Active suspension is now a well-tried technology in road vehicles. It has been installed on a HMMV and demonstrated to significantly improve performance in rough road conditions. This capability presents an opportunity for improved mobility in off-road conditions. The challenge is to devise a means of translating the desired trajectory of the vehicle into commands to the suspension actuators and the traction motors in an optimal, or near optimal manner. In this paper we describe part of a software architecture that was developed to enable such performance from a six-wheeled vehicle with active suspension and independent wheel drives. The vehicle was a concept developed under the DARPA Unmanned Ground Combat Vehicle Program.
The unmanned ground compat vehicle (UGCV) design evolved by the SAIC team on the DARPA UGCV Program is summarized in this paper. This UGCV design provides exceptional performance against all of the program metrics and incorporates key attributes essential for high performance robotic combat vehicles. This performance includes protection against 7.62 mm threats, C130 and CH47 transportability, and the ability to accept several relevant weapons payloads, as well as advanced sensors and perception algorithms evolving from the PerceptOR program. The UGCV design incorporates a combination of technologies and design features, carefully selected through detailed trade studies, which provide optimum performance against mobility, payload, and endurance goals without sacrificing transportability, survivability, or life cycle cost. The design was optimized to maximize performance against all Category I metrics. In each case, the performance of this design was validated with detailed simulations, indicating that the vehicle exceeded the Category I metrics. Mobility metrics were analyzed using high fidelity VisualNastran vehicle models, which incorporate the suspension control algorithms and controller cycle times. DADS/Easy 5 3-D models and ADAMS simulations were also used to validate vehicle dynamics and control algorithms during obstacle negotiation.
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