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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. [ABSTRACT FROM AUTHOR]

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. Note to Practitioners—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. [ABSTRACT FROM AUTHOR]

We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao–Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling. [ABSTRACT FROM PUBLISHER]