In this paper, we propose a virtual reality (VR) platform as a case study of machine learning, in this case applied to the goal representation heuristic dynamic programming (GrHDP) approach. In general, a VR platform normally includes a physical module, a control/learning module, and a VR module. It facilitates machine learning research, where scientists and engineers can participate in the sim...

In this article, we present a hybrid feedback approach to solve the navigation problem in $n$ -dimensional space containing an arbitrary number of ellipsoidal obstacles. The proposed algorithm guarantees both global asymptotic stabiliza...

We present work on a robot system capable of rapidly learning to avoid obstacles in previously unseen environments. We exploit deep learning’s generalized features to reason about when to turn left, turn right, or drive straight within an indoor environment. We present preliminary results of a deep convolutional neural network trained without any pre-training that can successfully avoid obstacles.

A new real-time obstacle avoidance approach for mobile robots has been developed and implemented. This approach permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advancing toward the target. The novelty of this approach, entitled the Virtual Force Field, lies in the integration of two known concepts: Certainty Grids for obsta...

Based on a non-linear single track model which describes the dynamics of vehicle, an optimal path planning strategy is developed. Real time optimization is used to generate reference control values to allow leading the vehicle alongside a calculated lane which is optimal for different objectives such as energy consumption, run time, safety or comfort characteristics. Strict mathematic formulati...

This paper presents a method for motion planning in dynamic environments, subject to robot dynamics and ac-tuator constraints. The time optimal trajectory is computed by rst generating an initial guess using the concept of velocity obstacle. The initial guess, computed by a global search over a tree of avoidance maneuvers, is then optimized using a dynamic optimization. This method is applicabl...

In this paper, we provide a dynamic graph theoretical framework for flocking in presence of multiple obstacles. In particular, we give formal definitions of nets and flocks as spatially induced graphs. We provide models of nets and flocks and discuss the realization/embedding issues related to structural nets and flocks. This allows task representation and execution for a network of agents call...

This paper describes a laser ranging sensor that is suitable for applications like small unmanned aerial vehicles. The hardware consists of a diode emitter array and line-scan charge coupled devices. A structured-light technique measures ranges up to 30 meters for 64 field angles in a 90 degree field of view. Operation is eye safe, and the laser wavelength is not visible to night vision goggles...

The Stanford AI Lab cart is a remotely controlled TV equipped mobile robot. A computer program has driven the cart through cluttered spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system. The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion. It plans an obstacle avoiding path to a desired destination on...