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In this paper, we present a holistic approach to enable mobile robots using video projection in a situation aware and dynamic way. Therefore, we show how to autonomously detect wall segments that are suitable to be used as projection target in a dynamic environment. We derive several quality measures to score the wall segments found in the local environment and show how these scores can be used by...
As robots make their way into our everyday lives, new behavioral concepts are needed to assure their acceptance as interaction partners. In the presence of humans, robots are required to take safety as well as human comfort into account. This paper presents a novel, planning-based approach for social robot navigation. It uses predicted human trajectories and a social cost function to plan collision-free...
In our previous work [1] we introduced the Anticipative Kinodynamic Planning (AKP): a robot navigation algorithm in dynamic urban environments that seeks to minimize its disruption to nearby pedestrians. In the present paper, we maintain all the advantages of the AKP, and we overcome the previous limitations by presenting novel contributions to our approach. Firstly, we present a multi-objective cost...
We introduce a task oriented optimal controller based on the finite horizon quadratic regulator (LQR). The task of being at a specified position with a specified velocity in specified time is formalized by cost functions. Moreover, we include soft constraints which are part of the cost function, such that the optimal control can be computed in fixed time. We show how soft constraints help to use redundancy...
Learning models of behaviours has many applications in robotics spanning both control, e.g. learning from demonstration and perception, e.g. monitoring and surveillance. Inverse reinforcement learning encodes behaviours as a reward function learned from a set of demonstrations. This paper addresses the problem of learning from unlabelled datasets containing an unknown number of behaviours in continuous...
Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning...
Members of a team are able to coordinate their actions by anticipating the intentions of others. Achieving such implicit coordination between humans and robots requires humans to be able to quickly and robustly predict the robot's intentions, i.e. the robot should demonstrate a behavior that is legible. Whereas previous work has sought to explicitly optimize the legibility of behavior, we investigate...
In this paper, we investigate the resilient cumulant game control problem for a cyber-physical system. The cyberphysical system is modeled as a linear hybrid stochastic system with full-state feedback. We are interested in 2-player cumulant Nash game for a linear Markovian system with quadratic cost function where the players optimize their system performance by shaping the distribution of their cost...
This paper applies game theory to multi-robot hunting target problem. A coordinated multi-robot hunting model based on extended cooperation game is established. And obstacle avoidance model and two kinds of target searching strategies--roaming search and regional search are proposed. Then an effective escape strategy for the target is also presented. Finally the whole process of the hunting task is...
New requirements of autonomous mobile vehicles necessitate hierarchical motion-planning techniques that not only find a plan to satisfy high-level specifications, but also guarantee that this plan is suitable for execution under vehicle dynamical constraints. In this context, the H-cost motion-planning technique has been reported in the recent literature. We propose an incremental motion-planning...
In this paper, an algorithm for switching time optimization for switched systems that exhibit discontinuities in the state trajectory on a switching is proposed. A derivative of the cost function with respect to the switching time is derived using a dynamic programming argumentation. Based on this derivative, a two-stage algorithm is described that alternates between solving an optimal control problem...
This paper presents a novel linear time-varying model predictive controller (LTV-MPC) using a sparse clothoid-based path description: a LTV-MPCC. Clothoids are used world-wide in road design since they allow smooth driving associated with low jerk values. The formulation of the MPC controller is based on the fact that the path of a vehicle traveling at low speeds defines a segment of clothoids if...
In this paper, a model predictive control approach is proposed for a vehicle convoy traveling on homogeneous highway. Specifically, the lane change behavior of a cut-in vehicle in front of the vehicle convoy is estimated and predicted for the predictive control of the leading vehicle in the convoy. In order to capture state differences of the preceding vehicle in different lane change manners, a cost...
A method is presented that approximates the solution to a nonlinear optimal control problem with quadratic cost function.We assume that the nonlinear system is accurately represented by a high-fidelity (hf) model which can be of high complexity or even of “black-box” type. The hf-model is oftentimes unsuitable for solving the optimal control problem. The proposed solution method is based on an Iterative...
The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes mellitus is considered. The contribution of this paper is to propose a velocity-weighting mechanism, within an MPC problem's cost function, that facilitates penalizing predicted hyperglycemic blood-glucose excursions based on the predicted blood-glucose levels'...
Dual control frameworks for systems subject to uncertainties aim at simultaneously learning the unknown parameters while controlling the system dynamics. We propose a robust dual model predictive control algorithm for systems with bounded uncertainty with application to soft landing control. The algorithm exploits a robust control invariant set to guarantee constraint enforcement in spite of the uncertainty,...
In this paper, a framework of human-robot shared control is developed based on finding the solution to an optimization problem. Human dynamics are taken into account in the analysis of the coupled human-robot system, and objectives of both human and robot are considered. Approximate dynamic programming is employed to solve the optimization problem in the presence of unknown human and robot dynamics...
Motion planning for the autonomous bicycles offers considerable challenges to the area of robotics due to the platform's nonholonomic, underactuated and nonminimum-phase properties. Instability and nontrivial dynamic coupling make the motion planning of the bicycles a rather complex task. In this paper, the motion planning for the autonomous bicycle is formulated as a dynamic constrained optimization...
We present a framework for improving adaptability of Learning from Demonstration (LfD) strategy by combining the LfD and Sequential Quadratic Programming (SQP). The advantage of the LfD method is that it can find a motion planning solution that is suitable to a task in a short time. Although the method successfully generates a motion when a query point is similar to learned trajectories, it has a...
Linear direct feed drives are widely used in high performance equipment and machine tools, but the abrupt counter force to the machine frame from the secondary part of the linear motor induces possible residue vibration. The jerk decoupling cartridge (JDC) provides a buffer to reduce such an impact. However, no existing JDC design considers the factors of closed-loop control together with mechanical...
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