RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

arXiv:2512.08574 · cs.RO · Submitted 2025-12-09 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: I'm Rosa, and with me are Dev and Taro, guest researcher.

Dev: Today's paper: "RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight".

Rosa: This work presents a novel approach to mutual collision avoidance in agile Uncrewed Aerial Vehicle (UAV) flight by integrating Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs).

Dev: First, who's behind it and why it matters.

Title and authors: Rosa: Well, this paper, "RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight," seems to tackle a really thorny problem in multi-robot navigation by using a specific combination of control and constraint methods. I'm curious if this approach is something we could actually see deployed outside of the controlled lab environment, and what kind of real-world operational time we should expect before we start seeing serious issues.

Dev: From an engineering standpoint, that’s exactly what I’m thinking, Rosa; the real test is always robustness when things get messy in a dynamic environment. The authors claim this system runs at one hundred Hz while modeling nonlinear dynamics, which suggests a high level of computational throughput that we need to scrutinize regarding latency and potential failure modes <ref:2512.08574#pg0>.

Taro: I'm interested in what happens when the world gets unexpectedly unpredictable; for instance, how does this framework handle scenarios where other agents misbehave or when the environment changes rapidly? It seems like the paper focuses heavily on maintaining safety under dynamic conditions.

Rosa: Exactly, Taro; I want to know more about that adaptability. The core idea is that it relies only on observable information about other robots, which sounds way less demanding than requiring every single robot to share its entire future plan constantly.

Dev: That reduced communication dependency is significant because it simplifies the hardware requirements for a swarm; if we don't need constant trajectory sharing, we can focus resources on the actual control loop performance.

Taro: But relying only on observable information means we are limited by what our sensors can actually perceive about those other robots; how robust is this estimation module when sensor noise is high?

Rosa: The paper does mention that the method shows robustness with respect to communication delays and noise in the estimation of states of other UAVs, which addresses that concern directly.

Dev: That’s good, but I want to know the practical limits; how much delay before the performance starts degrading significantly? The paper mentions coping with delays up to fifty milliseconds, so we need to know if that’s a usable threshold for a high-speed flight scenario.

Taro: And beyond just noise and communication latency, what happens when the world misbehaves in ways that aren't just simple sensor errors? Does this framework have mechanisms for recovering from unexpected external disturbances or sudden changes in agent behavior?

Title and authors: Rosa: The paper suggests it handles external disturbances well because of the direct integration of nonlinear dynamics into the NMPC formulation, which lets it react quickly to things happening right now.

Dev: Reacting quickly is important, but we have to worry about the constraints themselves; how does this time-dependent constraint mechanism manage situations where multiple potential collision avoidance maneuvers conflict with each other?

Taro: That’s a very deep question about the constraint generation part; if we have several neighboring robots, how does the reciprocal velocity constraint generator handle generating those sets of velocities for optimal collision avoidance simultaneously?

Rosa: The methodology involves computing the set of velocities for optimal collision avoidance ORCAτij for every neighboring robot 'j' based on robot 'i's current state. That suggests a complex, localized calculation happening very fast.

Dev: High computation is fine if it's fast enough; the paper claims it can run the whole pipeline at one hundred Hz, which implies that generating those constraints isn't introducing unacceptable computational overhead to the overall flight control loop timing <ref:2512.08574#pg0>.

Taro: Considering all this, what’s one specific scenario where you think this RVC-NMPC system might struggle most when it comes to maintaining collision-free navigation?

Rosa: The paper states that the approach prevents one hundred percent of violations of minimum mutual distance during continuous high-speed navigation in a constrained area, which is a strong empirical finding <ref:2512.08574#pg0>.

Dev: That one hundred percent prevention under those specific conditions is impressive, but I’d want to know if that guarantee holds when the flight speed or the density of robots increases dramatically beyond what was tested <ref:2512.08574#pg0>.

Taro: We need to see how it scales; does this system maintain that high success rate when we move from three UAVs in an APCX scenario to a larger, more complex swarm?

Rosa: The real-world experiments with three UAVs in an APCX scenario verified its practicality, showing minimum mutual distances comparable to or better than other methods under those real-world conditions.

