Topics in Machining with Industrial Robot Manipulators and Optimal Motion Control

Two main topics are considered in this thesis: Machining with industrial robot manipulators and optimal motion control of robots and vehicles. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of the strong process forces required for the task. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, may significantly degrade the achieved machining accuracy of the manufactured part. In this thesis, a macro/micro-manipulator configuration is considered to the purpose of increasing the milling accuracy. In particular, a model-based control architecture is developed for control of the macro/micro-manipulator setup. The considered approach is validated by experimental results from extensive milling experiments in aluminium and steel. Related to the problem of high-accuracy milling is the topic of robot modeling. To this purpose, two different approaches are considered; modeling of the quasi-static joint dynamics and dynamic compliance modeling. The first problem is approached by an identification method for determining the joint stiffness and backlash. The second problem is approached by using gray-box identification based on subspace-identification methods. Both identification algorithms are evaluated experimentally. Finally, online state estimation is considered as a means to determine the workspace position and orientation of the robot tool. Kalman Filters and Rao-Blackwellized Particle Filters are employed to the purpose of sensor fusion of internal robot measurements and measurements from an inertial measurement unit for estimation of the desired states. The approaches considered are fully implemented and evaluated on experimental data. The second part of the thesis discusses optimal motion control applied to robot manipulators and road vehicles. A control architecture for online control of a robot manipulator in high-performance path tracking is developed, and the architecture is evaluated in extensive simulations. The main characteristic of the control strategy is that it combines coordinated feedback control along both the tangential and transversal directions of the path; this separation is achieved in the framework of natural coordinates. One motivation for research on optimal control of road vehicles in time-critical maneuvers is the desire to develop improved vehicle-safety systems. In this thesis, a method for solving optimal maneuvering problems using nonlinear optimization is discussed. More specifically, vehicle and tire modeling and the optimization formulations required to get useful solutions to these problems are investigated. The considered method is evaluated on different combinations of chassis and tire models, in maneuvers under different road conditions, and for investigation of optimal maneuvers in systems for electronic stability control. The obtained optimization results in simulations are evaluated and compared

Topics in Machining with Industrial Robots and Optimal Control of Vehicles

Two main topics are considered in this thesis: Machining with industrial robots and optimal control of road-vehicles in critical maneuvers. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of strong process forces affecting the robot end-effector. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, significantly degrade the achieved position accuracy of the machined part. In this thesis, a macro/micro manipulator configuration is considered to the purpose of increasing the position accuracy. In particular, a model-based control architecture is developed for control of the micro manipulator. The macro/micro manipulator configuration are validated by experimental results from milling tests in aluminium. The main result is that the proposed actuator configuration, combined with the control architecture proposed in this thesis, can be used for increasing the accuracy of industrial machining processes with robots. The interest for research on optimal control of road-vehicles in timecritical maneuvers is mainly driven by the desire to devise improved vehicle safety systems. Primarily, the solution of an optimal control problem for a specific cost function and model configuration can provide indication of performance limits as well as inspiration for control strategies in time-critical maneuvering situations. In this thesis, a methodology for solving this kind of problems is discussed. More specifically, vehicle and tire modeling and the optimization formulation required to get useful solutions to these problems are investigated. Simulation results are presented for different vehicle models, under varying road-surface conditions, in aggressive maneuvers, where in particular the tires are performing at their limits. The obtained results are evaluated and compared. The main conclusion here is that even simplified road-vehicle models are able to replicate behavior observed when experienced drivers are handling vehicles in time-critical maneuvers. Hence, it is plausible that the results presented in this thesis provide a basis for development of future optimization-based driver assistance technologies

Optimal Tracking and Identification of Paths for Industrial Robots

In many application areas in industrial production, industrial robots are utilised for performing various tasks. Frequently a predefined geometric path exists, such that the robot should track this path with its tool centre point. The tracking is often to be performed with certain criteria specified, such as minimisation of time or energy. Accordingly, path tracking problems can often conveniently be formulated as optimisation problems. This thesis concerns the problem of timeoptimal path tracking for industrial robots. The path tracking is experimentally evaluated on a robot from ABB of type IRB140 available in the Robotics Lab at the Department of Automatic Control, Lund University. In the thesis, mainly the optimisation software JModelica.org has been used for optimisation purposes. In cases where the path only is defined by a motion of a tool along a contour of an object, experimental methods are required in order to determine the corresponding geometric motion of the robot. In the thesis a contact-force control approach for determining of the joint positions along the desired path is considered. Further, in a timeoptimal path tracking, one control signal is saturated in every time instance. Consequently, the robustness to modelling errors and disturbances is low. In order to make the control more robust, an earlier developed control structure called path velocity controller is implemented and tested in the robot system. Both contact-force controlled path identification and optimal path tracking are evaluated in simulations and in experiments on the robot system

A Convex Approach to Path Tracking with Obstacle Avoidance for Pseudo-Omnidirectional Vehicles

This report addresses the related problems of trajectory generation and time-optimal path tracking with online obstacle avoidance. We consider the class of four-wheeled vehicles with independent steering and driving on each wheel, also referred to as pseudo-omnidirectional vehicles. Appropriate approximations of the dynamic model enable a convex reformulation of the path-tracking problem. Using the precomputed trajectories together with model predictive control that utilizes feedback from the estimated global pose, provides robustness to model uncertainty and disturbances. The considered approach also incorporates avoidance of a priori unknown moving obstacles by local online replanning. We verify the approach by successful execution on a pseudo-omnidirectional mobile robot, and compare it to an existing algorithm. The result is a significant decrease in the time for completing the desired path. In addition, the method allows a smooth velocity trajectory while avoiding intermittent stops in the path execution

Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.Comment: 15 page

Optimal Tracking and Identification of Paths for Industrial Robots

This paper presents results from time-optimal path tracking for industrial robots. More specifically, three subproblems are studied and experimentally evaluated. The first is a contact-force control approach for determining the geometric robot motion, such that the tool centre point of the robot is moved according to the specification. The second problem is off-line solution of the optimisation problem describing the time-optimal path tracking problem, by using software which allows highlevel implementation and solution of optimisation problems. The third problem is robust control of the robot during real-time path tracking based on the optimisation results determined off-line. An earlier developed control structure for robust control is implemented and tested in a robot system. This paper discusses the theory behind time-optimal path tracking and presents experimental results. Both contact-force controlled path identification and real-time path tracking of the identified path are evaluated on a 6-DOF industrial robot of type IRB140 from ABB

MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.Comment: Code: https://github.com/westny/mtp-g

Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Given their adaptability 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 ones, 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 significantly improve predictions.Comment: https://github.com/westny/mtp-g

Adaptive Internal Model Control for Mid-Ranging of Closed-Loop Systems with Internal Saturation

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, model-based 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

Robust Predictive Motion Planning by Learning Obstacle Uncertainty

Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this paper, an efficient, robust, and safe motion-planing algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming problem. The learned control set is used to compute forward reachable sets of obstacles that are less conservative than the worst-case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. The method is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness