One of the primary goals of biped locomotion is to generate and execute joint trajectories on a corresponding step plan that takes the robot from a start point to a goal while avoiding obstacles and consuming as little energy as possible. Past researchers have studied trajectory generation and step planning independently, mainly because optimal generation of robot gait using dynamic formulation cannot be done in real time. Also, most step-planning studies are for flat terrain guided by search heuristics. In the proposed method, a framework for generating trajectories as well as an overall step plan for navigation of a 12 degrees of freedom biped on an uneven terrain with obstacles is presented. In order to accomplish this, a dynamic model of the robot is developed and a trajectory generation program is integrated with it using gait variables. The variables are determined using a genetic algorithm based optimization program with the objective of minimizing energy consumption subject to balance and kinematic constraints of the biped. A database of these variables for various terrain angles and walking motions is used to train two neural networks, one for real-time trajectory generation and another for energy estimation. To develop a global navigation strategy, a weighted A* search is used to generate the footstep plan with energy considerations in sight. The efficacy of the approach is exhibited through simulation-based results on a variety of terrains.

This paper presents an approach that integrates the geometric notion of clearance (distance to the closest obstacle) into sampling-based motion planning to enable a robot to safely navigate in challenging environments. To reach the goal destination, the robot must obey geometric and differential constraints that arise from the underlying motion dynamics and the characteristics of the environment. To produce safe paths, the proposed approach expands a motion tree of collision-free and dynamically feasible motions while maintaining locally maximal clearance. In distinction from related work, rather than explicitly constructing the medial axis, the proposed approach imposes a grid or a triangular tessellation over the free space and uses the clearance information to construct a weighted graph where edges that connect regions with low clearance have high cost. Minimum-cost paths over this graph produce high-clearance routes that tend to follow the medial axis without requiring its explicit construction. A key aspect of the proposed approach is a route-following component which efficiently expands the motion tree to closely follow such high-clearance routes. When expansion along the current route becomes difficult, edges in the tessellation are penalized in order to promote motion-tree expansions along alternative high-clearance routes to the goal. Experiments using vehicle models with second-order dynamics demonstrate that the robot is able to successfully navigate in complex environments. Comparisons to the state-of-the-art show computational speedups of one or more orders of magnitude.

This paper presents the design, analysis and experimentation of a Discrete Modular Serpentine Tail (DMST). The mechanism is envisioned for use as a robotic tail integrated onto mobile legged robots to provide a means, separate from the legs, to aid stabilization and maneuvering for both static and dynamic applications. The DMST is a modular two-degree-of-freedom (DOF) articulated, under-actuated mechanism, inspired by continuum and serpentine robotic structures. It is constructed from rigid links with cylindrical contoured grooves that act as pulleys to route and maintain equal displacements in antagonistic cable pairs that are connected to a multi-diameter pulley. Spatial tail curvatures are produced by adding a roll-DOF to rotate the bending plane of the planar tail curvatures. Kinematic and dynamic models of the cable-driven mechanism are developed to analyze the impact of trajectory and design parameters on the loading profiles transferred through the tail base. Experiments using a prototype are performed to validate the forward kinematic and dynamic models, determine the mechanism's accuracy and repeatability, and measure the mechanism's ability to generate inertial loading.

The increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

Hexapod robots are well suited for disaster rescuing tasks due to their stability and load capability. However, most current hexapod robots still rely on static gaits that largely limit their locomotion speed. This paper introduces a hierarchical control strategy to realize a dynamic alternating tripod trotting gait for a hexapod robot based on multi-modal impedance control. At the low level, a position-based impedance controller is developed to realize an adjustable compliant behavior for each leg. At the high level, a new gait controller is developed to generate a stable alternating tripod trotting gait, in which a gait state machine, a leg compliance modulation strategy, and a close-looped body attitude stabilizer are imposed. As a result, the alternating tripod trotting of the hexapod robot can be synchronized as the running of a bipedal robot with stable body attitude. Moreover, this control strategy was verified by experiments on a newly designed pony-sized disaster rescuing robot, HexbotIV, which successfully achieved a dynamic trotting gait with ability to resist the disturbances of mildly uneven terrains. Our control strategy as well as the experimental study can be a valuable reference for other hexapod robots and thus paves a way to the practical deployment of disaster rescuing robots.

Autonomous multi-agent systems show promise in countless applications, but can be hindered in environments where inter-agent communication is limited. In such cases, this paper considers a scenario where agents communicate intermittently through a cloud server. We derive a graph transformation mapping the kernel of a graph's Laplacian to a desired configuration vector while retaining graph topology characteristics. The transformation facilitates derivation of a self-triggered controller driving agents to prescribed configurations while regulating instances of inter-agent communication. Experimental validation of the theoretical results shows the self-triggered approach drives agents to a desired configuration using fewer control updates than traditional periodic implementations.

If the joint clearances of the joints of a manipulator are considered, an unconstrained motion of the end-effector can be computed. This is true for all poses of the manipulator, even with all actuators locked.

This paper presents how this unconstrained motion can be determined for a planar 3-RPR manipulator. The singularities are then studied. It is shown that when clearances are considered, the singularity curves normally found in the workspace of such a manipulator become singular zones. These zones can be significant and greatly reduce the usable workspace of a manipulator. Since a prescribed configuration that would not, in theory, corresponds to a singular pose can become singular due to the unconstrained motion, the results of this paper are relevant to manipulator design and trajectory planning.