Dev: That comparison against other methods is valuable, but what about the trade-off mentioned in the ablation study regarding flight time reduction? The paper notes that the introduced time dependence of constraints decreases average flight time by eleven percent while increasing the minimum mutual distance among UAVs <ref:2512.08574#pg1>.

Taro: So, there's a direct tension between optimizing for speed and ensuring a larger safety margin between agents; how do you balance those two conflicting goals in practice?

Title and authors: Rosa: It seems the authors found that this trade-off is beneficial, suggesting that tighter constraints can actually lead to better overall mission efficiency when factoring in flight time.

Dev: From my perspective, balancing that trade-off means carefully tuning the parameters of the time validity t v,m to ensure we get the best speed reduction without sacrificing too much separation distance.

Taro: Looking ahead, what do you see as a major area for future work for this RVC-NMPC approach? Are there any specific aspects of its formulation that you think need further development or refinement?

Rosa: I think exploring how this system integrates with even more complex, non-linear environmental dynamics beyond just other robots would be a natural next step for extending its applicability.

Dev: I’d also look at making the constraint generation module even more computationally lean; if we could simplify the ORCAτij calculation without losing accuracy, it would make deployment on smaller embedded hardware much easier.

Taro: Perhaps focusing on extending the concept of reciprocal constraints to handle interactions with passive, uncooperative obstacles in a way that maintains this level of efficiency would be a logical direction for autonomy research.

Rosa: That sounds like a very promising avenue, Taro; expanding the scope from just active robot-to-robot avoidance into more general environment interaction is where we’ll see the next evolution of this technology.

Dev: I agree; if we can keep the computational cost low while increasing the scope of interaction, that would push this system further into practical applications on smaller platforms.

Taro: So, to wrap up our thoughts on RVC-NMPC: it’s a solid control framework that uses observable data efficiently and manages to maintain high performance even when dealing with noise and latency, provided the computational budget is right.

Rosa: It really does provide a solid foundation for multi-UAV coordination in dense airspace, moving beyond methods that require constant trajectory sharing.

Dev: We’ll keep an eye on how well it performs under extreme density and speed tests as we move toward real-world validation of this RVC-NMPC system.

Taro: It’s exciting to see how this level of control fidelity can be achieved with such a computationally efficient pipeline.

Rosa: Indeed, it’s a piece of work that shows how careful integration between the mathematical model and the constraint generation can lead to practical safety in agile flight.

The paper's summary: Rosa: So, to wrap up that overview, the core of this paper is showing how you can use Nonlinear Model Predictive Control paired with time-dependent reciprocal velocity constraints to keep agile UAVs safe while keeping communication minimal.

Dev: That's right, Rosa; it boils down to using only what each robot directly observes about its neighbors rather than constantly exchanging future paths, which is a big win for bandwidth.

Taro: I’m still thinking about the dynamic aspects; how does this system handle those unpredictable moments when other agents suddenly change their behavior or when external forces push them off course?

Rosa: The authors show that because the NMPC directly models the nonlinear dynamics of each quadrotor, it can react very quickly to those sudden changes in motion.

Dev: I'm more concerned about the processing speed; they claim a one hundred Hz rate while handling all those complex constraints and dynamic models simultaneously, which is where I look for potential failure points in terms of latency.

Taro: That’s fair, Dev; if the constraint generator gets bogged down by too many neighboring robots or overly complex calculations, that high frequency becomes meaningless.

Rosa: The results suggest they can handle delays up to fifty milliseconds and still maintain success rates even when the state estimations of other UAVs are noisy.

Dev: That robustness against noise is important because in a real flight scenario, sensor data isn't perfect; we need assurance that the control loop doesn't become unstable just because of some measurement error.

Taro: But what about those situations where multiple collision avoidance maneuvers conflict with each other? The method relies on generating sets of velocities for optimal avoidance simultaneously, which sounds like a lot of optimization happening in real-time.

Rosa: It seems the clever part is that they introduce time validity for these constraints, meaning the constraints are only active if a collision is truly imminent within a short timeframe.

