TABLE 1.

Practical and conceptual barriers in swarm robotics, and their corresponding enablers.

Category	Barrier	Key enabler
Practical	Outdated platforms with limited sensing, actuation, and computation	Develop modern research platforms with enhanced sensors and computing capabilities
	Simulator limitations and deployment gap	Apply pseudo-reality testing, hardware-in-the-loop validation, and platform generalization techniques
	Poor integration of SLAM, vision, and communication	Embed advanced SLAM, vision, and communication stacks in new standard platforms
	Regulatory, ethical, and trust-related concerns	Promote transparency, human-swarm trust, and early engagement with regulators
Conceptual	Rigid adherence to canonical swarm properties	Rethink the paradigm: allow hybrid or leader-guided designs while preserving decentralization
	Unverified assumptions about swarm properties	Introduce formal validation, empirical testing, and standardized performance metrics
	Isolationist mindset (self-contained swarms only)	Reposition swarms as task enablers or data providers within broader multi-agent systems
	Overlooked aspects (e.g., navigation strategies, heterogeneity, security)	Prioritize these topics to enable richer, more realistic applications and robust deployments