Previous research has established that conscious stimuli can lead to non-conscious predictions. Yet, it also suggests that conscious awareness of stimuli is a necessary condition for using them in predictions. We use subliminal movement – with and without obstacles – to examine predictions from subliminal stimuli. In four experiments, a moving object was masked with continuous flash suppression...

A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed Evolutionary Artificial Potential Field approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for ...

We will present an algorithm to handle moving and deforming objects for free surface fluid simulations with the lattice Boltzmann method (LBM). To achieve this we extend methods available for flows without a free surface to enables simulations of moving objects with varying surface roughness, two-way coupled interaction and improved mass conservation. We furthermore show how to efficiently init...

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: a) SLAM in dynamic environments; b) Detection, characterization, and behavior prediction of the potential moving obstacles; and c) On-l...

In this paper, we address the issues related to navigation and control of a mobile robot which enable it to deal with unexpected moving obstacles by sensor-based control. We propose a probabilistic approach to deal with collision avoidance under uncertainty. We assume that the uncertainties both in the position of a moving obstacle and the error of the measurements is normally distributed. The ...

This work presents the experimental assessment of a hybrid control scheme based on Deep Reinforcement Learning (DRL) for obstacle avoidance in robot manipulators. More precisely, relying an equivalent Linear Parameter Varying (LPV) state-space representation system, two operative modes, one both joint positions and velocities, only velocity inputs, are activated depending measurement distance b...

This paper presents a deep reinforcement learning-based path planning algorithm for the multi-arm robot manipulator when there are both fixed and moving obstacles in workspace. Considering problem properties such as high dimensionality continuous action, proposed employs SAC (soft actor-critic). Moreover, order to predict explicitly future position of obstacle, LSTM (long short-term memory) is ...

This paper presents a method for redundancy resolution of an industrial manipulator in a teleoperated force control task A seven degree-of-freedom (DOF) industrial manipulator manufactured by the Mit-subishi Heavy Industries Ltd. is used for experiments. The task involves obeying a force command sent from a remote computer while autonomously adapting the posture to avoid unexpected obstacles mo...

Continuing the analysis of fracture size effect in Part I, which was focused on the maximum force in vertical penetration of ice, Part II tackles the problem maximum force that can be applied by a moving ice plate on an obstacle presented by a fixed structure. Based on an asymptotic approach, approximate solutions for are obtained for the size effects of ice thickness, effective structure diame...