Legged robots have the ability to adapt their walking posture to navigate confined spaces due to their high degrees of freedom. However, this has not been exploited in most common multilegged platforms. This paper presents a deformable bounding box abstraction of the robot model, with accompanying mapping and planning strategies, that enable a legged robot to autonomously change its body shape to navigate confined spaces. The mapping is achieved using robot-centric multi-elevation maps generated with distance sensors carried by the robot. The path planning is based on the trajectory optimisation algorithm CHOMP which creates smooth trajectories while avoiding obstacles. The proposed method has been tested in simulation and implemented on the hexapod robot Weaver, which is 33 ,cm tall and 82 ,cm wide when walking normally. We demonstrate navigating under 25cm overhanging obstacles, through 70cm wide gaps and over 22cm high obstacles in both artificial testing spaces and realistic environments, including a subterranean mining tunnel.
@cite_5: We present a contact planner for complex legged locomotion tasks: standing up, climbing stairs using a handrail, crossing rubble, and getting out of a car. The need for such a planner was shown at the DARPA Robotics Challenge, where such behaviors could not be demonstrated (except for egress). Current planners suffer from their prohibitive algorithmic complexity because they deploy a tree of robot configurations projected in contact with the environment. We tackle this issue by introducing a reduction property: the reachability condition. This condition defines a geometric approximation of the contact manifold, which is of low dimension, presents a Cartesian topology, and can be efficiently sampled and explored. The hard contact planning problem can then be decomposed into two subproblems: first, we plan a path for the root without considering the whole-body configuration, using a sampling-based algorithm; then, we generate a discrete sequence of whole-body configurations in static equilibrium along this path, using a deterministic contact-selection algorithm. The reduction breaks the algorithm complexity encountered in previous works, resulting in the first interactive implementation of a contact planner (open source). While no contact planner has yet been proposed with theoretical completeness, we empirically show the interest of our framework: in a few seconds, with high success rates, we generate complex contact plans for various scenarios and two robots: HRP-2 and HyQ. These plans are validated in dynamic simulations or on the real HRP-2 robot.
@cite_9: 
@cite_16: While legged robots are well suited to navigating complex three-dimensional (3-D) environments, their practical applicability is still hampered by the time taken to plan manoeuvres to traverse these challenging environments. We present contact dynamic roadmaps (CDRM), which extend dynamic roadmaps with contact information. The CDRM is precomputed offline to generate a discretized mapping from each leg's workspace to its configuration space, and then adapted online to the environment to rapidly identify collision-free foothold positions. The concept behind this is to perform the expensive foothold candidate generation and collision checking phases offline and store the data for use in the online planner. The CDRM is coupled with a rapidly-exploring random tree planner to generate acyclic full-body motion plans in complex 3-D environments. The performance of the approach is validated and compared in simulation in a wide variety of scenarios that require full-body planning to successfully navigate.
For complex 3D environments such as large steps, trusses or vehicle egress, full body contact planners using random sampling such as probabilistic roadmaps (PRM) are used @cite_9 @cite_5 @cite_16 . This can be done by randomly sampling a subspace of all possible contacts limited by stability and reachability as in @cite_5 . However, this method requires accurate knowledge of the environment in advance. Short and Bandyopadhyay @cite_16 deal with this by first pre-computing a set of possible configurations based on the robot model assuming no obstacles then selecting the best configurations for a given, dynamic environment. The computational cost of these planners are highly dependant on the complexity of the terrain and the number of joints of the robot. This makes them difficult to apply to high DOF, multilegged robots.