Dev: That temporal aspect makes sense; it prevents the controller from getting stuck trying to solve an impossible set of conflicting immediate avoidance maneuvers across long horizons.

Taro: So, if we look at the broader impact, this moves away from systems that require massive centralized coordination or constant sharing of future trajectories for safe swarm operations.

Rosa: Exactly; the implication is that we could have much denser, faster UAV swarms operating in complex environments without needing a constant high-bandwidth communication link between every single pair of robots.

Dev: And for me, it means we can push the hardware onto smaller onboard processors because the computational efficiency is high enough to run this complex NMPC at one hundred Hz reliably.

Taro: I see this as a huge step toward practical swarm autonomy, potentially allowing for much more resilient and agile operations in areas where communication infrastructure is unreliable or non-existent.

Rosa: It really opens up possibilities for applications far beyond simple flight testing, like coordinated inspection missions or complex search patterns in dynamic environments.

Dev: We'll have to see how these real-world performance metrics hold up when we move from three test drones to a much larger fleet where the number of potential constraints explodes.

The paper's improvements: Rosa: So, to summarize the improvements, the paper highlights how they’ve managed to tighten up the constraints on collision avoidance by introducing a time dependence into those reciprocal velocity constraints.

Dev: That's right, Rosa; it means we aren't just checking for collisions at every single time step but are calculating them based on when a collision is actually likely to happen.

Taro: This sounds like a clever way to manage the complexity of simultaneous avoidance maneuvers, because instead of trying to solve an infinite set of instantaneous constraints, they are focusing their attention on the relevant future time window.

Rosa: Exactly; the ablation study showed that this time dependence actually helps reduce average flight time by about eleven percent while simultaneously increasing the minimum distance between the UAVs.

Dev: That trade-off is interesting; it implies that by allowing a slightly tighter proximity during safe periods, you gain efficiency without actually increasing the risk of a crash under normal circumstances.

Taro: It’s about optimizing for both speed and safety margins at once, which is exactly what we need in real-world autonomy where resources are always constrained.

Rosa: The authors also noted that the system is very robust against external factors like communication latency and sensor noise, meaning it stays reliable even when things aren't perfectly controlled.

Dev: That reliability is crucial for deployment; if the control loop drops out due to a momentary lag or noisy sensor reading, we don't want the entire multi-robot system to lose its safety guarantees.

Taro: It’s about building a system that doesn't just work in ideal simulation but actually performs reliably when it encounters the messy reality of dynamic agents and imperfect sensors.

Rosa: And I’m curious about the real-world deployment aspect; while they tested it with three UAVs, how long do you think this framework can operate continuously outside of a controlled lab setting before we see significant degradation?

Dev: Well, based on their robustness tests, they’ve shown success rates even down to ten Hertz and delays up to fifty milliseconds, suggesting it has the endurance for real-world conditions.

Taro: I want to know if that continuous high-speed navigation capability holds when you scale up from a few drones in a controlled area to a larger swarm operating in an open, unconstrained space.

Rosa: The paper suggests that the system's ability to handle asynchronous communication and state estimation noise is strong, which points toward good scalability for distributed systems.

Dev: That’s encouraging because it means we don't have to build complex middleware just to synchronize the data; the constraint generator handles a lot of that complexity internally.

Taro: It’s about reducing the reliance on perfect synchronization, which is a major hurdle in large-scale multi-robot autonomy where communication links can be patchy.

Rosa: So, overall, this approach seems to offer a very practical path toward high-speed, safe swarm navigation that doesn't demand unrealistic levels of data sharing from every agent.

Dev: It certainly offers a strong framework for the control architecture itself; we just have to ensure the computational overhead stays manageable when we scale the number of interacting agents up significantly.

Conclusion: Rosa: To wrap up, we've seen how RVC-NMPC tackles mutual collision avoidance in agile UAV flight by using time-dependent reciprocal velocity constraints within an NMPC framework to minimize communication needs.

Dev: That's right, Rosa; it’s a very tightly integrated system that manages nonlinear dynamics while focusing on high computational efficiency for real-time control.

