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MEbots: Integrating a RISC-V Virtual Platform with a Robotic Simulator for Energy-aware Design

Giovanni Pollo, Mohamed Amine Hamdi, Matteo Risso, Lorenzo Ruotolo, Pietro Furbatto, Matteo Isoldi, Yukai Chen, Alessio Burrello, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari, Sara Vinco

TL;DR

MEbots addresses the missing link between timing/power-aware virtual platforms and environment-aware robotics simulators by integrating a RISC-V VP (MESSY) with Webots. The framework enables holistic, energy-aware co-simulation of electronics in dynamic environments, supporting design space exploration and policy evaluation before hardware prototyping. Through a Crazyflie nano-UAV gate-traversal case study, MEbots demonstrates how battery choice, weight, and adaptive flight policies influence energy efficiency and mission performance, highlighting the value of environment modeling for design optimization. The approach is open-source and generalizable, offering a practical path to accelerate hardware/software co-design in autonomous systems.

Abstract

Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies.

MEbots: Integrating a RISC-V Virtual Platform with a Robotic Simulator for Energy-aware Design

TL;DR

MEbots addresses the missing link between timing/power-aware virtual platforms and environment-aware robotics simulators by integrating a RISC-V VP (MESSY) with Webots. The framework enables holistic, energy-aware co-simulation of electronics in dynamic environments, supporting design space exploration and policy evaluation before hardware prototyping. Through a Crazyflie nano-UAV gate-traversal case study, MEbots demonstrates how battery choice, weight, and adaptive flight policies influence energy efficiency and mission performance, highlighting the value of environment modeling for design optimization. The approach is open-source and generalizable, offering a practical path to accelerate hardware/software co-design in autonomous systems.

Abstract

Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies.

Paper Structure

This paper contains 20 sections, 4 figures, 1 table.

Figures (4)

  • Figure 1: MEbots architecture: the blue boxes highlight the components involved in the connection between a HW-SW VP (MESSY with the GVSoC ISS, left) with a robotic simulator (Webots, right).
  • Figure 2: Example of MESSY and Webots exchanging messages.
  • Figure 3: Consumed State of Charge at different cruising speed and with different batteries when considering actual battery weight (full color) or the weight of the heaviest battery (shadows), to isolate the impact of capacity.
  • Figure 4: Trajectory comparison with standard Bitcraze’s battery at different fixed and adaptive speeds.