This paper presents a novel approach for collision avoidance in optimal and model predictive control, in which the environment is represented by a large number of points and the robot as a union of padded polygons. The conditions that none of the points shall collide with the robot can be written in terms of an infinite number of constraints per obstacle point. We show that the resulting semi-infinite programming (SIP) optimal control problem (OCP) can be efficiently tackled through a combination of two methods: local reduction and an external active-set method. Specifically, this involves iteratively identifying the closest point obstacles, determining the lower-level distance minimizer among all feasible robot shape parameters, and solving the upper-level finitely-constrained subproblems. In addition, this paper addresses robust collision avoidance in the presence of ellipsoidal state uncertainties. Enforcing constraint satisfaction over all possible uncertainty realizations extends the dimension of constraint infiniteness. The infinitely many constraints arising from translational uncertainty are handled by local reduction together with the robot shape parameterization, while rotational uncertainty is addressed via a backoff reformulation. A controller implemented based on the proposed method is demonstrated on a real-world robot running at 20 Hz, enabling fast and collision-free navigation in tight spaces. An application to 3D collision avoidance is also demonstrated in simulation.

The trajectory planning for a fleet of automated guided vehicles (AGVs) on a roadmap is commonly referred to as the multi-agent path finding (MAPF) problem, the solution to which dictates each AGV's spatial and temporal location until it reaches its goal without collision. When executing MAPF plans in dynamic workspaces, AGVs can be frequently delayed, e.g., due to encounters with humans or third-party vehicles. If the remainder of the AGVs keeps following their individual plans, synchrony of the fleet is lost and some AGVs may pass through roadmap intersections in a different order than originally planned. Although this could reduce the cumulative route completion time of the AGVs, generally, a change in the original ordering can cause conflicts, such as deadlocks. In practice, synchrony is therefore often enforced by using a MAPF execution policy employing, e.g., an action dependency graph (ADG) to maintain ordering. To safely re-order without introducing deadlocks, we present the concept of the switchable action dependency graph (SADG). Using the SADG, we formulate a comparatively low-dimensional mixed-integer linear program that repeatedly re-orders AGVs in a recursively feasible manner, thus maintaining deadlock-free guarantees, while dynamically minimizing the cumulative route completion time of all AGVs. Various simulations validate the efficiency of our approach when compared to the original ADG method as well as robust MAPF solution approaches.

Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spatial and temporal features. In this work, we extend the LfD framework to address forceful manipulation skills, which are of great importance for industrial processes such as assembly. For such skills, multi-modal demonstrations including robot end-effector poses, force and torque readings, and operation scene are essential. Our objective is to reproduce such skills reliably according to the demonstrated pose and force profiles within different scenes. The proposed method combines our previous work on task-parameterized optimization and attractor-based impedance control. The learned skill model consists of (i) the attractor model that unifies the pose and force features, and (ii) the stiffness model that optimizes the stiffness for different stages of the skill. Furthermore, an online execution algorithm is proposed to adapt the skill execution to real-time observations of robot poses, measured forces, and changed scenes. We validate this method rigorously on a 7-DoF robot arm over several steps of an E-bike motor assembly process, which require different types of forceful interaction such as insertion, sliding and twisting.

This paper compares the vehicle dynamics performances of two approaches for automated lane change manoeuvres of a long vehicle combination in simulated highway driving. One of the two approaches is a non-linear model predictive controller (NMPC), and the other is based on driver model control (DMC) theory. Both approaches utilize traffic situation predictions that include motion variable constraints and actuation requests for steering, propulsion and braking. The two automated driving approaches are compared in a simulation environment including a high-fidelity vehicle plant model and models of surrounding vehicles. Simulations show that both approaches can generate feasible lane change manoeuvres at the constant speeds of 44 and 78 km/h. In addition, lane changes were successfully conducted in combination with retardation due to leading vehicle braking from 80 to 50 km/h with a varying retardation range of 0.1-0.7 g. In general, the non-linear model predictive control shows a shorter lane change duration and lower values of the used absolute magnitude of the longitudinal and lateral accelerations. However, the specific objective function used in the NMPC leads to an unnecessary variation of longitudinal vehicle speed compared to the driver model control approach.