We present hardware, perception, and planning tools that enable a modular robot to autonomously deploy passive structures to reach otherwise-inaccessible regions of the environment to accomplish high-level tasks. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the identified features to expand the reachable space of the robot. A high-level planner reasons about the high-level task, robot locomotion capabilities, and the environment to decide if and where to augment the environment in order to perform the desired task. These autonomous, perception-driven augmentation tools extend the adaptive capabilities of modular robot systems.
@cite_12 present a system in which a mobile robot manipulates specially designed cubes to build functional structures. The robot explores an unknown environment, performing 2D SLAM and visually recognizing blocks and gaps in the ground. Blocks are pushed into gaps to create bridges to previously inaccessible areas. In a real but contrived experimental design'' @cite_12 , a robot is tasked with building a three-block tower, and autonomously uses two blocks to build a bridge to a region with three blocks, retrieving them to complete its task. Where the Magnenat system is limited to manipulating blocks in a specifically designed environment, our work presents hardware, perception, and high-level planning tools that are more general, providing the ability to complete high-level tasks involving locomotion and manipulation in realistic human environments.
@cite_12
2421882796 2283534560 1584484540 2054245952
For mobile robots to operate in compliance with human presence, interpreting the impact of human activities and responding constructively is a challenging goal. In this paper, we propose a generative approach for enhancing robot mapping and mobility in the presence of humans through a joint, probabilistic treatment of static and dynamic characteristics of indoor environments. Human spatial activity is explicitly exploited for the purpose of passage detection and space occupancy prediction while effectively discarding false positive human detections using prior map information. In turn, this allows the execution of plan trajectories within unexplored areas by using human presence for resolving the uncertainty or ambiguity that is due to dynamic events. A series of experiments with an indoor robot navigating in close human proximity within a multi-floor building demonstrate the effectiveness of our approach in realistic conditions. We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state. We present an algorithm that enables a humanoid robot to reason about its environment and use the available objects to build bridges, stairs and lever-fulcrum systems. Facing a challenge that is otherwise intractable, such as climbing a height or moving a heavy object, the proposed planner reasons about the physical limitations of the robot to design functional structures. Inducing constraints on the space of possible designs within a classical planning framework, the algorithm outputs feasible structures that can be used towards accomplishing goals. We present results in dynamic simulation with Golem Hubo, walking on a bridge to cross a hazardous area, and in real-world with Golem Krang, overturning 100 kg loads and pushing 240 kg obstacles. Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework.