Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain

Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this paper, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.Comment: https://www.youtube.com/watch?v=VzpRoEZeyWk https://cs.gmu.edu/~xiao/Research/Verti-Wheelers

Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms

Most conventional wheeled robots can only move in flat environments and simply divide their planar workspaces into free spaces and obstacles. Deeming obstacles as non-traversable significantly limits wheeled robots' mobility in real-world, extremely rugged, off-road environments, where part of the terrain (e.g., irregular boulders and fallen trees) will be treated as non-traversable obstacles. To improve wheeled mobility in those environments with vertically challenging terrain, we present two wheeled platforms with little hardware modification compared to conventional wheeled robots; we collect datasets of our wheeled robots crawling over previously non-traversable, vertically challenging terrain to facilitate data-driven mobility; we also present algorithms and their experimental results to show that conventional wheeled robots have previously unrealized potential of moving through vertically challenging terrain. We make our platforms, datasets, and algorithms publicly available to facilitate future research on wheeled mobility.Comment: https://www.youtube.com/watch?v=uk62ITBGoTI https://cs.gmu.edu/~xiao/Research/Verti-Wheelers

Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset

Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multi-modal robot perception data; we collect a large-scale (~50 km, 10 hours, 150 trials, 7 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases

Maintaining Power Relations in Supply Chain

Managing supply chain relations has evolved over a decade and many companies have given importance to regulate their relations in supply chain relations to stay competitive in the market. In this context of adjusting relations among supply chain members, central point of discussion is the role of power. Power can be a component that persuades one member of supply chain to do certain things that he/she wouldn’t agree on doing it voluntarily. The implication of that power among supply chain members is called as power relations. These power relations between the supply chain members need to be sustained under circumstances of whether the power is balanced or not balanced between the two actors. The key research questions are formulated as followed, What is the perspective of the supply chain members regarding to the role of power relations among supply chain actors? How do the cost, transparency, reliability and flexibility help to sustain the power relations in supply chain? In order to answer these questions, structured literature review was conducted. The conceptual model to sustain the supply chain relations included four main components that were cost, transparency, reliability and flexibility.  Interviews were conducted in three companies located in Sweden, Turkey and India. The company profiles regarding to power relations in this dyadic relationship were the main concern. The three cases tested were supplier dominancy, mutual dependency and subordinate buyer. In this thesis, we accomplished how supply chain members sustained their relations under the influence of power practices among supply chain members. We concluded our thesis study, showing the inter-connection in between these four elements to enable the sustainability of power relations. Moreover, we inferred that even though power seems to be a negative concept, the companies are able to maintain their power relations through awareness of existing power. In addition to that, the companies don’t give equal importance to each four elements though each element is present to maintain the power relations in their dyadic supply chain relationship