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This paper introduces four new collision avoidance systems for a remotely-operated helicopter using haptic feedback on the control stick of the operator. To calculate the feedback force artificial risk fields are used, which give an indication about the risk of hitting an obstacle. For each obstacle in the risk field a risk value is calculated, which is transformed to a risk vector, acting as an artificial...
We present a framework for improving conflict detection algorithms using a hybrid control paradigm. This allows us to separate the problem into two parts: state/mode estimation and threat prediction. Since the dynamic equations for a conflict can change discretely depending on the situation, we propose the use of multiple model (MM) estimators to predict the situation and ultimately improve threat...
Visual cues that are relevant to safe car driving are generally not supported by redundant non-visual cues, resulting in increased accident risk when visual attention is misallocated. Haptic gas pedal feedback, also referred to as longitudinal impedance, may assist drivers in proper and timely reallocation of their visual attention. A solution is presented for translating the relevant two-dimensional...
A novel model of organized neural network is shown to be very effective for path planning and obstacle avoidance in an unknown map which is represented by topologically ordered neurons. With the limited information of neighbor position and distance of the target position, the robot autonomously provides a proper path with free-collision and no redundant exploring in the process of exploring. Finally,...
In this article, we present a learning model that can control a simulated anthropomorphic arm kinematics motion in order to reach and grasp a static prototypic object placed behind an obstacle of varying position and size. The network, composed of two generic neural network modules, learns to combine multi-modal arm-related information such as trajectory parameters as well as obstacle-related information...
This paper presents a method for automatically generating a 2D obstacle map of an environment using a robot or team of robots. The algorithm is adaptable to a variety of robotic platforms and presents an iterative solution for determining the map. Its primary contributions to the field of autonomous mapping include the abstraction of specific robot hardware from the mapping algorithm and the integration...
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