Autonomous Exploration Development Environment is an open-source repository released to facilitate development of high-level planning algorithms and integration of com-plete autonomous navigation systems. The repository contains representative simulation environment models, fundamental navigation modules, e.g., local planner, terrain traversability analysis, waypoint following, and visualization tools. Together with two of our high-level planner releases - TARE planner for exploration and FAR planner for route planning, we detail usage of the three open-source repositories and share experiences in integration of autonomous navigation systems. We use DARPA Subterranean Challenge as a use case where the repositories together form the main navigation system of the CMU-OSU Team. In the end, we discuss a few potential use cases in extended applications.

Path planning in unknown environments remains a challenging problem, as the environment is gradually observed during the navigation, the underlying planner has to update the environment representation and replan, promptly and constantly, to account for the new observations. In this paper, we present a visibility graph-based planning framework capable of dealing with navigation tasks in both known and unknown environments. The planner employs a polygonal representation of the environment and constructs the representation by extracting edge points around obstacles to form enclosed polygons. With that, the method dynamically updates a global visibility graph using a two-layered data structure, expanding the visibility edges along with the navigation, and removing edges that become occluded by newly observed obstacles. When navigating in unknown environments, the method is attemptable in discovering a way to the goal by picking up the environment layout on the fly, updating the visibility graph, and fast replanning corresponding to the newly observed environment. We evaluate the method in simulated and real-world settings. The method shows the capability to attempt and navigate through unknown environments, reducing travel time by up to 12-47% from search-based methods: A*, D* Lite, and more than 24-35% from sampling-based methods: RRT*, BIT*, and SPARS.

Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward, including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this article constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.

This paper describes a novel framework for autonomous exploration in large and complex environments. We show that the framework is efficient as a result of its hierarchical structure, where at one level it maintains a sparse representation of the environment and at another level, a dense representation is used within a local planning horizon around the robot. The exploration path is computed at the two levels, coarsely at the global scale and finely around the robot. Such a framework produces detailed paths in the vicinity of the robot, while trades off data resolution far away from the robot for computational efficiency. In experiments, we evaluate our method with a real robot exploring large and complex indoor and outdoor environments. Results show that our method is twice as efficient in covering spaces while using less than one-fifth of processing in comparison to state-of-the-art methods.

We present a method for efficiently exploring highly convoluted environments. The method incorporates two planning stages - an exploration stage for extending the boundary of the map, and a relocation stage for explicitly transiting the robot to different sub-areas in the environment. The exploration stage develops a local Rapidly-exploring Random Tree (RRT) in the free space of the environment, and the relocation stage maintains a global graph through the mapped environment, both are dynamically expanded over replanning steps. The method is compared to existing state-of-the-art methods in various challenging simulation and real environments. Experiment comparisons show that our method is twice as efficient in exploring spaces using less processing than the existing methods. Further, we release a benchmark environment to evaluate exploration algorithms as well as facilitate development of autonomous navigation systems. The benchmark environment and our method are open-sourced.