Multi-agent planning has numerous real-world applications, such as autonomous agriculture, search and rescue, and infrastructure inspection. It is thus of tremendous benefit to improve on the state-of-the-art in multiple metrics to make autonomous multi-agent systems more efficient and a more viable real-world solution. In this letter, we propose a new framework for multi-agent planning in a static environment that improves upon the existing state-of-the-art in multiple areas such as computation time, trajectory smoothness, and feasibility. The proposed method first generates a global path that only avoids static obstacles and then generates a Safe Corridor around it. It then extends the notion of Safe Corridors and makes them time-aware in order to account for the future positions of other agents. The time-aware Safe Corridor, along with a local reference trajectory generated from the global path, are used in an Mixed-Integer Quadratic Program/Model Predictive Control formulation that generates a collision-free optimal trajectory. The proposed framework is real-time, decentralized, and synchronous. It is compared to two recent state-of-the-art methods in simulations. It outperforms both methods in robustness as well as feasibility and computation time.

Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are Kalman filtering and Bayesian localization, often implemented via particle filtering. This paper reports on-going experimentation with an attractive alternative approach recently developed and based on interval analysis. Contrary to classical extended Kalman filtering, this approach allows global localization, and contrary to Bayesian localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. The approach is particularly robust to outliers.

The aim of this paper is to perform a comparison among several different Kalman filters algorithms designed for nonlinear systems. After presenting the most popular of them and showing its limitations, we introduce some new Kalman filters in order to compare them in the vehicle localization context. This comparison is based on the sole use of their predictive steps that corresponds to the worst case that it can occur in vehicle localization (corrective data are unavailable).

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.