INDEX

Mobile Robotics: Autonomy Solution Providers

Autonomy Solution Providers (ASPs) refer to a subset of technology providers who provide navigation and mobility solutions to a range of vehicles, and are not tied to one platform. These companies focus on developing sensors, sensor fusion, machine vision, AI, and analytics to retrofit onto various platforms. This separates them from traditional mobile robot providers (MRPs) who integrate their entire technology stack into one specialized robotic platform.

Mobile Robotics are being developed at an accelerating pace, with intralogistics and material handling in warehousing and manufacturing being the main use-cases. In this report, ABI Research provides analysis and competitive assessment of ASPs, who have managed to drive robotics adoption beyond traditional environments. As companies, the range and extent of technologies they incorporate into their solution can be varied. Some have auxiliary AI services, simulation, and SDKs, while others are more specialized in a particular sensor solution or a solution tied to a specific pain point, like motion planning. Across different verticals, these companies are becoming increasingly important in the mobile robotics space and have served to empower vehicle OEMs in accelerating their deployment of autonomous vehicles. 

 

Table of Contents

  • 1. EXECUTIVE SUMMARY
  • 2. PLATFORM-AGNOSTIC NAVIGATION FOR ROBOTS
    • 2.1. What Is Platform Agnosticism?
    • 2.2. Taxonomy for Autonomy Solution Providers
    • 2.3. The Mobile Robotics Revolution
  • 3. OEM-AGNOSTIC AUTONOMY SOLUTION PROVIDERS IN THE MOBILE ROBOT SPACE
    • 3.1. Autonomous Solutions Inc.
    • 3.2. Balyo
    • 3.3. BlueBotics
    • 3.4. Brain Corp
    • 3.5. Canvas Technology
    • 3.6. Clearpath Robotics
    • 3.7. Movel AI
    • 3.8. PerceptIn
    • 3.9. Realtime Robotics
    • 3.10. RoboCV
    • 3.11. Seegrid
    • 3.12. Slamtec
  • 4. TECHNOLOGIES FOR MOBILITY
    • 4.1. Localization and Mapping / SLAM
    • 4.2. Location Tracking
    • 4.3. Navigation
    • 4.4. Sensors and Sensor Fusion
    • 4.5. Deep Learning-Enabled Machine Vision
    • 4.6. Fleet Management and Analytics
    • 4.7. Vehicle Hardware and Design
  • 5. DRIVERS AND OPPORTUNITIES
    • 5.1. Macro-Level Automation
    • 5.2. Delegation to Proprietary Systems for Specific Tasks
    • 5.3. Interaction with Open SOurce OS
  • 6. CHALLENGES
    • 6.1. Ability of OEMs to Compete
    • 6.2. System Integration and Deployment Challenges
    • 6.3. Open Source Competition
    • 6.4. Are Navigation, Localization, and Mobility Enough?
    • 6.5. Maintaining a Unique Value Proposition
    • 6.6. Platform Commoditization Not Likely
  • 7. COMPETITIVE ASSESMENT
    • 7.1. Methodology
    • 7.2. Findings
    • 7.3. Four Key Players
  • 8. KEY RECOMMENDATIONS
    • 8.1. Put Mobile Manipulation and Other Hardware-Related Problems into Your Long-Term Strategic Roadmap
    • 8.2. Find Ways to Work with Open Source Robotics Platforms
    • 8.3. Consider Teaming Up with UWB and Microlocation Providers
    • 8.4. Build the Necessary Platform for RaaS and Value-Added Solutions
    • 8.5. Develop Wireless Charging Solution
    • 8.6. Hardware and OEM's Will Remain Crucial