AKS

Autonomous and Kooperative Systems Lab

Institute of Technology, University of Tartu

We are affiliated with Institute of Technology, University of Tartu. Our core aim is to develop algorithms that allow robots to perform complex deision making under real-time systems. The application domain of our research spans across mobile robot navigation, and autonomous driving to manipulation.

Latest News and Information

4 paper accepted at ICRA 2024.
PRIEST: Projection Guided Sampling Based Optimization for Autonomous Navigation accepted at RAL 2024 and will be presented in IROS 2024.
4 Papers accepted in IROS 2024. Hurray.

Paper accepted at ICRA 2023

AMSwarm: An Alternating Minimisation Approach for safe motion planning of Quadrotor swarms in cluttered and Dynamic Environments.

This paper proposes an online and scalable algorithm capable of generating safe and kinematically feasible trajectories for quadrotor swarms.

16-01-2023

Paper accepted at Robotics and Automation Letters and will be presented in ICRA 2024

VACNA: Visibility-Aware Cooperative Navigation with Application in Inventory Management

The paper introduces visibiity-aware cooperative navigation between a UAV and a UGV with applications in inventory management. We develop a novel sampling based optimizer that can compute real-time coordination trajectories while satisfying occlusion and collision constraints

2 Paper accepted at IROS 2023

End-To-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic

Hilbert Space Embedding-Based Trajectory Optimization for Multi-Modal Uncertain Obstacle Trajectory Prediction

First paper proposes a new differentiable trajectory optimizer and embeds it into an end-to-end learning pipelines for autonomous driving.

Second paper proposes a chance constrained optimizer to handle multi-modal obstacle trajecory predictions leveraging the concept of embedding distribution in Reproducing Kernel Hilbert Space.

3 Papers accepted at CASE 2023

Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic

UAP-BEV: Uncertainty Aware Planning Using Bird's Eye View Generated from Surround Monocular Images

Disentangling Planning and Control for Non-Prehensile Tabletop Manipulation

First paper proposes simultaneous optimization of behavioral and motion planning layers of autonomous driving

Second paper proposes a chance-constrained optimizer that can take plan safe trajectories for autonomous cars based on Birds Eye View prediction of the occupancy map.

Third paper develops a lower-level RL based controller for pushing objects with a manipulator and couples it with a high-level planner like A* for object manipulation.

2 Papers accepted at American Control Conference 2023

UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps

GPU Accelerated Batch Trajectory Optimization for Autonomous Navigation

First paper proposes uncertainty aware planning for monocular slam systems

Second paper proposes a vectorized batch optimizer to solve tens of hundreds of trajectory optimization in parallel over GPUs.

PRIEST: Projection Guided Sampling-Based Optimization For Autonomous Navigation

Accepted at Robotics and Automation Letters, 2024. It will also be presented in IROS 2024

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