The Nemesis tracked vehicle platform is a differentially driven Humanitarian Demining tractor developed by Applied Research Associates, Inc. The vehicle is capable of teleoperational control and is outfitted with a sensor suite used for detecting and neutralizing landmines. Because the detection process requires the vehicle to travel at speeds less than 0.5 km/h, teleoperation is a tedious process. The added autonomous capabilities of waypoint navigation and obstacle avoidance could greatly reduce operator fatigue. ARA chose to leverage Virginia Tech's experience in developing an autonomous mobility capability for the Nemesis platform. The resulting algorithms utilize the waypoint navigation techniques of Virginia Tech's JAUS (Joint Architecture for Unmanned Systems) toolkit, and a modified version of the Dynamic Expanding Zones (DEZ) algorithm developed for the 2005 DARPA Grand Challenge. The modified approach discretizes the perception zones of the DEZ algorithm and provides the added capability of obstacle memory, resulting in the Discretized Dynamic Expanding Zones with Memory (DDEZm) algorithm. These additions are necessary for efficient autonomous control of the differentially driven Nemesis vehicle. The DDEZm algorithm was coded in LabVIEW and used to autonomously navigate the Nemesis vehicle through a waypoint course while avoiding obstacles. The Joint Architecture for Unmanned Systems (JAUS) was used as the communication standard to facilitate the interoperability between the software developed at Virginia Tech and the existing Nemesis software developed by ARA. In addition to development and deployment, the algorithm has been fully documented for embedded coding by a software engineer. With embedded implementation on the vehicle, this algorithm will help to increase the efficiency of the landmine detection process, ultimately saving lives. / Master of Science

Autonomous navigation of mobile robots through unstructured terrain presents many challenges. The task becomes even more difficult with increasing obstacle density, at higher speeds, and when a priori knowledge of the terrain is not available. Reactive navigation schemas are often dismissed as overly simplistic or considered to be inferior to deliberative approaches for off-road navigation. The Potential Field algorithm has been a popular reactive approach for low speed, highly maneuverable mobile robots. However, as vehicle speeds increase, Potential Fields becomes less effective at avoiding obstacles. The traditional shortcomings of the Potential Field approach can be largely overcome by using dynamically expanding perception zones to help track objects of immediate interest. This newly developed technique is hereafter referred to as the Dynamic Expanding Zones (DEZ) algorithm. In this approach, the Potential Field algorithm is used for waypoint navigation and the DEZ algorithm is used for obstacle avoidance. This combination of methods facilitates high-speed navigation in obstacle-rich environments at a fraction of the computational cost and complexity of deliberative methods. The DEZ reactive navigation algorithm is believed to represent a fundamental contribution to the body of knowledge in the area of high-speed reactive navigation. This method was implemented on the Virginia Tech DARPA Grand Challenge vehicles. The results of this implementation are presented as a case study to demonstrate the efficacy of the newly developed DEZ approach. / Master of Science