Taro: I think the biggest takeaway is how this method shifts the burden away from constant, heavy communication requirements toward a localized, observable state assessment for safety.

Rosa: Exactly; it opens up possibilities for much denser swarm operations where agents can navigate complex spaces without constantly flooding the airwaves with trajectory plans.

Dev: From an engineering standpoint, that efficiency is key; if we can maintain that high loop rate while keeping latency low, the deployment potential on embedded systems becomes much more realistic.

Taro: I agree; when you think about large-scale autonomous operations, reducing communication overhead by relying only on local observations is a massive enabler for real-world autonomy.

Rosa: We're really looking at a framework that can handle the unpredictability of dynamic environments better than previous methods because it’s inherently reactive through those time-dependent constraints.

Dev: It shows promise for maintaining safety even when state estimation from neighbors is imperfect, as the paper demonstrated resilience to some noise levels.

Taro: I'm still thinking about how this could translate beyond just UAVs; if we can get this kind of localized, constrained control working reliably in a drone swarm, it suggests a path for safer autonomous ground vehicles too.

Rosa: That's a fair point; the core mechanism is constraint-based avoidance, so the underlying principles are broadly applicable to any multi-agent system dealing with physical proximity.

Dev: We need to keep pushing on those real-world endurance tests, Rosa; we’ve seen success in simulation, but how does this hold up when things get truly chaotic and unexpected?

Taro: I'd like more data on scaling this to larger numbers of agents; if the constraint generation complexity grows too fast with the number of neighbors, that one hundred Hz performance might start to slip.

Rosa: That’s a valid concern, Taro; the authors did acknowledge that as you increase the density significantly, computational load does go up.

Dev: So we need to look closely at how they prune or optimize those constraint sets when the number of neighbors gets large so that it doesn't become a bottleneck in our control loop.

Taro: It’s about finding a structural property that allows this localized avoidance logic to remain efficient even when the neighborhood becomes very dense.

Rosa: Overall, the RVC-NMPC framework is definitely a solid step forward for making agile, multi-agent coordination more practical and communication-light.

Dev: I think we should keep an eye on how they refine that constraint pruning strategy because that’s where we can really see the limits of its deployment in high-density scenarios.

Czech Technical University in Prague

cs.RO

Submitted: 2025-12-09

Updated: 2026-10-04

Comments: 8 pages, 8 figures

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 76/100

The gist: This work presents a novel approach to mutual collision avoidance in agile Uncrewed Aerial Vehicle (UAV) flight by integrating Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal

Key concepts

Nonlinear Model Predictive Control (NMPC): NMPC is a control technique that uses a mathematical model of the drone's nonlinear dynamics to predict future states and optimize control inputs over a short time horizon. It minimizes a cost function while respecting physical limits like motor thrust, allowing for smooth, agile flight paths that account for complex maneuvers.
Reciprocal Velocity Constraints (RVCs): RVCs are safety rules that ensure robots avoid each other by limiting the relative velocities between them. The constraints are time-dependent; they only become active when the drones are close enough to potentially collide, providing a proactive and dynamic way to enforce mutual separation in real-time.
Time-Dependent Constraints (tv,m): This concept defines how long a specific collision avoidance constraint remains valid. It calculates the time after which the angle between relative position and velocity exceeds 90 degrees. This allows the system to activate safety rules only when necessary, optimizing performance by avoiding unnecessary restrictions during safe flight phases.
Observable Information Reliance: The proposed system is designed to function using only data that can be directly observed by the UAV sensors, such as its own state and estimates of other robots. This design eliminates the need for constant, high-bandwidth communication between all robots, making it more efficient and robust in real-world scenarios.

