Reconfiguration Motion Planning for Variable Topology Truss This paper presents an algorithm to do motion planning for a new class of selfreconfigurable modular robot: the variable topology truss (VTT). Modular robots consist of many modules that can be configured into various structures, and motion planning problem for modular robots with many degrees of freedom and many motion constraints is a significant challenge. In this paper, we propose a novel motion planning algorithm for modular robots to handle this problem with huge state space inspired by DNA replication process xe2x80x93 the topology of DNA can be changed by cutting and resealing strands as tanglements form. In a variable topology truss, a single node with enough edge modules can split into a pair of nodes and two separate nodes can be merged to become an individual one. This selfreconfiguration ability results in more potential applications for this type of robots in unstructured environment, such as space and underseas but also leads to more challenges for reconfiguration planning. A novel way to model the robot in a nonuniform grid space is presented and a simple local planner is also developed to check the validation of possible actions. This approach significantly simplifies the problem and some experiment results show that the complicated problem can be solved in a reasonable time.

Evolutionary AlgorithmBased Complete Coverage Path Planning for Tetriamond Tiling Robots Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a selfreconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shapeshifting of the platform. Thus, during motion planning, the optimal navigation sequence of a selfreconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamondbased robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shapeshifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamondbased reconfigurable robots.