Spiking neural network-based energy-efficient framework for real-time robotic arm manipulation

In industrial applications, robotic arms are widely utilized, posing an important challenge in achieving precise positioning. Traditional solutions, such as inverse kinematics and polynomial trajectory generation, prove computationally costly and time-consuming for real-time control applications. This work proposes a bio-inspired spiking neural network (SNN) for controlling a 3-degree-of-freedom robotic arm without explicit pre-planning. Simulating spiking neurons using the leaky integrate-and-fire model balances biological realism with computational efficiency. The work includes tests with extreme targets, random coordinates, and practical experiments with robot arm hardware to cover the working area thoroughly. Numerical experiments confirm the efficiency of the proposed SNN, which solves the inverse kinematics in just 1.50 ms, compared to 20.15 ms for artificial neural network (ANN), when executed on the computational hardware platform with an Intel Core i7 central processing unit (2.10 Gigahertz) and 16 Gigabytes of Random Access Memory. The reduction in time highlights SNN’s potential to optimize computational complexity, which further enhances the overall performance of robotic arm manipulators. Furthermore, across 3rd, 5th, and 7th order trajectories, the SNN consistently results in Mean Squared Error (MSE) for trajectory generation (0.0046–0.0900) and for inverse kinematics (0.3245–1.2474) as compared to ANN (0.5990–2.2320) and (1.5015–2.4870), respectively, confirming improved performance in both trajectory planning and inverse kinematics.