The excerpt note is about the grand challenges of robot navigation and exploration in extreme environments in the next 5 to 10 years according to the latest paper published in Science Robotics (Yang et al., Sci Robotics 2018).

Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R. and Nelson, B.J., 2018. The grand challenges of Science Robotics. Science Robotics, 3(14), p.eaar7650.

Yang et al., 2018 conducted an open online survey on major unsolved challenges in robotics. There are 10 grand challenges that may have major breakthroughs, significant research, and / or socioeconomic impact in the next 5 to 10 years. These grand challenges includes:

• New materials and fabrication schemes;
  
• Biohybrid and bioinspired robots;
  
• New power sources, battery technologies, and energy-harvesting schemes for long lasting operation of mobile robots;
  
• Robot swarms;
  
• Navigation and exploration in extreme environments;
  
• Fundamental aspects of artificial intelligence (AI) for robotics;
  
• Brain-computer interfaces;
  
• Social Interaction;
  
• Medical robotics;
  
• Ethics and security;
  

The following is about the challenges of autonomous navigation and exploration in extreme environments.

Path planning, obstacle avoidance, localization, and environment mapping are ubiquitous requirements of robot navigation and exploration. Advances in sensing, machine vision, and embedded computation have underpinned the remarkable progress of autonomous vehicles roaming complex terrains at speed, drones forming swarms for completing collaborative tasks, and surgical robots delivering targeted therapy while negotiating complex, delicate anatomical structures. Many robots we deploy are intrinsic explorers that we send to the far reaches of the planet—the deep oceans, under the Arctic ice pack, into volcanoes—and go where no human has yet tread, often under unknown and extreme conditions. The associated challenges are therefore much greater than those encountered today.

Foremost, the robots must operate in environments that are not only unmapped, but, at times, their very nature is not understood. Adding to this are challenges associated with communications and navigation. Robots in tunnels or mines must cope with rough terrain, narrow passageways, and degraded perception. Robots undertaking nuclear decommissioning must withstand radiation and restricted access, and those used to construct and assemble infrastructure must be able to resist chemicals and materials used in the construction process as well as being resistant to dirt, dust, and large impact forces. Undersea robots operate in an environment where radio does not penetrate and our usual forms of communication and navigation disappear; untethered undersea vehicles must be autonomous. As robotic spacecraft take on tasks like roaming distant planetary surfaces and, in the not-so-distant future, possibly landing on the icy moons of the outer planets, they enter a regime where long latency and low bandwidths of communications not only greatly reduce productivity but also put the survival of the robot itself at risk.

Undoubtedly, current mapping and navigation techniques will continue to evolve. For example, techniques such as SLAM (simultaneous localization and mapping) will go beyond the current rigid and static assumptions of the world and will effectively deal with time-varying, dynamic environments with deformable objects (60). With resource constraints, specific challenges include how to learn, forget, and associate memories of scene content both qualitatively and semantically, similar to how human perception works; how to surpass purely geometric maps to have semantic understanding of the scenes; how to reason about new concepts and their semantic representations and discover new objects or classes in the environment through learning and active interactions; and how to evolve through online, prospective, and lifelong continuous learning.

For navigation, the grand challenge is to handle failures and being able to adapt, learn, and recover (Fig. 6). For exploration, it is developing the innate abilities to make and recognize new discoveries. From a system perspective, this requires the physical robustness to withstand harsh, changeable environments, rough handling, and complex manipulation. The robots need to have significant levels of autonomy leading to complex self-monitoring, self-reconfiguration, and repair such that there is no single point of complete failure but rather graceful system degradation. When possible, solutions need to involve control of multiple heterogeneous robots; adaptively coordinate, interface, and use multiple assets; and share information from multiple data sources of variable reliability and accuracy.

For further info, please read the Yang et al., 2018.

Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R. and Nelson, B.J., 2018. The grand challenges of Science Robotics. Science Robotics, 3(14), p.eaar7650.