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Guidance-Control System of a Quadrotor for Optimal Coverage in Cluttered Environment with a Limited Onboard Energy: Complete Software

Abstract : In this paper, a Guidance-Control System (GCS) for optimal coverage planning, using a quadrotor, in damaged area is considered. The quadrotor is assumed to visit a set of reachable points, defined manually by the user or automatically generated, following the shortest path while avoiding the no-fly zones. The problem is solved by using a two-stage proposed algorithm. In the first stage, a novel tool for cluttered environments based on optimal Rapidly-exploring Random Trees (RRT) approach, called Multi-RRT* Fixed Node (RRT*FN), is developed to define the shortest paths from each point to its neighbors. By means of the pair-wise costs between points provided by the first-stage algorithm, in the second stage, the overall shortest path is obtained by solving a Traveling Salesman Problem (TSP) using Genetic Algorithms (GA). Taking into consideration the limited onboard energy, multi-rounds for the coverage planning are assumed as an alternative by adapting our problem as a Vehicle Routing Problem (VRP). This latter is solved using the savings heuristic approach. The guidance module is supported by an efficient controller that minimizes the consumed energy and allows a damped response (i.e. without overshoot). It is a reference model based control strategy called Interconnection Damping Assignment-Passivity Based Control (IDA-PBC). The effectiveness of the overall system is demonstrated via numerical simulations and confirmed experimentally with very promising results.
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https://hal.archives-ouvertes.fr/hal-03121872
Contributor : Houria Siguerdidjane Connect in order to contact the contributor
Submitted on : Tuesday, January 26, 2021 - 4:23:40 PM
Last modification on : Saturday, May 1, 2021 - 3:49:49 AM

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Yasser Bouzid, Yasmina Bestaoui, Houria Siguerdidjane. Guidance-Control System of a Quadrotor for Optimal Coverage in Cluttered Environment with a Limited Onboard Energy: Complete Software. Journal of Intelligent and Robotic Systems, Springer Verlag, 2019, 95 (2), pp.707-730. ⟨10.1007/s10846-018-0914-5⟩. ⟨hal-03121872⟩

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