RoboRacer Platform: Autonomous Racing Testbed

• RoboRacer Platform is a modular, open-source testbed for reproducible experiments in autonomous racing, integrating ROS-centric software and accessible hardware.
• The platform leverages a 1/10-scale chassis, multi-sensor fusion, and diverse perception, planning, and control algorithms to facilitate simulation-to-reality transfer.
• Its community-driven ecosystem supports academic learning and competitive benchmarking, driving innovation in high-speed autonomy research.

The RoboRacer Platform is a modular and open-source research and education testbed for autonomous racing, formerly widely known as F1TENTH. It integrates accessible hardware, ROS-centric software, standardized simulation environments, and curated datasets to enable reproducible experiments and benchmarks in perception, planning, control, and multi-agent autonomous driving (Charles et al., 18 Jun 2025). Its extensibility and community infrastructure have established RoboRacer as a canonical platform for bridging theoretical research and real-world deployment in high-speed autonomy.

1. Hardware Architecture

RoboRacer is based on a 1/10-scale electric chassis (Traxxas Slash 4x4), providing up to 4 m/s maximum speed with 3S LiPo batteries (11.1 V, ~5 Ah), supporting 20–30 minutes of continuous operation (Charles et al., 18 Jun 2025). The platform’s sensing envelope typically includes:

• 2D LiDAR (e.g., Hokuyo UST-10LX): 270° FOV, 0.25° angular resolution, 10 Hz scan rate, 0.1–30 m range.
• RGB-D Camera (e.g., Intel RealSense D435): 1280×720 at 30 fps, depth up to 10 m, USB 3.0 interface.
• IMU: 3-axis ±16 g accelerometer, ±2000 °/s gyro, commonly integrated directly on compute module.

Onboard computation is provided by NVIDIA Jetson TX2/Xavier NX/AGX platforms (256–512 CUDA cores, 1–2 TFLOPS FP16, 32–128 GB SSD/eMMC), with adjustable power profiles (5–15 W). Communication uses Wi-Fi 802.11ac, plus optional USB/UART/CAN bus integration for actuator control and sensor fusion.

This hardware configuration is modular, allowing addition or replacement of sensors and compute units. Power management circuits provide 5 V/12 V rails to all components. Comparable platforms such as MuSHR (Srinivasa et al., 2019) and AutoRally (Goldfain et al., 2018) choose alternate chassis sizes, battery capacities, and compute units but adhere to similar modular and open-source integration philosophies.

2. Software Architecture and System Design

The RoboRacer system runs Ubuntu Linux (18.04/20.04), with ROS 1 (Melodic/Noetic) as the primary middleware; there is also growing ROS 2 support. It comprises multiple ROS node modules:

• Perception: LiDAR processing (scan-to-point cloud, clustering, occupancy mapping with Gmapping/Cartographer), camera-based object detection (YOLO v3/v4), and sensor fusion using message_filters and tf transformations.
• State Estimation: Extended Kalman Filter (EKF) fuses IMU, wheel odometry, and LiDAR-based pose correction; SLAM variants (Gmapping, Hector SLAM) enable mapping of unknown tracks.
• Planning: Global path planning with A* or RRT*, local planning with Pure Pursuit, Stanley, or Follow-The-Gap implemented as ROS Action servers, and learning-based planners (PPO/DDPG) via custom topics.
• Control: PID loops for throttle and steering (via ros_control), Model Predictive Control (MPC: CasADi/CVXGEN), and reactive control (Follow-The-Gap).
• Simulation Environments: F1TENTH Gym (2D physics, OpenAI Gym-compliant), Gym-ROS, Gazebo with f1tenth.dev plugin, as well as CARLA, AutoDRIVE, SVL Simulator, and AWSIM for high-fidelity studies.

This modular stack supports rapid prototyping, distributed experiments, and direct transfer from simulation to hardware (Charles et al., 18 Jun 2025). Inter-platform communication leverages ROS topics and services, supporting both intra-vehicle and inter-vehicle coordination scenarios.

3. Simulation-to-Reality (Sim2Real) Transfer and System Identification

RoboRacer addresses the Sim2Real gap through extensive domain randomization and adaptation techniques:

• Domain Randomization: Variations in textures, lighting, friction coefficients $\mu \in [0.4, 0.8]$, and measurement noise $\sigma_L \sim \mathcal{N}(0, \sigma^2)$ are imposed in simulation to encourage policy generalization.
• Domain Adaptation: Perception networks are fine-tuned with small, real-vehicle datasets. Contrastive feature distribution alignment is employed to minimize sim-real discrepancy, formalized as:

$$\mathcal{L}(\theta) = \mathbb{E}_{(x_s, y_s) \sim \mathcal{D}_s}\left[\ell(f_\theta(x_s), y_s)\right] + \lambda\,\mathrm{dist}\left(\phi(x_s), \phi(x_t)\right)$$

where $\phi$ extracts features and $\mathrm{dist}$ can be the maximum mean discrepancy.