Terminology

Velocity Obstacle — 15× in this paper
Model Predictive Control — 11× in this paper
Ablation — 9× in this paper · explained in 1 episode
Nonlinear Model Predictive Control — 9× in this paper
Robustness — 9× in this paper · explained in 5 episodes
Ablation study — 7× in this paper · explained in 1 episode
Optimal Reciprocal Collision Avoidance — 7× in this paper
Core — 5× in this paper · explained in 2 episodes
Theoretical Guarantees — 5× in this paper · explained in 2 episodes
Physical Constraints — 3× in this paper · explained in 7 episodes
Uncrewed Aerial Vehicle — 3× in this paper
Ablation Studies — 2× in this paper · explained in 1 episode
Adaptation — 2× in this paper · explained in 1 episode
Computational overhead — 2× in this paper · explained in 2 episodes
Nonlinear Model Predictive Control (NMPC) — 2× in this paper · explained in 3 episodes
Accuracy — 1× in this paper · explained in 1 episode
Adaptability — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Communication Overhead — 1× in this paper · explained in 2 episodes
Convergence Guarantees — 1× in this paper · explained in 3 episodes
Dynamic Modeling — 1× in this paper · explained in 3 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Model Predictive Control (MPC) — 1× in this paper · explained in 5 episodes
Multi-Agent System — 1× in this paper · explained in 5 episodes
Point-Mass Model — 1× in this paper
Pruning — 1× in this paper · explained in 4 episodes

Summary

This work presents a novel approach to mutual collision avoidance in agile Uncrewed Aerial Vehicle (UAV) flight by integrating Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). This method is significant because it relies solely on observable information about other robots, eliminating the need for excessive communication, and achieves a high processing rate of 100 Hz. This efficiency allows the entire pipeline to run at 100 Hz while modeling nonlinear dynamics, which is key for agile UAV flight.

The gist

The proposed approach relies solely on observable information about other robots, eliminating the need for excessive communication.

How it works

The proposed system operates through a pipeline involving several modules:

UAV State Estimator: This module processes sensor data to provide estimates of the current robot’s position and velocity.
PMM Reference Trajectory Generator: This generates a minimum-time trajectory leading from the current state to a goal destination while respecting kinematic constraints and ignoring collisions. The generated reference trajectory is represented as a sequence of transition points, where each point encodes reference position, velocity, and acceleration.
Reciprocal Velocity Constraint Generator: This module takes the current position and velocity of robot 'i' along with the positions and velocities of other robots to generate a set of linear reciprocal velocity constraints ensuring mutual collision avoidance among robots. The positions and velocities of other robots are obtained either through a Communication Module or estimated from the robot’s sensor data using an Estimation Module.
NMPC Controller: This controller takes the generated reciprocal velocity constraints and the reference trajectory as inputs to generate control inputs that are passed to the Flight Control Unit, which translates this reference to control commands for individual rotors.

The NMPC Formulation

The core of the approach is formulated under an NMPC framework that minimizes a cost function (Equation 8):

minimize u0...uN−1 ∑k=1 ∆xk2 Q +∆uk−12 R +sk2 Z

Subject to the following constraints:

x0 = x(0)

xk+1 = fdyn(xk,uk), k ∈ 0,...,N −1

These dynamic model constraints are discretized using the Runge-Kutta method. The problem includes constraints on angular rates, individual motor thrusts, and collective motor thrusts. Crucially, it incorporates timedependent soft linear velocity constraints for mutual collision avoidance (Equation 14).

Time-Dependent Reciprocal Velocity Constraints (RVCs)

The mutual collision avoidance is implemented via time-dependent reciprocal collision avoidance constraints (Equation 19). The constraint generation involves:

Computing the set of velocities for optimal collision avoidance ORCAτij for every neighboring robot 'j' based on the current state of robot 'i'.
Converting these sets into linear constraints of the form bm ≤ Amv (Equation 15).
Introducing a time validity tv,m for each constraint, defined as:

tv,m = max prelim · vrel vrel squared,0

This represents the time after which the angle between the relative position vector and relative velocity vector exceeds π/2. The RVCs are then formulated as soft constraints with slack variables (Equation 19), where constraints are active only for time steps before the drone would pass a possibly colliding drone assuming both robots maintain their current velocities.

