Reachable set approach to collision avoidance for UAVs

Zhou, Yuchen; Baras, John S.

doi:10.1109/cdc.2015.7403154

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…Therefore, to avoid collision is to solve the optimization problem with appropriate methods under different constrains. The work in [20] formulates the collision avoidance problem as a convex optimization problem, and seeks for a suitable control constraint set for participating UAV based on reachable sets and tubes for UAVs. This method is limited to linear systems.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

2019

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This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

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Section: Literature Reviewmentioning

confidence: 99%

“…Figure 3 illustrates these state variables. The value ranges for all the velocity variables are [10,20], while the value ranges for all the angle variables are [0, 360). Suppose the length and width of the region are l and w, respectively.…”

Section: State Spacementioning

confidence: 99%

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

2019

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“…To the knowledge of the authors, the reachable sets (see [21]) are mostly used in the framework of safety guarantee, as in [8] and [22]. We intend here to use them in another framework: to find a proper path in complex scenarios, where no guarantee can be provided.…”

Section: Introductionmentioning

confidence: 99%

RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles

Beaucorps

Verroust-Blondet

Poncelet

et al. 2018

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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We present here a framework to integrate into a motion planning method the interaction zones of a moving robot with its future surroundings, the reachable interaction sets. It can handle highly dynamic scenarios when combined with path planning methods optimized for quasi-static environments. As a demonstrator, it is integrated here with an artificial potential field reactive method and with a Bézier curve path planning. Experimental evaluations show that this approach significantly improves dynamic path planning methods, especially when the speeds of the obstacles are higher than the one of the robot. The presented approach is used together with a global planning approach in order to handle complex static environments in presence of fast-moving obstacles. When the ego vehicle is not holonomic the presented approach is able to take dynamic constraints into account, which improve the prediction accuracy.

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“…In the case of aerial vehicles, tasks involving area coverage/inspection or rescue missions point out the importance of using multi-agent setups. The standard problem of formation control for a team of aerial vehicles is addressed in [1]- [6], whereas [7]- [11] consider leader-follower formation approaches, where the latter also treats the problem of collision avoidance with static obstacles in the environment; [12], [13] and [14] employ dynamic programming, Model Predictive Control and reachable set algorithms, respectively, for interagent collision avoidance. In [15] the cooperative evader pursuit problem is treated.…”

Section: Introductionmentioning

confidence: 99%

Decentralized motion planning with collision avoidance for a team of UAVs under high level goals

Verginis

Dimarogonas

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper addresses the motion planning problem for a team of aerial agents under high level goals. We propose a hybrid control strategy that guarantees the accomplishment of each agent's local goal specification, which is given as a temporal logic formula, while guaranteeing inter-agent collision avoidance. In particular, by defining 3-D spheres that bound the agents' volume, we extend previous work on decentralized navigation functions and propose control laws that navigate the agents among predefined regions of interest of the workspace while avoiding collision with each other. This allows us to abstract the motion of the agents as finite transition systems and, by employing standard formal verification techniques, to derive a high-level control algorithm that satisfies the agents' specifications. Simulation and experimental results with quadrotors verify the validity of the proposed method.

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Reachable set approach to collision avoidance for UAVs

Cited by 20 publications

References 24 publications

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles

Decentralized motion planning with collision avoidance for a team of UAVs under high level goals

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