This paper presents a novel multi-robot coverage path planning (CPP) algorithm -aka SCoPP -that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., nofly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.
This paper presents the conceptual design and fabrication/assembly of an autonomous solar powered small unmanned ground vehicle (UGV) platform for operation in outdoor environments. The contribution lies in the ability of the proposed design to offer uninterrupted operation in terms of endurance, to facilitate educational and research applications that are otherwise challenging to perform with a typical UGV (that needs significant downtime for recharging). A high incident area for solar PV panels is required to be able to support the complete energy needs of a ∼ 46 lb UGV (i.e., fully recharge the suitably sized battery powering the UGV). This makes it challenging to develop a stable platform that can carry solar panels much larger than the surface area of the platform itself (an aspect receiving minimal attention in other similar purpose platforms). To address this challenge, a novel umbrella-like folding mechanism is conceived, designed and successfully incorporated in the baseline prototype. This mechanism allows incorporating a remarkable ∼1 sq.m of incident solar PV with a net rated capacity of 200 W, one that remains folded to facilitate mobility, and can open/unfold to different extents for energy capture when needed. At the same time, the proposed design facilitates static and dynamic stability in spite of the significant solar PV incorporation. With the reference of the baseline prototype, an optimization approach is taken to develop a conceptual design of the next generation of this solar UGV. Specifically, the incident angle of the solar panels (enabled by the umbrella mechanism) at complete-open stage and the dimensions of the mechanism links and associated supports are separately optimized to respectively maximize the energy capture and the range of the UGV (assuming operation in Buffalo, NY), subject to stability and nominal velocity (of 2km/hr) constraints. The optimum design is found to provide an estimated range of 19.8 km/day.
To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, path planning, and formation control, and mission-specific objectives such as target search and group coverage. Most such missions are designed manually by teams of robotics experts. Recent work in automated approaches to learning swarm behavior has been limited to individual primitives with sparse work on learning complete missions. This paper presents a systematic approach to learn tactical mission-specific policies that compose primitives in a swarm to accomplish the mission efficiently using neural networks with special input and output encoding. To learn swarm tactics in an adversarial environment, we employ a combination of 1) map-to-graph abstraction, 2) input/output encoding via Pareto filtering of points of interest and clustering of robots, and 3) learning via neuroevolution and policy gradient approaches. We illustrate this combination as critical to providing tractable learning, especially given the computational cost of simulating swarm missions of this scale and complexity. Successful mission completion outcomes are demonstrated with up to 60 robots. In addition, a close match in the performance statistics in training and testing scenarios shows the potential generalizability of the proposed framework.
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