23 December 2025 | Interaction | By editor@rbnpress.com
As autonomous robots, mobile platforms, and drone swarms move beyond pilot programs into complex, real-world deployments, the limitations of traditional wireless infrastructure are becoming increasingly apparent. Reliable, low-latency, and secure communication is no longer a supporting technology—it is a foundational requirement for scaling physical AI. In this interview with Robotics Business News, Amol Parikh, Co-CEO of Doodle Labs, shares insights on why networking has become the defining constraint—and competitive advantage—for autonomous systems operating at scale, from warehouse fleets and humanoid robots to large-scale drone swarms in contested environments.
1. Autonomous robots and drone swarms depend on continuous, real time data exchange. In your view, how has wireless connectivity become a foundational constraint or competitive advantage for scaling autonomous systems?
As autonomous systems move from pilot programs into real-world deployments, wireless connectivity can become both a foundational constraint and a clear competitive advantage. Many robots and drone swarms perform well in lab conditions, but struggle to scale once they face interference, mobility, and shared spectrum. In these environments, unreliable connectivity directly limits performance and safety.
The primary technical challenge in scaling autonomy is networking. Autonomous systems depend on continuous, real-time data exchange to collaborate and operate cohesively. Applications such as drone swarms, warehouse automation, and mobile robotics require highly reliable wireless performance outside controlled settings. Organizations that design connectivity as core infrastructure are able to move beyond demonstrations and deploy autonomous systems at scale.
2. Many robotics and drone deployments operate in GPS denied, congested, or infrastructure limited environments. How do mesh networking architectures and advanced radios overcome these limitations compared to traditional communication models?
Many robotics and drone deployments operate in GPS-denied, congested, or infrastructure-limited environments where traditional communication models break down. Those models assume fixed infrastructure, stable topology, and predictable spectrum access. Mesh networking replaces those assumptions with a “bring your network with you” approach, where each robot or drone becomes part of the network itself and can dynamically route traffic as conditions change.
As more nodes are added, the network becomes more robust rather than more fragile. Mesh architectures can form relays around physical obstructions such as buildings, trees, or terrain, and continue operating without pre-installed infrastructure. Advanced radios further strengthen this model by supporting adaptive spectrum use and anti-jamming capabilities, enabling reliable communication in congested or contested environments across both defense and commercial applications.
3. From your experience, what are the most significant differences in connectivity requirements between defense applications and commercial or industrial robotics use cases?
Defense and commercial robotics place different primary demands on connectivity, driven by the environments they operate in. Defense applications are built with the assumption that communications will be contested. Jamming is the dominant constraint, and systems must support secure, encrypted, long-range links while managing large swarms at scale. Connectivity is expected to degrade gracefully while maintaining mission-critical command, control, and coordination.
Commercial and industrial robotics operate in less adversarial environments, but place strong emphasis on safety, determinism, and reliability. These systems must meet regulatory requirements, deliver high throughput with extreme uptime, and include redundancy to ensure safe operation in populated areas. Interference avoidance in shared or unlicensed spectrum is often a bigger concern than long-range performance.
The gap between these requirements is narrowing. As commercial autonomy moves outdoors and into public or remote environments, it inherits many of the same connectivity challenges long addressed in defense systems. As a result, technologies developed for fast, secure, and resilient military communications are increasingly relevant for commercial and industrial robotics at scale.
4. As the industry moves from individual robots to coordinated fleets and swarms, what are the biggest technical and operational challenges in maintaining reliable, low latency, and secure communications at scale?
As the industry moves from individual robots to coordinated fleets and swarms, maintaining reliable communications becomes more complex. Doodle Labs exists to solve these exact challenges that emerge around communications at scale. Small fleets may function on fragile networks, but as node counts and operating distances increase, issues such as routing instability, congestion under bursty traffic, and timing drift begin to break coordinated behavior at the system level.
At scale, latency and security become especially unforgiving. Coordinated fleets depend on tightly bounded delays, not just average throughput, and variable latency can disrupt real-time coordination even when links appear healthy. Security must also scale across many mobile nodes without creating bottlenecks or single points of failure, while remaining resilient to interference and jamming.
To address this, Doodle Labs designs intelligence directly into the network. Our systems support true drone swarms, large-scale warehouse mesh networks, and AMR fleets operating over tens of kilometers. With built-in compute and proprietary mesh software, the network autonomously manages routing, prioritization, latency, and recovery, enabling reliable, low-latency, and secure communications for physical AI at any scale.
5. With humanoid robots and mobile autonomous platforms entering factories and warehouses, how important is deterministic and secure wireless communication to enabling safe human robot collaboration?
Deterministic and secure wireless communication is foundational to safe human-robot collaboration in factories and warehouses. When humanoid robots and mobile platforms operate near people, they cannot lose connectivity while operational. Delayed or dropped messages are not just performance issues but real safety risks, especially given the physical mass of humanoid robots and the potential consequences of a fall or loss of control.
Safety-critical functions such as emergency stops, proximity awareness, task coordination, and remote operator takeover depend on guaranteed message delivery within strict time bounds. If the network cannot provide that level of determinism, systems are forced into overly conservative behaviors that limit productivity and slow adoption. In practice, safety and efficiency rise and fall together based on communication predictability.
Security is equally essential in these environments. Only trusted devices must be allowed to participate in control loops, and commands must be protected from interference or spoofing. As deployments expand from small, controlled settings into larger warehouses and eventually broader environments, communication redundancy and multiple network paths become key enablers of continuous, safe operation.
6. Looking ahead as a leader in this space, where do you see the largest growth opportunities for autonomous system connectivity over the next three to five years, and what risks should the industry be most mindful of?
The largest growth opportunities over the next three to five years are in autonomous systems operating beyond controlled environments. This includes outdoor industrial automation, large-scale inspection and logistics, defense drone deployments, warehouse robotics, humanoids, and mixed human-robot teams. As physical AI becomes more deeply integrated and millions of connected devices come online, connectivity will be the foundation that enables fleets and swarms to move, sense, and act together rather than operate in isolation.
These use cases demand networking that supports mobility, scale, and spectrum flexibility without reliance on fixed infrastructure. Coordinated autonomy depends on systems communicating and collaborating in real time, enabling quiet, harmonious operation instead of chaotic behavior. Cloud-based AI and edge intelligence will increasingly tie into physical robots, making reliable, adaptive connectivity essential to coordination across environments and industries.
The primary risk for the industry is underestimating real-world wireless complexity. Designs based on narrow spectrum assumptions, centralized architectures, or lab conditions tend to break down at scale. Connectivity is not a component choice but a long-term platform decision that shapes performance, security, and upgrade paths for years. Treating connectivity with the same rigor as autonomy, sensing, and compute is critical to scaling autonomous systems safely and reliably.
