Hierarchical Navigation Architecture and Robotic Arm Controller for a Sample Return Rover

Abstract

This work presents a hierarchical navigation architecture and cascade classifier for sample search and identification on a space exploration rover. A three tier navigation architecture and inverse Jacobian based robot arm controller are presented. The algorithms are implemented on AERO, the Autonomous Exploration Rover, participating in the NASA Sample Return Robot Centennial Challenge in 2013 and initial results are demonstrated.

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

“…In the context of mobile robot navigation, bipartite hierarchical systems often consist of a global planner that generates paths with prior knowledge of the environment, and a local planner that reacts to the local information obtained from the real-time sensors [4]. They leverage the advantages of different planners while offsetting their limitations, and have long been used in robotics, as the hierarchical structure leads to higher fault tolerance and increased robustness [8]- [11]. The global planner, which runs at a lower rate and considers all known map information to provide a global path from a given start pose to a goal pose, can be any applicable planner [12].…”

“…There is a trade-off between tuning for task completion versus safety. Figure 10 shows the effect of varying the parameter r min in (8). For lower values there are less aborts or time outs, but at the cost of increased collisions.…”

Kinova Modular Robot Arms for Service Robotics Applications

“…Kinova's products are used in many applications and research work such as mobile manipulation (Dimitrov et al, 2013;Allan and Beaudry, 2014;Fathzadeh et al, 2014), computer vision algorithms (Jiang et al, 2013), human-robot interaction (Kondaxakis et al, 2014;Leroux et al, 2013), artificial intelligence (Lampe and Riedmiller, 2013), teleoperation (Bidwell et al, 2014), assistance (Cavallo et al, 2014;Maheu et al, 2011), medical applications (Entsfellner et al, 2012;Morse et al, 2013;Entsfellner et al, 2013), planning (Eich et al, 2014), tactile sensing (Lee et al, 2013), control interfaces (Bougrain et al, 2013;Lampe et al, 2014;Bassily et al, 2014;Ianov et al, 2013;Fall et al, 2015) and kinematics (Gosselin and Liu, 2014). Figure 19 shows an integration of JACO at Worcester Polytechnic Institute (WPI) (Dimitrov et al, 2013). The mission of the CARE (Cyber Physical Systems for Advanced Response to Epidemics) project team at WPI is to deliver a safe, reliable, and intuitive emergency treatment unit to facilitate a higher degree of safety and situational awareness for medical staff, leading to an increased level of patient care during an epidemic outbreak in an unprepared, underdeveloped, or disaster stricken area.…”