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Energy-Aware Path Planning for Autonomous Mobile Robot Navigation

AAAI Conferences

Battery life is yet one of the main limiting factors to a robot's total mission time, and efficient energy management is paramount in a robotic application. In this paper, we integrate energy awareness in the path planning of a mobile robot performing autonomous navigation. Our contributions are: 1) The formalization of a planning domain for mobile robot path planning which accounts for energy consumption and integrates energy actions in the generated plans; 2) A proof of concept of automatic path planning that avoids high energy areas in a known environment. We test our approach in simulation, extending an embedded computer's total battery discharge time by approximately 42.8%, and in a real ground mobile robot, achieving a mean energy draw reduction of 52.02%, both compared to conventional path planning.


MengeROS: A Crowd Simulation Tool for Autonomous Robot Navigation

AAAI Conferences

While effective navigation in large, crowded environments is essential for an autonomous robot, preliminary testing of algorithms to support it requires simulation across a broad range of crowd scenarios. Most available simulation tools provide either realistic crowds without robots or realistic robots without realistic crowds. This paper introduces MengeROS, a 2-D simulator that realistically integrates multiple robots and crowds. MengeROS provides a broad range of settings in which to test the capabilities and performance of navigation algorithms designed for large crowded environments.


Your average car is a lot more code-driven than you think

USATODAY - Tech Top Stories

The growing interest in smart and connected cars is getting tangible, both for car buyers and car makers, just as they get more complicated -- a lot more complicated. In fact, to really appreciate both the value of the car-related technologies that are being offered now, as well as what's possible in the not-to-distant future, it helps to have a basic understanding of how modern cars function. At their core, today's cars consist of an extremely complex set of subsystems connected across a labyrinth of different digital connection buses, a type of interface, with names like the CAN bus, FlexRay, LIN bus and even Ethernet (yes, that Ethernet). Each of these operate at different speeds, carry different types of data, and enable connections across different parts of the car. Conceptually, it's not altogether different from how all the different components in a smartphone or PC are connected and work together.


SpotMini Autonomous Navigation

#artificialintelligence

SpotMini autonomously navigates a specified route through an office and lab facility. Before the test, the robot is manually driven through the space so it can build a map of the space using visual data from cameras mounted on the front, back and sides of the robot. During the autonomous run, SpotMini uses data from the cameras to localize itself in the map and to detect and avoid obstacles. Once the operator presses'GO' at the beginning of the video, the robot is on its own. Total walk time for this route is just over 6 minutes.


Shaojie Shen: Minimalist Visual Perception and Navigation for Consumer Drones CMU RI Seminar

Robohub

Abstract: "Consumer drone developers often face the challenge of achieving safe autonomous navigation under very tight size, weight, power, and cost constraints. In this talk, I will present our recent results towards a minimalist, but complete perception and navigation solution utilizing only a low-cost monocular visual-inertial sensor suite. I will start with an introduction of VINS-Mono, a robust state estimation solution packed with multiple features for easy deployment, such as online spatial and temporal inter-sensor calibration, loop closure, and map reuse. I will then describe efficient monocular dense mapping solutions utilizing efficient map representation, parallel computing, and deep learning techniques for real-time reconstruction of the environment. The perception system is completed by a geometric-based method for estimating full 6-DoF poses of arbitrary rigid dynamic objects using only one camera. With this real-time perception capability, trajectory planning and replanning methods with optimal time allocation are proposed to close the perception-action loop. The performance of the overall system is demonstrated via autonomous navigation in unknown complex environments, as well as aggressive drone racing in a teach-and-repeat setting."