersion present in static joints.

Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and reduced degree of injuries in accidents for new, not yet existing systems, a framework has been developed by combining available historic data, in the form of crash databases, and statistical methods with comparative calculations of vehicle behavior using numerical optimization rather than simulation. The framework performs effectively, it gives interesting insights into the relation between more traditional active yaw control and optimal autonomous lane-keeping control, and it clearly demonstrates the potential of saved lives by using autonomous vehicle maneuvers.

The problem of planning a trajectory for robots starting in an initial state and reaching a final state in a desired interval of time is tackled. We propose an approach based on model predictive control to solve the problem of point-to-point trajectory generation for a given final time. We discuss various choices of models, objective functions, and constraints for generating trajectories to transfer the state of the robot, while respecting physical limitations on the motion as well as fulfilling computational real-time requirements. Extensive simulation results illustrate the use of the approach, and experiments on an industrial robot in a challenging ball-catching task show the effectiveness of the approach also in demanding scenarios with real-time constraints on the computation. This paper was motivated by the problem of generating movements to transfer a robot from its current state to a new position and velocity at a certain time, when the target state and the final time may require correction at a high rate. For example, for picking small objects from a conveyor belt with a variable feed rate, an off-line planning would fail, since the motion has to be adjusted as soon as the speed is changed. Under the assumption that the desired pickup position and velocity and arrival time can be predicted, the approach in this paper is applicable. The movements can be optimized, for example, for energy efficiency or for reduction of vibrations in the robot. We discuss how to mathematically express the desired performance criteria and other requirements on the motion, such as not violating a maximum joint speed. Quick reactions to sensor inputs are computationally demanding. Thus, we limit ourselves to a class of motion-generation problems that lends itself to numerical optimization. Additionally, we save computation power by gradually refining the motion.

This paper considers the problem of performing mid-ranging control of two closed-loop controlled systems that have internal saturations. The problem originates from previous work in machining with industrial robots, where an external compensation mechanism is used to compensate for position errors. Because of the limited workspace and the considerably higher bandwidth of the compensator, a mid-ranging control approach is proposed. An adaptive, modelbased solution is presented, which is verified through simulations and experiments, where a close correspondence of the obtained results is achieved. Comparing the IAE of experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained.

This paper presents a method for modeling and control of a piezo-actuated high-dynamic compensation mechanism for usage together with an industrial robot during a machining operation, such as milling in aluminium. The machining spindle was attached to the compensation mechanism and the robot held the workpiece. Due to the inherent resonant character of mechanical constructions of this type, and the nonlinear phenomena appearing in piezo actuators, control of the compensation mechanism is a challenging problem. This paper presents models of the construction, experimentally identified using subspace-based identification methods. A subsequent control scheme, based on the identified models, utilizing state feedback for controlling the position of the spindle is outlined. Results from experiments performed on a prototype of the compensation mechanism are also provided.