2A1-W05 Guidance Control with Collision Avoidance for Multiple UAVs under Communication Restricted(Cooperation Control of Multi Robots)

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

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Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments

Sensors ◽

10.3390/s18124101 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4101 ◽

Cited By ~ 6

Author(s):

Eduardo Ferrera ◽

Alfonso Alcántara ◽

Jesús Capitán ◽

Angel Castaño ◽

Pedro Marrón ◽

...

Keyword(s):

Collision Avoidance ◽

System Integration ◽

Field Experiments ◽

Positioning Systems ◽

Wind Gusts ◽

Aerial Vehicles ◽

Multiple Uavs ◽

Outdoor Environments ◽

External Conditions ◽

Field Experimentation

The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.

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