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