Path design and optimization with obstacle avoidance via reinforcement learning

For the last couple of decades, finding an optimized drilling path has been one of the key concerns for drilling engineers. It takes a couple of months to plan a well for a large number of people. The motive of this thesis is to find the optimal drilling path based on coordinates. To trace the optimal path, this thesis will apply the reinforcement learning algorithm in Matlab. Another approach for this thesis is to find the shortest path by avoiding collision in a threedimensional grid view

Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm

The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.Comment: Final version as submitted to ICAPS-2017 (main track); 8 pages; 4 figures; 1 algorithm; 2 table

Algorithms for the minimum non-separating path and the balanced connected bipartition problems on grid graphs (With erratum)

For given a pair of nodes in a graph, the minimum non-separating path problem looks for a minimum weight path between the two nodes such that the remaining graph after removing the path is still connected. The balanced connected bipartition (BCP$_2$) problem looks for a way to bipartition a graph into two connected subgraphs with their weights as equal as possible. In this paper we present an algorithm in time $O(N\log N)$ for finding a minimum weight non-separating path between two given nodes in a grid graph of $N$ nodes with positive weight. This result leads to a 5/4-approximation algorithm for the BCP$_2$ problem on grid graphs, which is the currently best ratio achieved in polynomial time. We also developed an exact algorithm for the BCP$_2$ problem on grid graphs. Based on the exact algorithm and a rounding technique, we show an approximation scheme, which is a fully polynomial time approximation scheme for fixed number of rows.Comment: With erratu

Flight Planning in Free Route Airspaces

We consider the problem of finding cheapest flight routes through free route airspaces in a 2D setting. We subdivide the airspace into regions determined by a Voronoi subdivision around the points from a weather forecast. This gives rise to a regular grid of rectangular regions (quads) with every quad having an associated vector-weight that represents the wind magnitude and direction. Finding a cheapest path in this setting corresponds to finding a piece-wise linear path determined by points on the boundaries of the quads. In our solution approach, we discretize such boundaries by introducing border points and only consider segments connecting border points belonging to the same quad. While classic shortest path graph algorithms are available and applicable to the graphs originating from these border points, we design an algorithm that exploits the geometric structure of our scenario and show that this algorithm is more efficient in practice than classic graph-based algorithms. In particular, it scales better with the number of quads in the subdivision of the airspace, making it possible to find more accurate routes or to solve larger problems

GPU-based dynamic search on adaptive resolution grids

Abstract — This paper presents a GPU-based wave-front propagation technique for multi-agent path planning in ex-tremely large, complex, dynamic environments. Our work proposes an adaptive subdivision of the environment with efficient indexing, update, and neighbor-finding operations on the GPU to address several known limitations in prior work. In particular, an adaptive environment representation reduces the device memory requirements by an order of magnitude which enables for the first time, GPU-based goal path planning in truly large-scale environments (> 2048 m2) for hundreds of agents with different targets. We compare our approach to prior work that uses an uniform grid on several challenging navigation benchmarks and report significant memory savings, and up to a 1000X computational speedup. I

GPU-Based Dynamic Search on Adaptive Resolution Grids

This paper presents a GPU-based wave-front propagation technique for multi-agent path planning in extremely large, complex, dynamic environments. Our work proposes an adaptive subdivision of the environment with efficient indexing, update, and neighbor-finding operations on the GPU to address several known limitations in prior work. In particular, an adaptive environment representation reduces the device memory requirements by an order of magnitude which enables for the first time, GPU-based goal path planning in truly large-scale environments (\u3e 2048 m2 ) for hundreds of agents with different targets. We compare our approach to prior work that uses an uniform grid on several challenging navigation benchmarks and report significant memory savings, and up to a 1000X computational speedup

NafisNav: an Indoor Navigation Algorithm for Embedded Systems and based on Grid Maps

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. N. O. Eraghi, F. López-Colino, A. de Castro and J. Garrido, "NafisNav: An indoor navigation algorithm for embedded systems and based on grid maps," 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, 2015, pp. 345-350. doi: 10.1109/ICIT.2015.7125122An important goal in navigation of low cost robots is low memory usage. In this paper, we propose a novel navigation algorithm (NafisNav) suitable for embedded systems with low resources, mainly memory. The proposed path finding algorithm is designed and implemented in grid maps. Unlike existing algorithms, that mainly focus on obtaining the shortest possible path for navigation, the proposed algorithm focuses on reducing memory consumption, even at the cost of not always obtaining the best path. Experimental results show the trade-off between path length and memory consumption that is obtained, comparing it with typical algorithms such as Dijkstra or A*.This work has been supported by the Spanish Ministerio de Ciencia e Innovacion under project TEC2009-09871