## 4 Challenges in Industrial Robot Control  
* Scalability: The high computational demands of AI-based controllers limit deployment in small-scale industries. [PERSON] et al. [45] reported a \(\sim\)40% higher processing load for multi-agent DRL versus centralized heuristics. [PERSON] et al. [72] highlighted a \(\sim\)30% scalability gap in autonomous process control (see also [PERSON] et al. [40] for ROS/DRL deployment constraints). [PERSON] et al. [30] and [PERSON] et al. [31] found 15-20% additional compute overheads in additive manufacturing (AM) control workflows. Quantum-inspired acceleration could mitigate compute by up to \(\sim\)25% in simulation studies [20], but practical deployment remains constrained by specialized hardware (cryogenic/photonic) and advanced network protocols that are rarely available in conventional industrial environments. * Energy Efficiency: Power-intensive algorithms can undermine sustainability goals. [PERSON] et al. [63] observed up to a \(\sim\)25% energy overhead for DRL motion planning relative to cubic spline trajectories. [PERSON] et al. [80] identified a \(\sim\)20% efficiency loss in 5G robotic communications under round-robin URLLC scheduling. Bio-inspired end-effector approaches [19] cut energy by \(\sim\)18% vs. motor-driven parallel-jaw grippers, and soft/continuum pressure-adaptive control [74] reduced power by \(\sim\)15% vs. proportional pressure control, though broader scaling and cost remain hurdles [73; 81; 82]. * Safety: Ensuring fail-safe human-robot collaboration remains unresolved. [PERSON] et al. (2017) documented a ~15% gap in real-time safety validation beyond ISO 10218 static-zone assumptions. [PERSON] (2018) reported a ~10% residual collision risk versus tactile-free impedance control. [PERSON] et al. (2019) identified ~20% safety verification gaps in multi-robot systems. Tactile and multi-modal sensing can reduce collision risk by ~20% ([PERSON] et al., 2018; [PERSON] et al., 2019), yet real-time integration and standardization lag behind adoption. * Cybersecurity: [PERSON] et al. (2018) measured ~20% exploitable vulnerability in networked robots under deception attacks, aggravated by 5G exposure. [PERSON] and [PERSON] (2018) still observed a ~15% successful attack penetration despite AI-augmented defenses. Digital twin cyber ranges reduced exploitable vectors by ~15% in production cells ([PERSON] et al., 2018), but full resilience depends on standardized, interoperable security protocols spanning the edge, network, and controller layers ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). * Ethical Deployment: [PERSON] and [PERSON] (2016) warn of a ~20% higher risk exposure in the absence of formal ethical frameworks. [PERSON] et al. (2017) identify ~10% trust gaps in HRC adoption. [PERSON] and [PERSON] (2017) report ~12% autonomy challenges; these socio-technical shortfalls compound the operational risk in Industry 5.0 unless mitigated through governance, training, and inclusive design ([PERSON] et al., 2018). * Complexity in Additive Manufacturing: [PERSON] et al. (2018) note ~15% added real-time monitoring complexity in high-resolution AM cells; [PERSON] et al. (2018) report a ~20% design optimization burden when lightweight AM tooling is introduced ([PERSON] et al., 2018); [PERSON] et al. (2018) highlight ~10% prediction accuracy gaps that limit large-scale AM/robot integration. To deepen this analysis, Figure 5 cross-maps how five key enablers--AI/DRL, digital twins, 5G URLLC, quantum-inspired optimization, and bio-inspired control--address the six outstanding challenges above. Each colored flow inherits the fill color of its technology node; the line thickness encodes evidence maturity (thick = mature, industry-validated; thin = limited/prototype). This visual grammar lets R&D managers see both who addresses what and with what level of confidence, supporting investment and adoption roadmaps.