• System Identification: Physical models (e.g., kinematic or bicycle dynamics) are fit by minimizing rollout errors between measured and predicted states.

Additionally, high-fidelity simulation environments permit evaluation under varied dynamics and sensor conditions, and benchmark policies against standardized datasets (Charles et al., 18 Jun 2025).

4. Perception, Planning, and Control Algorithms

• Perception: LiDAR-based wall-following, mapping (5 cm occupancy grid, 5–10 Hz), object detection (YOLO-cone/barrier datasets), and SLAM (loop closure with Cartographer, LOAM for 3D).
• Planning:
  • Graph search (A*, Dijkstra) with cost function $$J(P) = \sum_{k=0}^{|P|-1} \big( w_d\,\|p_{k+1}-p_k\| + w_s\,\kappa(p_k)^2\big)$$, where $\kappa$ is path curvature.
  • Sampling-based planners (RRT*): collision-free path samples, rewiring for cost minimization.
  • Learning-based (PPO): reward $r = -($lap_time$+ \alpha \cdot$collision_penalty$)$.
• Control:
  • PID: $$u(t) = K_Pe(t) + K_I\int_0^t e(\tau)d\tau + K_D\dot{e}(t)$$.
  • Pure Pursuit: geometric look-ahead-based steering angle prediction.
  • Stanley: combines cross-track and heading error.
  • MPC: horizon-$N$ constrained optimization for $$\min_{u_{0:N-1}} \sum_{k=0}^{N-1} \|x_k - x_k^\mathrm{ref}\|_Q^2 + \|u_k\|_R^2$$, subject to $x_{k+1}=f(x_k, u_k)$.

Reactive obstacle avoidance uses the Follow-The-Gap algorithm: LiDAR scan identifies the largest collision-free sector, and actuates toward its centerline (Charles et al., 18 Jun 2025).

5. Benchmarks, Datasets, and Performance Evaluation

RoboRacer maintains a comprehensive benchmarking ecosystem:

Asset Type	Description	Source/Format
Track Maps	20+ tracks (Spielberg, Silverstone, Yas Marina)	Standardized 1:10 layouts, 2.2 m width
LiDAR Datasets	TinyLidarNet: ~15,000 scans w/ labeled gaps	2D LiDAR, annotated
Vision Datasets	~2,000 cone/barrier-annotated images	YOLO format, Roboflow Universe
Trajectory Data	ORL: 300+ trajectories × 30 agents × 250 steps	Multi-agent interactions

Benchmark metrics include lap time (s), mean/peak velocity (m/s), obstacle collision rate, and path deviation (cm). Leaderboards register controller class results (PID, MPC, RL) across test circuits (Charles et al., 18 Jun 2025).

6. Educational Impact and Community Ecosystem

RoboRacer is widely used in academic curricula and autonomous racing competitions:

• Education: The “F1TENTH CourseKit” is adopted at universities such as UCF, BU, and TUM, supporting modules on PID control, SLAM, and MPC, and hands-on lab experiences (wall-following, relay racing, convoy control with OpenConvoy) (Charles et al., 18 Jun 2025).
• Competitions: Annual “Autonomous Grand Prix” events at premier robotics conferences (ICRA, ITSC, IROS) and virtual racing leagues (F1TENTH Gym). Over 500 teams across 50+ institutions participate, with observed lap-time improvements of ~10–20% year-over-year. Multi-agent RL-based overtaking and advanced MPC methods have achieved leading placements (Charles et al., 18 Jun 2025).

Community-maintained documentation, open-source repositories, and ongoing research projects have established RoboRacer as a reference platform for benchmarking and rapid innovation.

7. Current Trends, Lessons, and Research Directions

Key observations include:

• Modularity accelerates sensor/compute upgrades and algorithm prototyping.
• Sim2Real transfer remains an open problem; combinations of domain randomization and minimal real-data fine-tuning yield the most robust transfer.
• Classical controllers (Stanley, Pure Pursuit) provide reliable baselines; learning-based controllers are increasingly favored for unstructured environments but require further robustness validation.

Active research explores:

• Hybrid pipelines (MPC with learned residual dynamics).
• Hierarchical planning (integrating global graph search with locally learned cost maps).
• Edge-based online system identification for adapting vehicle dynamics.
• Safe multi-agent coordination under partial observability and real-time collaborative planning (Charles et al., 18 Jun 2025).

The RoboRacer platform persists as a fundamental asset for reproducible, high-speed autonomous systems experimentation, education, and benchmarking, and it continues to evolve in response to new research challenges and community-driven innovation.