AgileX COBOT KIT is a ROS mobile manipulator kit combining LiDAR SLAM, depth vision, and a collaborative arm for autonomous navigation and grasping.
AgileX COBOT KIT is a mobile manipulation development platform (often described as an “autonomous cobot kit”) designed for robotics education, research, and application prototyping. It combines a mobile robot base with an integrated perception stack (typically multi-beam LiDAR and a depth camera), onboard computing, and a collaborative robotic arm plus gripper—delivering an end-to-end testbed for navigation, mapping (SLAM), perception, and pick-and-place tasks. AgileX positions the COBOT KIT as part of its ROS-based research ecosystem, emphasizing faster setup for common mobile-manipulation workflows such as mapping, localization, motion planning, and autonomous grasping.
COBOT KIT is commonly presented as a modular “kit” architecture rather than a single fixed robot. Published configurations pair AgileX mobile chassis with a 6-degree-of-freedom (6-DOF) collaborative arm and an adaptive electric gripper, plus sensors and a compute unit. AgileX marketing materials highlight “quick mapping” from 3D point cloud data and simplified integration—aimed at reducing the engineering overhead of bringing LiDAR, navigation, and manipulation together in one platform.
A typical COBOT KIT bundle (as described in a widely circulated user manual) includes a tracked AgileX chassis, an industrial PC, multi-beam LiDAR, a depth camera, a display, and a 4G router, alongside the robot arm and gripper. In distributor listings, the kit is also described as supporting multiple AgileX chassis options to match indoor/outdoor scenarios and different payload and mobility requirements.
AgileX’s current product page for COBOT KIT highlights performance figures including 1.5 m/s speed, ~80 minutes runtime, and 35 kg payload (for the mobile platform), indicating a configuration optimized for research and mobile manipulation demonstrations.
In contrast, the user manual’s example configuration is based on an AgileX BUNKER tracked chassis, listing (for that chassis) an overall size of 1000 × 750 × 360 mm, payload up to 70 kg, speed 0–1.5 m/s, and a 48 V / 30 Ah lithium battery system.
Interpretation (configuration variability): These differing figures are consistent with COBOT KIT being offered in multiple variants and chassis pairings; payload, runtime, and dimensions can vary substantially depending on the underlying base and accessories.
AgileX describes COBOT KIT as supporting collaborative arms such as AUBO i5 or xArm6, with published working-radius figures around 819–886 mm in marketing copy, and an emphasis on close human–robot interaction “without a safety fence” (a typical attribute of collaborative robot deployments when properly risk-assessed).
In the manual’s AUBO-i5 example, the arm is listed as a 6-DOF collaborative robot with 5 kg payload, ~924 mm reach, and ±0.02 mm repeat positioning accuracy, supporting interfaces such as Ethernet/Modbus and SDK/ROS integration.
AgileX’s COBOT KIT marketing highlights adaptive electric gripping with a stated 160 N maximum grasping force and ~0.03 mm-class positioning precision, designed to conform to objects using multiple contact points.
The user manual’s sample configuration includes a DH AG-series gripper and notes 0.03 mm finger repeat positioning accuracy, alongside multiple communication options (e.g., TCP/IP, USB, RS485, I/O, CAN; with EtherCAT listed as optional).
COBOT KIT is built around 3D perception for navigation and grasping. The manual specifies an Intel RealSense D435 depth camera and a Velodyne VLP-16 multi-line LiDAR in the example bill of materials.
AgileX’s product messaging emphasizes using multi-beam LiDAR point clouds for mapping and navigation workflows inside ROS. Distributor materials frequently describe 16-channel LiDAR-based “quick mapping” and list RealSense-based depth perception as part of the kit concept.
A core value proposition of COBOT KIT is pre-integration around ROS. AgileX explicitly frames “quick mapping” as a ROS-powered workflow that reduces the complexity of standing up LiDAR mapping/navigation pipelines.
The manual describes the platform as integrating “multi-line lidar automatic navigation,” MoveIt-based arm motion planning, visual identification, and automated grasping, with “all codes open-source” (as described in the manual) to lower integration difficulty for high-complexity applications. It also references common ROS tooling such as GMapping (mapping), move_base (navigation), MoveIt (manipulation planning), and supporting nodes/workflows for camera and gripper bring-up.
COBOT KIT is positioned for scenarios where mobile autonomy and manipulation must be developed together:
Robotics education and training: A single platform for teaching ROS navigation, mapping, localization, perception, and manipulation planning.
Research in mobile manipulation: Benchmarking grasping, task planning, and perception-to-action pipelines with real sensors and a real arm.
Prototyping industrial workflows: Pick-and-place trials, tool handling, or assisted material handling in controlled environments—especially where rapid integration matters.
Field-oriented experimentation (configuration-dependent): When paired with rugged AgileX bases (tracked or outdoor-capable), the platform concept is marketed for domains such as agriculture and industrial inspection-style scenarios.
Integrated “full-stack” starting point: By bundling chassis, sensors, compute, ROS interfaces, and manipulation hardware, COBOT KIT targets teams who want to focus on algorithms and applications rather than hardware bring-up.
Mobile manipulation in one platform: The combination of navigation (LiDAR + SLAM) and grasping (arm + gripper + depth camera) supports end-to-end workflows like “navigate to target → localize object → plan grasp → execute pick/place.”
Collaborative-robot ergonomics (use-case dependent): AgileX highlights cobot-style interaction without a fence in typical grasping scenarios, aligning with collaborative arm positioning and application design—subject to appropriate safety assessment in real deployments.
Open development approach: The manual describes included code and documentation intended to reduce learning curve and integration time for complex applications.
AgileX COBOT KIT is a ROS-based mobile manipulation development platform that combines a mobile base, sensors (commonly multi-beam LiDAR and a depth camera), onboard computing, and a collaborative robotic arm with an adaptive gripper to support navigation, mapping, perception, and grasping workflows.
In typical use, the robot builds or loads a map using LiDAR-based SLAM, localizes for navigation, and uses MoveIt-enabled motion planning for the arm. The depth camera supports visual perception for object detection or pose estimation, enabling autonomous or semi-autonomous pick-and-place demonstrations and research experiments.
Mobile manipulation is more complex than either navigation or grasping alone. COBOT KIT is designed to reduce integration difficulty by packaging key components (base + LiDAR + camera + arm + gripper + ROS tooling) into one platform, helping labs and developers move faster from setup to experiments and applications.
Commonly cited benefits include an integrated ROS stack for mapping/navigation, built-in support for MoveIt-based manipulation planning, a sensor suite suitable for SLAM and visual perception, and a collaborative arm/gripper combination for safe, fence-free research-style interaction in appropriate contexts.
AgileX COBOT KIT is a configurable, ROS-centered mobile manipulator kit intended to accelerate robotics R&D by integrating autonomous navigation (LiDAR + SLAM), perception (depth camera), and manipulation (6-DOF collaborative arm + adaptive gripper) into a single development platform. With variant-dependent specifications and chassis options, it is commonly used as a practical testbed for mobile grasping, mapping, and end-to-end autonomy in education, research, and early-stage industrial prototyping.
| ROBOT TYPE | COBOT |
|---|---|
| MARKA | AGILEX |
| DIO # | COBOT KIT |
