Multi-agent systems, such as fleets of robots or drones, are increasingly deployed in logistics, inspection, and surveillance. These systems must reach their targets while maintaining safe separation, even under uncertain dynamics. This is challenging because unmodeled effects, disturbances, and sensor noise can degrade tracking performance and compromise safety. Model Predictive Control (MPC) is well suited for multi-agent navigation since it optimizes trajectories over a prediction horizon while enforcing input and state constraints. However, its performance depends on accurate models, and centralized formulations suffer from poor scalability and a single point of failure. We propose a cooperative Gaussian Process–augmented MPC (GP-MPC) framework that combines learning, chance-constrained safety, and distributed optimization. Each agent uses a Gaussian Process to learn its residual dynamics and quantify local uncertainty, incorporates this uncertainty into a chance-constrained collision-avoidance scheme, and coordinates only with neighbors through an ADMM-based distributed optimization method. This integration provides robustness to model errors and scalability to larger teams. The framework enables collision avoidance using only local uncertainty estimates, removing the need to share covariance information. By restricting computation and communication to each agent’s neighborhood, it maintains scalability and efficiency. Simulations show that the approach yields smoother and more efficient trajectories, faster convergence to targets, and reliable probabilistic safety compared to nominal and nonlinear MPC baselines. Convergence analysis further confirms robust consensus across a range of tuning parameters. ... Read more

One of the key challenges for multi-agent systems is collision free navigation in an unknown environment. In this work, we propose a unified framework for joint localization, control, and collision avoidance of multi-agent systems navigating in an unknown environment in the presence of dynamic obstacles. The cooperative agents rely on information from immediate neighboring agents within their communication neighborhood, and the dynamic obstacles are modelled as non-cooperative agents. The agents achieve localization by exploiting the individual agent dynamics, and pairwise distance measurements with agents in the sensing neighborhood of each cooperative agent. To ensure collision-free navigation, we exploit a Model Predictive Control (MPC) for each agent, with avoidance constraints using safety radius between pairwise agents. Futhermore, to avoid single point of failure, we propose Cooperative Positioning, Control and Collision Avoidance (CPCCA), which is based on distributed Method of Multipliers methods. We validate our framework and algorithms through simulations, demonstrating its effectiveness in real world scenarios, and propose directions for future work. ... Read more

Multi-agent networks are known for their scalability, robustness, flexibility, and are typically tasked with a variety of tasks such as target tracking, surveillance, traffic control, and environmental monitoring. Distributed Particle Filters (DPF) are often employed when the for non-linear parameter estimation with non-Gaussian noise. In this paper, we propose a novel distributed particle filter whose transmitted quantities are particles. The fusion process of particles is implemented in a distributed and iterative fashion. To reduce the communication overhead, we adopt the Gaussian process-enhanced resampling algorithm, which reduces the size of local particle set, while still ensures acceptable filtering performance. To determine the local particle set after the communication, we propose two solutions. Our first algorithm (GP-DPF) adopts a “scoring mechanism”, allowing local agents score the received particles and using the scores as the selection criterion. Our second proposed solution (FA-DPF) is a meta-heuristic approach, which uses the well known firefly algorithm as a selection method for particle-based distributed particle filtering. Our simulations demonstrate the superiority of our proposed algorithms under the condition of limited communication and computational resources against other state-of-the-art distributed particle filters. ... Read more

Detect-and-avoid is a crucial challenge in the autonomous navigation of single or multiple agent systems. For safe and reliable autonomous navigation in unknown and dynamic environments, obstacles should be sensed using onboard sensors and the trajectory should be adjusted accordingly. Additional challenge is introduced in the case of multi-agent systems, where the adjusted trajectory could introduce collisions between agents, for example in satellite swarms in Low Earth Orbits (LEO). The increasing amount of occupancy of the low orbit and the presence of space debris gives high risk of damaging satellites due to collisions. With communication between nearby satellites, cooperative methods enable the avoidance of collisions with dynamic obstacles while simultaneously finding an optimal trajectory of the cooperative agents. Drone swarms equipped in industrial settings encounter the challenge of navigating through a dynamic environments in a similar way. The dynamic obstacles are now other autonomous systems as well as humans, performing tasks simultaneously. ... Read more

Loudspeakers are usually placed in an environment unknown to the loudspeaker designers. Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. ... Read more

Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection. ... Read more