Modular robots have the unique ability to reconfigure their shape and capabilities to adapt to various challenges in the environment. In order to perform tasks autonomously in unknown environments, active perception and planning algorithms are required that can leverage their adaptive capabilities. This work presents several such perception and planning tools. An novel, probabilistic object reconstruction algorithm is presented that allows a generic mobile robot (such as a modular robot) intelligently position a 3D sensor to explore unknown objects in its environment. Then, it presents fully autonomous, perception-informed systems for modular self-reconfigurable robots (MSRRs) that enable them to explore, dynamically adapt to their environment, and even augment their environment to perform high-level tasks. Finally, it presents an end-to-end path planning framework for MSRR systems that enables them to reconfigure between multiple morphologies and use multiple gaits in order to traverse and plan optimal paths over challenging terrain.