Search Results for author:
Found 45 papers, 18 papers with code
MeshA*: Efficient Path Planing With Motion Primitives
To this end we suggest a novel technique rooted in the idea of searching over the grid cells (as in vanilla A*) simultaneously fitting the possible sequences of the motion primitives into these cells.
Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and second, it often makes mistakes in whether to use a tool at all.
NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot
Based on this idea, we proposed NavTopo - a full navigation pipeline based on topological map and two-level path planning.
MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset.
Safe Policy Exploration Improvement via Subgoals
The subgoal policy is trained to generate the subgoal based on the transitions from the buffer of the safe (main) policy that helps the safe policy to reach distant goals.
POGEMA: A Benchmark Platform for Cooperative Multi-Agent Navigation
Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments with, mostly, few agents and full observability.
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
This is known as any-angle pathfinding.
PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching
Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot.
Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding
Our approach utilizes the agent's observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS.
Improved Anonymous Multi-Agent Path Finding Algorithm
A well-established approach to solve this problem is to reduce it to a special type of a graph search problem, i. e. to the problem of finding a maximum flow on an auxiliary graph induced by the input one.
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints.
Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results
We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem.
Safe Interval Path Planning With Kinodynamic Constraints
Safe Interval Path Planning (SIPP) is a powerful algorithm for solving single-agent pathfinding problem when the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment.
TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers
To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search.
Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)
Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to the high-level of CBS - Anytime BCBS.
Pathfinding in stochastic environments: learning vs planning
Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner.
POGEMA: Partially Observable Grid Environment for Multiple Agents
We introduce POGEMA (https://github. com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems .
Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot
The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments
In this paper, we consider the problem of multi-agent navigation in partially observable grid environments.
Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints
In this paper, we present a method that mitigates this issue to a certain extent.
MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
The dataset is photo-realistic and provides both the localization and the map ground truth data.
Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles
Path finding is a well-studied problem in AI, which is often framed as graph search.
Improving Continuous-time Conflict Based Search
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps.
Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
Simultaneous localization and mapping, especially the one relying solely on video data (vSLAM), is a challenging problem that has been extensively studied in robotics and computer vision.
A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation
We study the problem of multi-agent navigation in static environments when no centralized controller is present.
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles.
GAN Path Finder: Preliminary results
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it.
Real-time Vision-based Depth Reconstruction with NVidia Jetson
Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution.
Multi-Agent Pathfinding with Continuous Time
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide.
eLIAN: Enhanced Algorithm for Angle-constrained Path Finding
This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer's search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered.
Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results)
We study the problem of planning collision-free paths for a group of homogeneous robots.
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper.
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision.
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning.
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots).
Original Loop-closure Detection Algorithm for Monocular vSLAM
Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays.
Loop Closure Detection
Simultaneous Localization and Mapping
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m).
Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths
We study the multi-agent path finding problem (MAPF) for a group of agents which are allowed to move into arbitrary directions on a 2D square grid.
Behavior and path planning for the coalition of cognitive robots in smart relocation tasks
In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location.
Psychologically inspired planning method for smart relocation task
On the subsymbolic level the task of path planning is considered and solved as a graph search problem.
Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments
As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.
Grid-based angle-constrained path planning
Square grids are commonly used in robotics and game development as spatial models and well known in AI community heuristic search algorithms (such as A*, JPS, Theta* etc.)
