Introduction
The thought of fully unmanned ground vehicles fascinates everyone with an interest in robotics and automation technologies. Governments, corporations, and individuals have been pursuing various technologies that could see the dream of self-driven robots taking over critical roles, such as warfare in rough terrains and dangerous environments. Emerging technologies could soon lead to exemplary designs featuring advanced artificial intelligence (AI) capabilities, which could give future autonomous vehicles unaided decision-making capabilities. Essentially, the incorporation of carefully developed AI systems in crewless land vehicles designed for the DARPA Grand Challenge could be a vital supplement to the 2005 designs, as it could yield autonomous vehicles capable of functioning intelligently with greater precision.
Description of the Vehicle Design
The teams behind the autonomous designs featured in the 2005 DARPA Challenge were limited to using GPS, the only signal that could be transmitted or received by the vehicle. The requirement was that the vehicles be completely autonomous and free from any external human assistance throughout the race (Defense Advanced Research Projects Agency, 2004). Notably, designers used adaptive vision technologies and computer codes to define the route. Stanford Racing Team’s vehicle, dubbed Stanley, finished the 132-mile race in less than seven hours, taking the first position (Stanford Racing Team, 2005). The vehicle featured a video camera, light detection and ranging (LIDAR) laser sensors, and a GPS system, which were incorporated to aid navigation through the desert terrain route (The great robot race, 2006). Possibly, Stanley could have performed much better if designers used generative software to give the vehicle an increased capacity to avoid collisions.
Recommended Technology Upgrades
The technologies available for designers in 2005 were just sufficient for the design of vehicles that could maneuver through predetermined routes with minimal obstacles. The terms of the challenge included the requirement that vehicles have all intelligence, computing, and sensor-processing systems needed for navigation onboard (DARPA, 2004). Similar to the other vehicles in the race, Stanley encountered challenges in maneuvering around obstacles. Teams had incorporated route information in the onboard navigation systems, meaning that a slight deviation or change of route could be detrimental. Hence, it would be recommendable to incorporate google maps in the Stanley design for an easy and precise maneuver and navigation. Besides, the incorporation of a generative code or software to aid Stanley’s navigation around unexpected barriers could have been a critical technology upgrade.
Benefits of the Recommended System
An intelligent system with the capability to detect changes or deviations in the terrain and unexpected obstacles would be possible with regenerative software. Software with a generative code could boost the crewless vehicle’s ability to determine when and where to accelerate with greater precision. The vehicle would be able to respond to all obstacles, including those that might be introduced in the route after the map is incorporated in the onboard intelligence systems. However, navigation systems used in the 2005 Stanley design, particularly LIDAR sensors, would require replacement with relatively more precise infrared detection devices. These improvements would culminate in improved performance, as the vehicle would sense obstacles more precisely and make appropriate decisions fast and efficiently.
Conclusion
In overview, it is apparent that governments and corporations are continuously pursuing the idea of full autonomous ground vehicles. The DARPA Grand Challenge is an important manifestation of these efforts, which allows enthusiasts to showcase their innovations. Notably, the outcomes of these challenges influence the progress realized in the robotics and automation industry. For instance, the Stanford Racing Team would not hesitate to incorporate google maps in their vehicle had they been designing Stanley, the 2005 winner, at this time. Designers and developers must consistently explore advanced AI systems to develop safe and reliable autonomous vehicles that surpass human performance.
References
Defense Advanced Research Projects Agency (2004). DARPA Grand Challenge-2005 Rules. [PDF document]. Web.
Stanford Racing Team. Stanford racing team’s entry in the 2005 DARPA grand challenge. [PDF document] Web.
The great robot race [Film] (2006). The Public Broadcasting Services.