Enabling intelligent energy management for robots using publicly available maps

Abstract

Energy consumption represents one of the most basic constraints for mobile robot autonomy. We propose a new framework to predict energy consumption using information extracted from publicly available maps. This method avoids having to model internal robot configurations, which are often unavailable, while still providing invaluable predictions for both explored and unexplored trajectories. Our approach uses a heteroscedastic Gaussian Process to model the power consumption, which explicitly accounts for variance due to exogenous latent factors such as traffic and weather conditions. We evaluate our framework on 30km of data collected from a city centre environment with a mobile robot travelling on pedestrian walkways. Results across five different test routes show an average difference between predicted and measured power consumption of 3.3%, leading to an average error of 6.6% on predictions of energy consumption. The distinct advantage of our model is our ability to predict measurement variance. The variance predictions improved by 84.3% over a benchmark.

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

“…Embedded machine learning is casually adopted in smartphones ( [6,7]), CCTV cameras (e.g., Reference [8]) and robots (e.g., Reference [9]). The concept of hierarchical classification itself has been proposed for sound classification in the context of smartphones [10] and smart vehicles [11].…”

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

“…The ability of management is mostly employed in combination with perception and navigation-typically scenarios involving more than one robot, e.g. such as monitoring spaces for security or traffic management purposes [5,40,51,60,61,110,112]. Figure 4 shows the robot autonomy level in the analyzed works.…”

“…[22,40,65,90,110]). This reveals that providing a more semantic representation of the data exchanged is still of little emphasis in RCI, as works tend to focus on building ad-hoc cyber-physical systems [5,10,26,83], where robots are often seen as mobile sensors rather than knowledge producers [60,62,79,81]. This also conforms with the fact that robotics lacks of established standards and best practices for data representation and exchange, while these have been proven to be fundamental in promoting interoperability in applications involving heterogeneous systems (e.g.…”

“…1). Other examples include cities deploying autonomous cars to enhance urban mobility, 3 drone deliveries employed for commercial or first-aid emergency scenarios, 4 and social robots operating in public and commercial spaces in many guises (waiters, policemen, receptionists 5 ).…”

Energy Modeling and Power Measurement for Three-Wheeled Omnidirectional Mobile Robots for Path Planning

“…MRs reduce energy consumption by finding the shortest path [26]. Bartlett et al proposed a probabilistic, data-driven approach to estimating the energy consumption of a mobile robot on a set of trajectories, whether they have been traversed or not [27]. Sun et al [28] take into account friction and gravity in finding energy-efficient paths across a terrain.…”