[PDF] DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms | Semantic Scholar (2024)

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This work reformulates the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor and observes that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method.

The linear collision avoidance constraints are expressed as chance constraints and solved to solve a probabilistic optimal control problem to improve safe operation and produce smoother paths with improved collision avoidance performances even under noisy sensing.

A decentralized algorithm that generates continuous-time trajectory online for a swarm of robots based upon model predictive control that is computationally efficient and can be used for online planning in moderate sized multi-robot systems is presented.

An optimization-based method is presented that ensures collision-free trajectory generation for formation flight and achieves spatial-temporal planning using polynomial trajectories, and a novel differentiable metric is proposed to quantify the overall similarity distance between formations.

This letter presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment and adopts a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield.

Decentralized spatial-temporal trajectory planning is introduced, which puts a well-formed trajectory representation named MINCO into multi-agent scenarios and ensures high-quality local planning for each agent subject to any constraint from either the coordination of the swarm or safety requirements in cluttered environments.

This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, and introduces a prescribed performance mechanism in the translational loop to render the formation regulation as a prior.

This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios and evaluates and verify the proposed communication strategy in simulation with four quadrotors and compares it with three baseline strategies.

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A novel approach for optimal trajectory tracking for unmanned aerial vehicles (UAV), using a linear model predictive controller (MPC) in combination with non-linear state feedback, which allows safe outdoors execution of multi-UAV experiments without the need for in-advance collision-free planning.

A tight bound for approximation of collision probability is developed, which makes the CCNMPC formulation tractable and solvable in real time.

The potential of LQG-Obstacles is demonstrated by safely and smoothly navigating a simulated quadrotor helicopter with complex non-linear dynamics and motion and sensing uncertainty through three-dimensional environments with obstacles and narrow passages.

The approach defines a protocol for robots to select their control input independently while guaranteeing collision-free motion for all robots, assuming the robots can perfectly observe each other's state.

A novel concurrent optimal trajectory planning method for a team of quadrotors to switch between various formation patterns in the confined indoor environment and a robust perfect tracking outer-loop controller is designed to compensate the poor resolution of the indoor localization system.

A search-based planning method to compute dynamically feasible trajectories for a quadrotor flying in an obstacle-cluttered environment that does not assume a hovering initial condition and is suitable for fast online re-planning while the robot is moving.

The method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in three-dimensional urban environments while considering coverage constraints and calculates collision avoiding velocities that maximize the conformity of an agent to an optimal path given by a global coverage strategy.

This paper addresses the problem of multi-MAV reactive collision avoidance by employing a model-based controller to simultaneously track a reference trajectory and avoid collisions and accounts for the full MAV dynamics.

This paper presents a decentralized strategy for collision-free navigation of multiple agents that combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and Model Predictive Control (MPC) and shows that this new algorithm can reduce velocity vibrations in the traditional ORCA algorithm.

It is shown that this approach permits the development of trajectories and controllers enabling such aggressive maneuvers as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.

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