Collision avoidance for aerial vehicles in multi-agent scenarios


This article describes an investigation of local motion planning, or collision avoidance, for a set of decision-making agents navigating in 3D space. The method is applicable to agents which are heterogeneous in size, dynamics and aggressiveness. It builds on the concept of velocity obstacles (VO), which characterizes the set of trajectories that lead to a collision between interacting agents. Motion continuity constraints are satisfied by using a trajectory tracking controller and constraining the set of available local trajectories in an optimization. Collision-free motion is obtained by selecting a feasible trajectory from the VO’s complement, where reciprocity can also be encoded. Three algorithms for local motion planning are presented—(1) a centralized convex optimization in which a joint quadratic cost function is minimized subject to linear and quadratic constraints, (2) a distributed convex optimization derived from (1), and (3) a centralized non-convex optimization with binary variables in which the global optimum can be found, albeit at higher computational cost. A complete system integration is described and results are presented in experiments with up to four physical quadrotors flying in close proximity, and in experiments with two quadrotors avoiding a human.

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  1. 1.

    Estimated states with a Kalman filter, including time delay compensation, are used in our implementation.

  2. 2.

    In this formulation arbitrary object shapes can be considered, but with the assumption that they do not rotate during the local planning horizon (typically a few seconds).

  3. 3.

    In previous works we have referred to it as holonomic trajectory and reference trajectory but, to hopefully increase clarity we now adopt the latter.

  4. 4.

    To account for the downwash effect that does not allow for close operation in the vertical direction. The results readily extend to robots of arbitrary shape with the assumption of constant orientation for \(t\in [0,\tau ]\).

  5. 5.

    We use IBM ILOG CPLEX.

  6. 6.

    We use IBM ILOG CPLEX.

  7. 7.

    Although the local planning is designed for on-board performance, this is left as future work.

  8. 8.

    Position control and the local trajectory interpreter could be on-board given access to position sensing.

  9. 9.

    Alternatively, any other controller with arbitrary constraints, or an LQR controller can be employed.

  10. 10.

  11. 11.

  12. 12.

  13. 13.

  14. 14.

    This might not always be the case, mostly in scenarios with fast dynamic obstacles, and if a collision can not be avoided. In that case, the quadrotor stops to guarantee passive safety.

  15. 15.

    If they are linearized following a given strategy such as avoid to the right, coordination is always achieved, but the solutions can be suboptimal.

  16. 16.

    For example both agents try to avoid each other on the same side.


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Correspondence to Javier Alonso-Mora.

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Supplementary material 1 (mp4 14075 KB)

Supplementary material 1 (mp4 14075 KB)


Appendix 1: Extension to homogenous group of agents

This appendix describes an extension of the local motion planning method of Sect. 4 towards an homogeneous group of agents (having the same control parameters).

With the assumption that all agents have the same control parameters (\(\omega _0, \omega _1\)) for the local trajectories (Sec. 7.4), the \(\varepsilon \) enlargement of the agents is not required. This is achieved by substituting the \(VO\) constraint (Constraint 3 of Sec. 4.5) by a 3D extension of the control obstacle \(C^M-CO\) introduced by Rufli et al. (2013). The \(C^M-CO\) characterizes the (\(n\)-differentiable) control trajectories in collision and is computed by formulating in relative candidate reference velocity space the full trajectories (Eq. (22) for \(M=2\)). Linearization of the constraint is still required and is done with respect to the current velocity. The algorithms described in this paper can be applied thereafter.

Relying on the concept of differential flatness for a quadrotor vehicle (Mellinger and Kumar 2011), if \(M=5\) is used, the quadrator would, in theory, be able to perfectly track the control trajectory. In this case the full state (up to the fifth derivative) shall be known for all agents.

Appendix 2: Equations repulsive velocity

For the repulsive velocity field of Fig. 4, left, the repulsive velocity for agent \(i\in {\mathcal {A}}\) is given by


where \(V_r\) is the maximum repulsive force and \(D_r\), \(D_h\) the preferred minimal inter-agent distance in the X–Y plane and in the Z component respectively.

Appendix 3: Equations linearization of VO

Denote \(\bar{h}_{ij} = \bar{h}_i + \bar{h}_j\) and \(\bar{r}_{ij} = \bar{r}_i + \bar{r}_j\).

The non-convex constraint \({\mathbb {R}}^3 \setminus {\mathcal {VO}}_{ij}^{\tau }\) is linearized to obtain a convex problem. For an approximation with five linear constraints, as in Fig. 7, the linear constraints are given by


where \(H_{ij}^1\) and \(H_{ij}^2\) represent avoidance to the right / left, \(H_{ij}^3\) and \(H_{ij}^4\) above / below and \(H_{ij}^5\) represents a head-on maneuver, which remains collision-free up to \(t=\tau \).

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Alonso-Mora, J., Naegeli, T., Siegwart, R. et al. Collision avoidance for aerial vehicles in multi-agent scenarios. Auton Robot 39, 101–121 (2015).

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  • Collision avoidance
  • Reciprocal
  • Aerial vehicle
  • Quadrotor
  • Multi-robot
  • Multi-agent
  • Motion planning
  • Dynamic environment