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In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the...
This paper presents the design, implementation, and comparative analysis of two intelligent neural network based controllers employed for nonlinear dynamic compensation and adaptive trajectory tracking of a mobile robot system. The first control law is an integration of a backstepping controller with a neural network which is designed to learn the inverse dynamic model of the robot and to compensate...
This paper provides a different method for human-robot interaction and further encourages communication between human users and their robotic counterparts. The focus of our work is to develop a human-robot communication system that is not easily detectable and increases stealth when necessary. The human-robot interaction system we propose involves hand signals. Hand gestures are a common modality...
A range of different value systems have been proposed for self-motivated agents, including biologically and cognitively inspired approaches. Likewise, these value systems have been integrated with different behavioral systems including reflexive architectures, reward-based learning and supervised learning. However, there is little literature comparing the performance of different value systems for...
We describe the architecture of a hierarchical learning-based controller for bots in the First-Person Shooter (FPS) game Unreal Tournament 2004. The controller is inspired by the subsumption architecture commonly used in behaviour-based robotics. A behaviour selector decides which of three sub-controllers gets to control the bot at each time step. Each controller is implemented as a recurrent neural...
Neuromodulation is thought to be one of the underlying principles of learning and memory in biological neural networks. Recent experiments have shown that neuroevolutionary methods benefit from neuromodulation in simple grid-world problems. In this paper we investigate the performance of a neuroevolutionary method applied to a more realistic robotic task. While confirming the favorable effect of neuromodulatory...
Shape recognition is an important part of machine intelligence in both decision making and data processing. A good shape representation in shape recognition should describe the shape in the way that makes it distinguishable from other shapes and be invariant to transform of position, size, angle and skew. More importantly, developing and finding appropriate shape representation are still a challenging...
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