Kiril Solovey
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View article: Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees
Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees Open
Kinodynamic motion planning is concerned with computing collision-free trajectories while abiding by the robot's dynamic constraints. This critical problem is often tackled using sampling-based planners (SBPs) that explore the robot's high…
View article: Effective Sampling for Robot Motion Planning Through the Lens of Lattices
Effective Sampling for Robot Motion Planning Through the Lens of Lattices Open
View article: Effective Sampling for Robot Motion Planning Through the Lens of Lattices
Effective Sampling for Robot Motion Planning Through the Lens of Lattices Open
Sampling-based methods for motion planning, which capture the structure of the robot's free space via (typically random) sampling, have gained popularity due to their scalability, simplicity, and for offering global guarantees, such as pro…
View article: From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection
From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection Open
Motion planning is a central challenge in robotics, with learning-based approaches gaining significant attention in recent years. Our work focuses on a specific aspect of these approaches: using machine-learning techniques, particularly Su…
View article: Impossibility of Self-Organized Aggregation without Computation
Impossibility of Self-Organized Aggregation without Computation Open
In their seminal work, Gauci et al. (2014) studied the fundamental task of aggregation, wherein multiple robots need to gather without an a priori agreed-upon meeting location, using minimal hardware. That paper considered differential-dri…
View article: Toward certifiable optimal motion planning for medical steerable needles
Toward certifiable optimal motion planning for medical steerable needles Open
Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to har…
View article: Inspection planning under execution uncertainty
Inspection planning under execution uncertainty Open
Autonomous inspection tasks necessitate path-planning algorithms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty,…
View article: Terraforming – Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery
Terraforming – Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery Open
In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Mu…
View article: Toward certifiable optimal motion planning for medical steerable needles
Toward certifiable optimal motion planning for medical steerable needles Open
Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to har…
View article: Terraforming -- Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery
Terraforming -- Environment Manipulation during Disruptions for Multi-Agent Pickup and Delivery Open
In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Mu…
View article: Corrections to “Probabilistic Completeness of RRT for Geometric and Kinodynamic Planning With Forward Propagation”
Corrections to “Probabilistic Completeness of RRT for Geometric and Kinodynamic Planning With Forward Propagation” Open
Our original publication Kleinbort et al. (2019) contains an error in the analysis of the case of the kinodynamic RRT. Here, we rectify the problem by modifying the proof of Theorem 2 , which, in particular, necessitated a revision of Lemm…
View article: Leveraging Experience in Lifelong Multi-Agent Pathfinding
Leveraging Experience in Lifelong Multi-Agent Pathfinding Open
In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackle…
View article: Resolution-Optimal Motion Planning for Steerable Needles
Resolution-Optimal Motion Planning for Steerable Needles Open
Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating nee…
View article: Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems
Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems Open
Robust motion planning entails computing a global motion plan that is safe under all possible uncertainty realizations, be it in the system dynamics, the robot's initial position, or with respect to external disturbances. Current approache…
View article: Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation
Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation Open
Multi-agent pathfinding (MAPF) is concerned with planning collision-free paths for a team of agents from their start to goal locations in an environment cluttered with obstacles. Typical approaches for MAPF consider the locations of obstac…
View article: Leveraging Experience in Lifelong Multi-Agent Pathfinding
Leveraging Experience in Lifelong Multi-Agent Pathfinding Open
In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackle…
View article: Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road\n Networks
Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road\n Networks Open
We address the problem of routing a team of drones and trucks over\nlarge-scale urban road networks. To conserve their limited flight energy,\ndrones can use trucks as temporary modes of transit en route to their own\ndestinations. Such co…
View article: Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks
Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks Open
We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coord…
View article: Resolution-Optimal Motion Planning for Steerable Needles
Resolution-Optimal Motion Planning for Steerable Needles Open
Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating nee…
View article: When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes
When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes Open
Congestion pricing has long been hailed as a means to mitigate traffic congestion; however, its practical adoption has been limited due to the resulting social inequity issue, e.g., low-income users are priced out off certain roads. This i…
View article: Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition
Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition Open
Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fund…
View article: Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment
Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment Open
System optimum (SO) routing, wherein the total travel time of all users is minimized, is a holy grail for transportation authorities. However, SO routing may discriminate against users who incur much larger travel times than others to achi…
View article: Efficient Large-Scale Multi-Drone Delivery using Transit Networks
Efficient Large-Scale Multi-Drone Delivery using Transit Networks Open
We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas. Besides flying directly, drones can use public transit vehicles such as buses and trams as temporary modes of transport…
View article: Near-Optimal Multi-Robot Motion Planning with Finite Sampling
Near-Optimal Multi-Robot Motion Planning with Finite Sampling Open
An underlying structure in several sampling-based methods for continuous multi-robot motion planning (MRMP) is the tensor roadmap (TR), which emerges from combining multiple PRM graphs constructed for the individual robots via a tensor pro…
View article: Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space
Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space Open
We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores the state-cost space and has been shown to efficiently obta…
View article: Revisiting the Asymptotic Optimality of RRT
Revisiting the Asymptotic Optimality of RRT Open
RRT* is one of the most widely used sampling-based algorithms for asymptotically-optimal motion planning. This algorithm laid the foundations for optimality in motion planning as a whole, and inspired the development of numerous new algori…
View article: On Local Computation for Optimization in Multi-Agent Systems
On Local Computation for Optimization in Multi-Agent Systems Open
A number of prototypical optimization problems in multi-agent systems (e.g., task allocation and network load-sharing) exhibit a highly local structure: that is, each agent's decision variables are only directly coupled to few other agent'…
View article: Multi-Robot Path Planning Using Medial-Axis-Based Pebble-Graph Embedding
Multi-Robot Path Planning Using Medial-Axis-Based Pebble-Graph Embedding Open
We present a centralized algorithm for labeled, disk-shaped Multi-Robot Path Planning (MPP) in a continuous planar workspace with polygonal boundaries. Our method automatically transform the continuous problem into a discrete, graph-based …
View article: Sample Complexity of Probabilistic Roadmaps via $\epsilon$-nets.
Sample Complexity of Probabilistic Roadmaps via $\epsilon$-nets. Open
We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in\nthe finite time (non-asymptotic) regime. In particular, we investigate how\ncompleteness and optimality guarantees of the approach are influenced by the\nunderlyi…
View article: Sample Complexity of Probabilistic Roadmaps via $ε$-nets
Sample Complexity of Probabilistic Roadmaps via $ε$-nets Open
We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time (non-asymptotic) regime. In particular, we investigate how completeness and optimality guarantees of the approach are influenced by the underlying …