Kavan Singh Sikand

Pranav Atreya, Haresh Karnan, Kavan Singh Sikand et al.

Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.

Kavan Singh Sikand, Logan Zartman, Sadegh Rabiee et al.

Long-term deployment of a fleet of mobile robots requires reliable and secure two-way communication channels between individual robots and remote human operators for supervision and tasking. Existing open-source solutions to this problem degrade in performance in challenging real-world situations such as intermittent and low-bandwidth connectivity, do not provide security control options, and can be computationally expensive on hardware-constrained mobile robot platforms. In this paper, we present Robofleet, a lightweight open-source system which provides inter-robot communication, remote monitoring, and remote tasking for a fleet of ROS-enabled service-mobile robots that is designed with the practical goals of resilience to network variance and security control in mind. Robofleet supports multi-user, multi-robot communication via a central server. This architecture deduplicates network traffic between robots, significantly reducing overall network load when compared with native ROS communication. This server also functions as a single entrypoint into the system, enabling security control and user authentication. Individual robots run the lightweight Robofleet client, which is responsible for exchanging messages with the Robofleet server. It automatically adapts to adverse network conditions through backpressure monitoring as well as topic-level priority control, ensuring that safety-critical messages are successfully transmitted. Finally, the system includes a web-based visualization tool that can be run on any internet-connected, browser-enabled device to monitor and control the fleet. We compare Robofleet to existing methods of robotic communication, and demonstrate that it provides superior resilience to network variance while maintaining performance that exceeds that of widely-used systems.

Haresh Karnan, Kavan Singh Sikand, Pranav Atreya et al.

One of the key challenges in high speed off road navigation on ground vehicles is that the kinodynamics of the vehicle terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3.5 m/s.

Kavan Singh Sikand, Sadegh Rabiee, Adam Uccello et al.

Autonomous mobile robots deployed in outdoor environments must reason about different types of terrain for both safety (e.g., prefer dirt over mud) and deployer preferences (e.g., prefer dirt path over flower beds). Most existing solutions to this preference-aware path planning problem use semantic segmentation to classify terrain types from camera images, and then ascribe costs to each type. Unfortunately, there are three key limitations of such approaches -- they 1) require pre-enumeration of the discrete terrain types, 2) are unable to handle hybrid terrain types (e.g., grassy dirt), and 3) require expensive labelled data to train visual semantic segmentation. We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space. The learned representations are then used along with the same unlabeled human navigation demonstrations to learn a mapping from the representation space to terrain costs. At run time, VRL-PAP maps from images to representations and then representations to costs to perform preference-aware path planning. We present empirical results from challenging outdoor settings that demonstrate VRL-PAP 1) is successfully able to pick paths that reflect demonstrated preferences, 2) is comparable in execution to geometric navigation with a highly detailed manually annotated map (without requiring such annotations), 3) is able to generalize to novel terrain types with minimal additional unlabeled demonstrations.