**ABSTRACT**

In multirobot path planning, optimizing each robot’s position is important. Machine learning models, such as DARP, are used to optimize these paths and predict future paths given a set of starting positions and obstacles. This ensures complete coverage of the given maps. This paper explores the use of DARP to optimize the initialization and exploration of multirobot systems, and output the best possible initial positions for a set of robots, given a map and its set of obstacles. A dataset, **REIOset**, of 4000 elements containing, randomized maps, initial values, partition standard deviation, and  co-visibility was created. A convolutional neural network (CNN) is then used to train the generated map and position data, to output optimized initial positions, trained on image and parameter data. Despite inconsistent predictions from our model, an improved version of this algorithm can be utilized for purposes such as robot swarm formation, other collaborative multi robot systems, or relative pose prediction.

Keywords: mCPP, DARP, Convolutional Neural Networks, Path Planning, Co-visibility

[Rest of the paper](https://www.are.na/block/20410274)

Robotic Exploration, Initialization, Optimization: leveraging multi-robot exploration data to determine the initial position of robots

**Abstract**

This work presents a novel ultrasonic haptic interface to improve nonvisual perception and situational awareness in fully autonomous vehicles. User study results (n=14) suggest comparable performance with the dynamic ultrasonic stimuli versus a control using static embossed stimuli. The utility of the ultrasonic interface coupled with gestural control is demonstrated with an autonomous small-scale robot vehicle on a simplified grid of intersections. These efforts support the application of ultrasonic haptics for improving nonvisual information access in autonomous transportation with strong implications for inclusive design, usability, and human-in-the-loop decision making

[Paper](https://dl.acm.org/doi/10.1145/3568162.3576975)

Expanded Situational Awareness Without Vision: A Novel Haptic Interface for Use in Fully Autonomous Vehicles