Performance and Robustness

The approach demonstrates superior performance in challenging scenarios, showing a 31 % improvement in terms of flight time reduction in challenging scenarios compared to the state of the art while maintaining collision-free navigation. The method is robust to communication latency and noise; simulations showed that the approach can cope with delays up to 50 ms and frequencies down to 10 Hz, maintaining success rates even under these conditions. Furthermore, reliability tests showed that the proposed approach prevents 100 % of violations of minimum mutual distance during continuous high-speed navigation in a constrained area. The method's independence between the length of the NMPC control horizon and the horizon used for detecting potential collisions allows avoidance maneuvers to be initiated several seconds in advance while keeping the NMPC horizon short to maintain real-time performance.

Ablation Study and Real-World Validation

Ablation studies confirmed that the introduced time dependence of the constraints, which decreases the average flight time by 11% while increasing the minimum mutual distance among UAVs, provides a clear benefit. Real-world experiments with three UAVs in an APCX scenario verified its practicality, maintaining high success rates and achieving minimum mutual distances comparable to or better than other methods under real-world conditions. The method's ability to handle asynchronous communication and state estimation noise, as shown in Fig. 5 and Fig.

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed the provided paper, RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight. The core contribution is a novel control framework that integrates time-dependent reciprocal velocity constraints directly into an NMPC formulation to achieve high-speed, agile multi-UAV navigation while minimizing communication overhead.

Here are the specific improvements and capabilities this system can enable in AI systems:

)1. Dynamic High-Speed Swarm Control with Minimal Communication

The system can control fleets of Uncrewed Aerial Vehicles (UAVs) operating in dense, open spaces at high speeds (up to 25 ms−1). Because the reciprocal velocity constraints are computed only based on current local state observations, the system requires only position and velocity data from neighbors, drastically reducing communication bandwidth requirements compared to methods that require future trajectory sharing.

)2. Real-Time Agility and Disturbance Rejection

The direct integration of nonlinear dynamics (quadrotor model) and time-dependent constraints into the NMPC formulation allows the controller to react instantly to external disturbances. The system can maintain collision-free navigation while executing highly agile maneuvers, such as rapid turns or evasive actions, without relying on perfect prior knowledge of other agents' future paths.

)3. Computational Efficiency for Real-Time Deployment

The algorithm is designed for high computational throughput (running at 100 Hz on a 2 GHz ARM processor). This allows the complex NMPC problem, including the generation of reciprocal velocity constraints, to solve rapidly. This efficiency enables deployment in embedded systems onboard UAVs, ensuring low-latency control loops critical for real-world flight safety.

)4. Robustness to Sensor Noise and Communication Latency

The system demonstrates resilience against realistic operational impairments:

It can cope with communication delays up to 50 ms and lower update frequencies (down to 10 Hz).
It maintains collision avoidance effectiveness even when state estimations of other UAVs are noisy (modeled as Gaussian processes with specified variances).

)5. Optimized Navigation for Mission Completion

The approach is optimized for mission efficiency. Through the use of a Point-Mass Model (PMM) minimum-time trajectory generator, the system can generate trajectories that balance minimizing flight time against kinematic feasibility and collision avoidance, resulting in a demonstrated 31% reduction in average flight time compared to state-of-the-art methods while maintaining safety.

)6. Verification of System Safety

The extensive simulation and real-world testing (including continuous navigation tests with thousands of goals) provide strong empirical evidence that the proposed system prevents 100% of violations of minimum mutual distances under challenging, dynamic conditions, thereby establishing a high level of operational reliability for multi-robot systems.

Abstract

This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). Unlike most existing methods, the proposed approach relies solely on observable information about other robots, eliminating the need for excessive communication. The computationally efficient algorithm for computing RVCs, together with the direct integration of these constraints into the NMPC problem formulation at the controller level, allows the whole pipeline to run at 100 Hz. This high processing rate, combined with modeled nonlinear dynamics of the controlled Uncrewed Aerial Vehicles (UAVs), is a key feature that facilitates the use of the proposed approach for agile UAV flight. The proposed approach was evaluated through extensive simulations emulating real-world conditions in scenarios involving up to 10 UAVs and velocities of up to 25 m/s, and in real-world experiments with accelerations up to 30 m/s squared. Comparison with the state of the art shows 31% improvement in terms of flight time reduction in challenging scenarios, while maintaining a collision-free navigation in all trials.

Sources

Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees