Autonomous Navigation Algorithms

Nav2Bot: ROS 2 Autonomous Navigation in Ignition Gazebo ➡ Differential drive robot simulation using ROS 2 Humble ➡ Autonomous navigation using Nav2 stack ➡ LiDAR-based obstacle detection and environment perception ➡ AMCL-based localization for accurate robot positioning ➡ Global and local path planning with real-time execution ➡ Complete TF tree (map → odom → base_link → lidar_link) ➡ RViz visualization for costmaps, paths, and robot pose ➡ Keyboard teleoperation support for manual control ✨ Why this matters: Autonomous navigation is one of the core challenges in robotics, where a robot must perceive its environment, determine its position, and plan a safe path to a goal without human intervention. This project demonstrates a complete ROS 2 Nav2 pipeline that integrates localization, planning, and control into a unified system. By combining LiDAR data, odometry, and costmaps, the robot can intelligently navigate through unknown environments while avoiding obstacles in real time. These principles are widely used in real-world robotics applications such as autonomous vehicles, warehouse automation systems, delivery robots, and service robotics. 📊 Key Highlights: ✔ Full ROS 2 Navigation Stack (Nav2) integration ✔ LiDAR-based perception and obstacle avoidance ✔ AMCL localization for accurate positioning ✔ Global and local path planning ✔ Real-time costmap generation ✔ Gazebo simulation with realistic robot behavior ✔ RViz-based monitoring and debugging 💡 Future Potential: This framework can be extended to: ➡ Multi-robot navigation systems ➡ SLAM + Nav2 integration for unknown environments ➡ AI-based dynamic obstacle detection ➡ Reinforcement learning for path optimization ➡ Real-world deployment on mobile robots 🔗 For students, engineers & robotics enthusiasts: This project provides a complete hands-on implementation of autonomous navigation using ROS 2, making it ideal for understanding how intelligent robots perceive, plan, and act in real environments. 🔁 Repost to support robotics research & engineering education! #ROS2 #Nav2 #Robotics #AutonomousSystems #Gazebo #Mechatronics #EngineeringProjects #Lidar #RViz #Automation #Navigation #AI #STEM #EngineeringEducation #RobotSimulation

Pleased to see the publication of the most recent work from MIT/ACL on "Off-Road Navigation via Implicit Neural Representation of Terrain Traversability", an excellent collaboration between Lucas (Yixuan Jia) and Andy (Qingyuan Li). https://lnkd.in/ej4aSgcm Autonomous off-road navigation requires robots to estimate terrain traversability from onboard sensors and plan motion accordingly. Conventional approaches typically rely on sampling-based planners such as MPPI to generate short-term control actions that aim to minimize traversal time and risk measures derived from the traversability estimates. These planners can react quickly but optimize only over a short look-ahead window, limiting their ability to reason about the full path geometry, which is important for navigating in challenging off-road environments. Moreover, they lack the ability to adjust speed based on the terrain-induced vibrations, which is important for smooth navigation on challenging terrains. In this paper, we introduce TRAIL (Traversability with an Implicit Learned Representation), an off-road navigation framework that leverages an implicit neural representation to model terrain properties as a continuous field that can be queried at arbitrary locations. This representation yields spatial gradients that enable integration with a novel gradient-based trajectory optimization method that adapts the path geometry and speed profile based on terrain traversability.

One of the key challenges of autonomous driving is scalably handling the complexity of driving scenarios, where traffic rules, city environments, and vehicles/pedestrians can interact in a myriad of possible ways. It’s not tractable to create hand-crafted rules that handle every case, so instead we rely on the power of “next frame prediction” in a compact world representation. Here the world representation is semantic segmentation, which captures the essence of what’s happening around a vehicle, and can be stably computed in real-time using Helm.ai’s production grade perception stack. One example of a set of complex scenarios is an intersection with traffic lights, which presents a large number of possibilities that an autonomous vehicle must navigate safely. To tackle this challenge, we added traffic light segmentation and traffic light state to our world model representation, and trained a foundation model to predict what might happen next based on an input sequence of observed segmentations. Our foundation model learned in a fully unsupervised way from real driving data the relationship between traffic light state and what the vehicles/agents on the road should do in various contexts. The result is an ability to forecast a wide variety of scenarios of interaction between traffic lights, intersection geometry, vehicles, and pedestrians that are consistent with potential real world scenarios, including predicting the paths of the ego vehicle and the other agents. In our latest demo, our intent and path prediction models predict 9 seconds into the future using 3 seconds of observed driving data, at 5 frames per second. This prediction capability includes learned human-like driving behaviors, such as intersection navigation, interaction with green and red lights, yielding to oncoming traffic before turning, and keeping a safe distance to other vehicles. Our foundation models are able to predict these future behaviors and plan safe paths by scalable learning from real driving data, without any hand crafted rules nor traditional simulators. Stay tuned for upcoming updates as we continue to expand our unified approach to ADAS through L4 autonomous driving by enriching the world model representation and scaling up our predictive DNNs. #helmai #generativeai #selfdrivingcars #artificialintelligence #ai #autonomousdriving #adas #computervision 

🌑🌑Navigation In Complete Darkness Using A Monocular Camera🌑🌑 Check out our latest work, "AsterNav: Autonomous Aerial Robot Navigation In Darkness Using Passive Computation," published in the IEEE Robotics and Automation Letters (IEEE RA-L) by Deepak Singh and Shreyas Khobragade, where we use nature-inspired custom apertures to obtain passive depth cues for accurate metric depth estimation using a cheap Raspberry Pi camera. No REAL data or Calibration NEEDED! #sim2real #computationalimaging Code is Open-Source! The paper videos are shot at max ISO using 8000$ filming setup from Nikon! 📄 PDF: https://lnkd.in/gACQnWBn 🌎 Project Website: https://lnkd.in/gaUxxta2 💻 Code: https://lnkd.in/gtXVzjic 📹 Full Video: https://lnkd.in/gSXH5VyD Autonomous aerial navigation in absolute darkness is crucial for post-disaster search and rescue operations, which often occur from disaster-zone power outages. Yet, due to resource constraints, tiny aerial robots, perfectly suited for these operations, are unable to navigate in the darkness to find survivors safely. In this paper, we present an autonomous aerial robot for navigation in the dark by combining an Infra-Red (IR) monocular camera with a large-aperture coded lens and structured light without external infrastructure like GPS or motion-capture. Our approach obtains depth-dependent defocus cues (each structured light point appears as a pattern that is depth dependent), which acts as a strong prior for our AsterNet deep depth estimation model. The model is trained in simulation by generating data using a simple optical model and transfers directly to the real world without any fine-tuning or retraining. AsterNet runs onboard the robot at 20 Hz on an NVIDIA Jetson Orin Nano. Furthermore, our network is robust to changes in the structured light pattern and relative placement of the pattern emitter and IR camera, leading to simplified and cost-effective construction. We successfully evaluate and demonstrate our proposed depth navigation approach AsterNav using depth from AsterNet in many real-world experiments using only onboard sensing and computation, including dark matte obstacles and thin ropes (diameter 6.25mm), achieving an overall success rate of 95.5% with unknown object shapes, locations and materials. To the best of our knowledge, this is the first work on monocular, structured-light-based quadrotor navigation in absolute darkness. P.S. Shreyas Khobragade is on the job market! Hire him :) Kudos to Deepak Singh, Shreyas Khobragade for the amazing work. #pearwpi Worcester Polytechnic Institute #dronesforgood #searchandrescue #SAR

In this demo we extend our prior work on obstacles avoidance at aggressive speeds, showcasing our Thar based autonomous vehicle navigating at near drift speeds, progressing towards our endeavour of Level-5 autonomy. Our autonomous vehicle at Swaayatt Robots (स्वायत्त रोबोट्स) was tasked with avoidance of traffic cones on the road, placed in a zig-zag fashion, at aggressive speeds. The location of the marked cones was not known to the planner beforehand. The #autonomousdriving task, i.e., motion planning (time parametrized trajectory computation) and decision making, was made even more challenging by restricting the AI agents to not act on obstacles unless they are within 24m radius. Level-5 #autonomousvehicles should be able to react quickly to overtake, or to avoid, any sudden unforeseeable obstacle or pedestrian on the road to avoid fatalities -- a capability demonstrated by our novel motion planning and decision making algorithmic framework over here. Our previous demo showcased our Bolero based platform consistently keeping speeds beyond 45 KMPH for most part, slowing down to only 39 KMPH at one point. Given Thar has lesser body roll, our framework successfully kept speeds well above 47 KMPH (even at the points of avoidance of obstacles), with speeds reaching as high as 55 KMPH. A typical human driver would feel uncomfortable at speeds beyond 40 KMPH in such as scenario. The entire algorithmic framework with 5 classical (one #reinforcementlearning-) agents , runs at 800+ Hz on a regular i7 processor, single thread. This algorithmic framework is being further scaled up with end-to-end deep reinforcement learning, and will be showcased in the month of March. #deeplearning #machinelearning #motionplanning

Lane Detection using Graph Search and Geometric Constraints for Formula Student Driverless This paper presents a graph-based approach for autonomous track mapping. The core algorithm, called CLC (Cone Lane Connector), uses backtracking search with geometric constraints to detect lane boundaries from sparse 2D points, a common challenge in Formula Student Driverless scenarios with noise and false positives. The implementation reconstructs track boundaries in real-time using only odometry and feature points from the perception stack, then autonomously navigates the track using pure pursuit control. Technical highlights: ↳ Custom ROS2 software pipeline interfacing with Assetto Corsa as the simulation environment ↳ Virtual joystick simulating an Xbox 360 controller for in-game vehicle control ↳ C++ bridge between ROS2 and Rerun for real-time visualization of the mapping pipeline The mapping algorithm was the most challenging aspect balancing real-time performance with accurate boundary reconstruction while managing multiple concurrent data streams required careful system design and optimization. Results: real-time mapping speeds up to 50 km/h, and 150 km/h during high-performance runs using the reconstructed track with a non-optimized racing line and speed profile. The original research was done: Silviu Roberto Popitanu Edoardo Caciorgna Alessandro Ciccone Marco Gabiccini Want to learn more about computer vision, VLMs, and the latest tools? Subscribe to my newsletter: https://lnkd.in/gRsEfgju #AutonomousDriving #ROS2 #Robotics #ComputerVision #SLAM #PathPlanning #Simulation

Mapless Navigation for Mobile Robot! Robots navigating unknown environments often encounter local optima, where they get stuck avoiding obstacles but fail to reach their destination. This research introduces a deep reinforcement learning-based mapless navigation framework that enables robots to efficiently escape local optima and improve real-world navigation performance. The researchers introduce: 1. Local Exploration Task: Encourages the robot to explore new paths instead of getting trapped in previously visited areas. 2. Adaptive Temperature Parameter: Adjusts exploration-exploitation trade-offs, stabilizing training while improving decision-making. 3. Soft Actor-Critic (SAC) Enhancement: Improves strategy learning by balancing risk and efficiency. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? The research showed higher success rates and shorter navigation paths compared to existing methods in both simulations and real-world experiments. This research moves us closer to truly autonomous robots capable of navigating complex, unstructured environments without predefined maps! Congrats to Yiming Hu, Shuting Wang, Yuanlong Xie, Shiqi Zheng, Peng Shi, Imre Rudas, and Xiang Cheng! 🔗 Read the full paper: https://lnkd.in/gJDgypCr I post the latest and interesting developments in robotics - 𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! #ReinforcementLearning #Robotics #AI #DeepLearning #AutonomousNavigation #RobotLearning P.S. I wasn't able to find some researchers on, please let me know if they're on linkedin!