Refine
Year of publication
Document Type
- Conference Proceeding (35)
- Article (8)
- Part of a Book (2)
Language
- English (45) (remove)
Keywords
- Automation (1)
- Autonomous mobile robots (1)
- Benchmark (1)
- Computational modeling (1)
- Control (1)
- GPU (1)
- Heuristic algorithms (1)
- Industry 4.0 (1)
- Lidar (1)
- Mpc (1)
- Multi-robot systems (1)
- Navigation (1)
- Path-following (1)
- RoboCup (1)
- Self-driving (1)
- Smart factory (1)
- autonomous driving (1)
- do-it-yourself (1)
- education (1)
- embedded hardware (1)
Institute
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (45) (remove)
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.