Refine
Year of publication
Document Type
- Conference Proceeding (32)
- Article (9)
- Part of a Book (3)
Has Fulltext
- no (44) (remove)
Keywords
- Autonomous mobile robots (2)
- Industry 4.0 (2)
- Multi-robot systems (2)
- Smart factory (2)
- Automation (1)
- Benchmark (1)
- Computational modeling (1)
- Control (1)
- Cyber-physical systems (1)
- GPU (1)
- Heuristic algorithms (1)
- Lidar (1)
- Mpc (1)
- Navigation (1)
- Path-following (1)
- RoboCup (1)
- Self-driving (1)
- autonomous driving (1)
- do-it-yourself (1)
- education (1)
Institute
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (44) (remove)
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
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.