Javier Alonso-Mora
115 records found
1
In this paper, we present an approach for fleet sizing in the context of flash delivery, a time-sensitive delivery service that requires the fulfilment of customer requests in minutes. Our approach effectively combines individual delivery requests into groups and generates optimi
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We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dyn
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Representing the 3D environment with instance-aware semantic and geometric information is crucial for interaction-aware robots in dynamic environments. Nevertheless, creating such a representation poses challenges due to sensor noise, instance segmentation and tracking errors, an
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Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Optimization-based planners typically avoid humans through collision avoidance chance constraints. This allows the planner to optimize performance
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This study investigates the impact of walking and e-hailing on the scale economies of on-demand mobility services. An analytical framework is developed to i) explicitly characterize the physical interactions between passengers and vehicles in the matching and pickup processes, an
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Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to quantify the environment's di
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This work formally defines the problem of fleet sizing with delays (FSD), where the option of delaying individual tasks within fleet sizing is considered. We prove that the FSD problem is NP-hard and solve a formulation of the FSD problem as a mixed integer linear problem (MILP).
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Biased-MPPI
Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers
Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addr
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Beyond the last mile
Different spatial strategies to integrate on-demand services into public transport in a simplified city
Integrating on-demand services into public transport networks might be the best way to face the current situation in which these new technologies have increased congestion in most cities. When cooperating with on-demand services rather than competing with them, public transport w
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Autonomous mobile robots require predictions of human motion to plan a safe trajectory that avoids them. Because human motion cannot be predicted exactly, future trajectories are typically inferred from real-world data via learning-based approximations. These approximations provi
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Motion planning for autonomous robots in tight, interaction-rich, and mixed human-robot environments is challenging. State-of-the-art methods typically separate prediction and planning, predicting other agents’ trajectories first and then planning the ego agent’s motion in the re
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We study the problem of selecting a fleet of robots to service spatially distributed tasks with diverse requirements within time-windows. The problem of allocating tasks to a fleet of potentially heterogeneous robots and finding an optimal sequence for each robot is known as mult
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This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of multiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the o
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Ground robots navigating in complex, dynamic environments must compute collision-free trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular method to compute a trajectory in real-time. However, these methods often converge to locally optimal
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Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to mu
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As robots shift from industrial to human-centered spaces, adopting mobile manipulators, which expand workspace capabilities, becomes crucial. In these settings, seamless interaction with humans necessitates compliant control. Two common methods for safe interaction, admittance, a
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Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active
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Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conserva
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Many problems in robotics seek to simultaneously optimize several competing objectives. A conventional approach is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal sol
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On-demand ridepooling (ODRP) vehicles follow routes that are fully flexible. However, when the system does not provide door-to-door service and users can be asked to walk, their paths tend to concentrate, particularly along main streets that connect highly demanded areas of the c
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