Search Results for author:
Found 10 papers, 1 papers with code
Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Vehicle Decision-Making in Dynamic Environment
We introduce an AV centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles.
A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle
Both conventional and nonlinear SMC controllers' outcomes are showcased with a lawn-mowing manoeuvre scenario.
Geometric Tracking Control of a Multi-rotor UAV for Partially Known Trajectories
We formalize theoretically the effect of these filter structures on the system error dynamics.
CAMEL: Learning Cost-maps Made Easy for Off-road Driving
In this paper, we address the problem of learning the cost-map values from the sensed environment for robust vehicle path planning.
Nonlinear Model Predictive Control Framework For Cooperative Three-Agent Target Defense Game
The TAD game consists of an attacker and a cooperative target-defender pair.
A Deep Learning Approach To Estimation Using Measurements Received Over a Network
The measurements are communicated over a network as packets, at a rate unknown to the estimator.
NMPC-Based Cooperative Strategy For A Target Pair To Lure Two Attackers Into Collision
This paper presents a cooperative target defense strategy using nonlinear model-predictive control (NMPC) framework for a two--targets two--attackers (2T2A) game.
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments
We present OffRoadTranSeg, the first end-to-end framework for semi-supervised segmentation in unstructured outdoor environment using transformers and automatic data selection for labelling.
Context-Aware Deep Q-Network for Decentralized Cooperative Reconnaissance by a Robotic Swarm
One of the crucial problems in robotic swarm-based operation is to search and neutralize heterogeneous targets in an unknown and uncertain environment, without any communication within the swarm.
Multiagent Systems
MAPEL: Multi-Agent Pursuer-Evader Learning using Situation Report
We present a detailed analysis of how these two cooperation methods perform when the number of agents in the game are increased.
