Dobot Rover X1 Explorer Sentinel Quadruped Robot

• Dobot Rover X1 Explorer Sentinel Quadruped Robot
• Full SLAM navigation with integrated LiDAR for autonomous mapping and path planning
• Dual built-in depth cameras plus Intel RealSense D435i for comprehensive three-dimensional environmental awareness
• 40 TOPS Orin Nano onboard AI compute powers real-time perception and autonomy tasks
• Enhanced Camera + LiDAR SDK for sensor fusion, inspection algorithm development, and navigation research
• Inverted knee joint architecture enables stair climbing and agile performance across complex terrain
• Extended 8000 mAh battery delivers approximately 2.5 hours of continuous runtime

Dobot Rover X1 Explorer Sentinel Quadruped Robot is the most capable configuration in the Explorer lineup, developed by DOBOT Robotics for institutions and research teams that require full autonomous navigation alongside a comprehensive onboard sensor suite. Beyond the depth cameras and Intel RealSense D435i carried over from the Vision tier, the Sentinel adds an integrated LiDAR unit and SLAM navigation support, enabling the platform to build real-time maps of its environment and plan paths without external infrastructure. A 40 TOPS Orin Nano compute module handles the demanding inference workloads that simultaneous localization, mapping, and multi-sensor fusion require, while the same inverted knee joint architecture handles terrain from flat floors to 30 to 35-degree inclines and obstacles up to 16 cm tall. This combination positions the Sentinel as a self-sufficient field platform for autonomous inspection, campus patrol, and applied navigation research.

The Sentinel ships with an Enhanced Camera + LiDAR SDK that provides unified access to the full sensor stack, including visual, depth, and LiDAR data streams, making it straightforward to develop and validate sensor fusion and autonomous navigation algorithms directly on hardware. Dual-language AI voice interaction, Wi-Fi and 4G connectivity, and OTA update support sustain long-term deployment across connected campus and laboratory environments. The open development ecosystem built around C++ and Python SDKs and the Isaac Gym simulation environment carries forward from the rest of the Explorer family, ensuring that research workflows and curriculum developed on the base platform translate without friction to the Sentinel. With an extended 8000 mAh battery, collision protection, and a modular payload interface, the Explorer Sentinel is built for institutions ready to operate fully autonomous quadruped systems in real-world environments.

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Model Features

Key hardware and engineering differentiators of the Explorer platform.

Mobility & Gait →

Mobility & Gait

Architecture: Inverted knee joint for enhanced stair climbing and confined-space navigation

Max Speed: 1.8 m/s

Climbing: 30 to 35-degree slopes

Obstacle Clearance: Up to 16 cm

Motion Actions: Waving, Jumping, Leaping

Sensing & Perception →

Sensing & Perception

RGB Cameras: Front and rear 1080P (1920x1080 at 30fps)

Depth Cameras: Dual built-in depth cameras + Intel RealSense D435i

LiDAR: Integrated LiDAR with SLAM navigation

Obstacle Avoidance: Bidirectional with auto-braking response

Autonomous Following: Supported

AI Visual Tracking: Supported

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Computing & SDK →

Computing & SDK

SDK: C++ and Python with open low-level and high-level control APIs

Simulation: Isaac Gym environment provided

Secondary Development: Supported

Connectivity: Wi-Fi, 4G, Bluetooth, App control

OTA Updates: Supported

AI Computing 40 TOPS onboard compute (Orin Nano)

Capabilities

Autonomous Navigation & SLAM

Integrated LiDAR and SLAM navigation let the Sentinel map its surroundings and plan paths autonomously in real time, with onboard 40 TOPS compute handling localization and sensor fusion for fully untethered operation. Visual following and bidirectional obstacle avoidance underpin safe movement around people.

Open SDK & Sensor Access

C++ and Python SDKs expose high-level motion control and low-level joint interfaces, while the Enhanced Camera + LiDAR SDK provides unified access to visual, depth, and LiDAR data streams — covering speed control, gait switching, raw IMU, and MIT-style torque commands.

Isaac Gym Simulation

An Isaac Gym simulation environment is provided out of the box, enabling reinforcement learning experiments and motion algorithm validation before deployment on the physical robot. Motion and navigation application examples are also included to accelerate research workflows.

Modular Payload and Sensor Expansion

A modular camera mount and multiple expansion interfaces allow researchers to attach additional sensors and custom payloads to the platform. This makes the Explorer compatible with a wide range of experimental configurations and development needs.

Use Cases & Application Scenarios

Teaching & Research — Autonomy & SLAM

Extending the perception tier, the Sentinel configuration adds the full autonomy stack for the most advanced coursework and research: real-time mapping, localization, path planning, and inspection-algorithm development on live hardware. It closes the gap between perception and action, letting researchers validate complete autonomous-navigation pipelines end to end.

Autonomous Inspection

Built for untethered, unsupervised operation. With onboard mapping and navigation, Sentinel patrols campus corridors, laboratories, and outdoor routes on scheduled or on-demand routines — collecting environmental and safety data without an operator and supporting practical smart-security and facilities applications.

Autonomous Guided Tours

Beyond reactive engagement, Sentinel navigates a space on its own while interacting with people — planning paths through museums, campuses, and exhibitions and guiding visitors through voice, demonstrating a complete applied-autonomy scenario in a public-facing form.